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Mohamed Shams
Day 2 Tue, October 19, 2021; https://doi.org/10.2118/208662-ms

Abstract:
This paper provides the field application of the bee colony optimization algorithm in assisting the history match of a real reservoir simulation model. Bee colony optimization algorithm is an optimization technique inspired by the natural optimization behavior shown by honeybees during searching for food. The way that honeybees search for food sources in the vicinity of their nest inspired computer science researchers to utilize and apply same principles to create optimization models and techniques. In this work the bee colony optimization mechanism is used as the optimization algorithm in the assisted the history matching workflow applied to a reservoir simulation model of WD-X field producing since 2004. The resultant history matched model is compared with with those obtained using one the most widely applied commercial AHM software tool. The results of this work indicate that using the bee colony algorithm as the optimization technique in the assisted history matching workflow provides noticeable enhancement in terms of match quality and time required to achieve a reasonable match.
Precious Ogbeiwi, Karl Stephen
Day 4 Thu, October 21, 2021; https://doi.org/10.2118/205117-ms

Abstract:
The compositional simulations are required to model CO2 flooding are computationally expensive particularly for fine-gridded models that have high resolutions, and many components. Upscaling procedures can be used in the subsurface flow models to reduce the high computation requirements of the fine grid simulations and accurately model miscible CO2 flooding. However, the effects of physical instabilities are often not well represented and captured by the upscaling procedures. This paper presents an approach for upscaling of miscible displacements is presented which adequately represents physical instabilities such as viscous and heterogeneity induced fingering on coarser grids using pseudoisation techniques. The approach was applied to compositional numerical simulations of two-dimensional reservoir models with a focus on CO2 injection. Our approach is based on the pseudoisation of relative permeability and the application of transport coefficients to upscale viscous fingering and heterogeneity-induced channelling in a multi-contact miscible CO2 injection. Pseudo-relative permeability curves were computed using a pseudoisation technique and applied in combination with transport coefficients to upscale the behaviour of fine-scale miscible CO2 flood simulations to coarser scales. The accuracy of the results of the pseudoisation procedures were assessed by applying statistical analysis to compare them to the results of the fine grid simulations. It is observed from the results that the coarse models provide accurate predictions of the miscible displacement process and that the fingering regimes are adequately captured in the coarse models. The study presents a framework that can be employed to represent the dynamics of physical instabilities associated with miscible CO2 displacements in upscaled coarser grid reservoir models.
Russell Julier, Craig Smalley, Karen Van der Molen, Rene Roeterink
Day 3 Wed, October 20, 2021; https://doi.org/10.2118/205201-ms

Abstract:
The long-term prosperity of oil and gas companies requires a constant influx of new volumes of producible oil and gas that can be developed to replace existing production. Without such activity it is inevitable that production will eventually fall as the resource base is gradually consumed The attractiveness of achieving more barrels from existing discovered fields has always been strong as it has long been recognized that such opportunities can be economically attractive and rapidly brought to fruition. It is also recognized however that such opportunities may be more complex relying both upon excellent subsurface understanding and successful brownfield project execution. It is therefore not surprising that in many cases actual recovery factors (produced volume/initial in-place volume) in oil and gas fields can be significantly less than what should be technically achievable. Identification of economically robust brownfield opportunities remains an industry challenge. In this paper we address this challenge by reporting a new workflow for brownfield opportunity identification leading to recovery factor improvement. Shell's Recovery Factor Improvement (RFI) Workflow was developed to address these issues and builds upon the existing best practice workflows to better explore and define the activities that would be required to achieve top quartile recovery factor performance. The workflow combines elements of various existing published approaches: (1) Shell's TQ-EUR Tool is an internal database that allows current and forecast recovery factor to be compared with that of analogue reservoirs using a reservoir complexity factor and key reservoir performance parameters as comparison criteria across the Shell portfolio. (2) An efficiency factor-based analysis of recovery factor; (3) a structured workshop to elicit new recovery factor improvement activities by addressing each individual efficiency factor in turn; (4) Consistent reporting of results. The combination of these approaches creates a powerful workflow to improve brownfield field opportunity identification and maturation. The RFI Workflow is intended to provide asset teams with a practicable and repeatable process that can be completed without specialist technical support or software to enable the identification of robust new opportunities. Experience with using the new workflow has demonstrated that it is able to bring new understanding to asset teams, consistently identify new opportunities and highlight common portfolio-wide opportunity types that would benefit from further central technology development funding.
Jimmy Thatcher, Abdul Rehman, Ivan Gee, Morgan Eldred
Day 2 Tue, October 19, 2021; https://doi.org/10.2118/208658-ms

Abstract:
Oil & Gas extraction companies are using a vast amount of capital and expertise on production optimization. The scale and diversity of information required for analysis is massive and often leading to a prioritization between time and precision for the teams involved in the process. This paper provides a success story of how artificial intelligence (AI) is used to dynamically and effeciently optimize and predict production of gas wells. In particular, we focus on the application of unsupervised machine learning to identify under different potential constraints the optimal production parameter settings that can lead to maximum production. A machine learning model is supported by a decision support system that can enhance future drilling operations and also help answer important questions such as why a particular well or group of wells is producing differently than others of the same type or what kind of parameters that work on different wells in different conditions. The model can be advanced to optimize within field constraints such as facility handling capacity, quotas, budget or emmisions. The methods used were a combination of similarity measures and unsupervised machine learning techniques which were effective in identifying wells and clusters of wells that have similar production and behavioral profiles. The clusters of wells were then used to identify the process path (specific drilling and completion, choke size, chemicals, etc processes) most likely to result in optimal production and to identify the most impactful variables on production rate or cumulative production via an additional clustering of the principle charactersitics of the well. The data sets used to build these models include but are not limited to gas production data (daily volume), drilling data (well logs, fluid summary etc.), completion data (frac, cement bond logs), and pre-production testing data (choke, pressure etc.) Initial results indicate that this approach is a feasible approach, on target in terms of accuracy with traditional methods and represents a novel, data driven, method of identifying optimal parameter settings for desired production levels; with the ability to perform forecasts and optimization scenarios in run-time. The approach of using machine learning for production forecasting and production optimization in run-time has immense values in terms of the ability to augment domain expertise and create detailed studies in a fraction of the time that is typically required using traditional approaches. Building on same approach to optimise the field to deliver most reliable or most effeciently against a parameter will be an invaluable feature for overall asset optimisation.
Oleksandr Burachok, Oleksandr Kondrat, Serhii Matkivskyi, Dmytro Pershyn
Day 4 Thu, October 21, 2021; https://doi.org/10.2118/205149-ms

Abstract:
Low value of final condensate recoveries achieved under natural depletion require implementation of enhanced gas recovery (EGR) methods to be implemented for the efficient development of gas-condensate reservoirs. The study was performed using synthetic numerical 9-component compositional simulation model that approximated the typical conditions of deep gas-condensate reservoirs of Dnieper-Donetsk Basin in Easter Ukraine. Injection of water, methane, nitrogen, carbon dioxide, mixture of methane and nitrogen, mixture of methane, ethane and propane at different concentrations were evaluated at 50% and 100% voidage replacement for reservoir fluids with 100 g/m3, 300 g/m3 and 500g/m3 potential condensate yield. Condensate recovery studied at different stages after primary depletion, when reservoir pressure reached 25, 50, 75% from dew point and at pressure of maximum liquid dropout. Results comparison was done based on the two criteria: technical efficiency – incremental condensate recovery towards the base depletion cases and economic efficiency – cumulative NPV. Status of initial depletion as well as voidage replacement have a direct impact on breakthrough time and negative economic indicators. Despite providing the highest incremental condensate recovery by injecting CO2 at 100% voidage, it has a strong negative economic effect. Based on incremental condensate recovery EGR methods are ranked as following for all condensate potential yields and levels of primary depletion: CO2 100%; solvent gas mixture of C1 90%, C2 5%, C3 5%; solvent gas mixture C1 98%, C2 1%, C3 1%; C1 100%; mixture of C1 50% and N2 50%; N2 100%; water. Economically, the highest efficiency was shown for C1 100% injection, due to the fact, that produced re-cycled gas has a sales value as well. For the maximum incremental recovery it is advisable to start the injection as early as possible, while highest economic increments received for the cases of delayed injection, particularly when the reservoir pressure is equal to the pressure of maximum liquid condensation. The results of study can be used a guidance for rapid screening of applicable EGR method for gas-condensate fields depending on depletion stage and potential condensate yield.
Weibing Tian Tian, Keliu Wu, Zhangxin Chen, Yanling Gao, Yin Gao, Jing Li
Day 4 Thu, October 21, 2021; https://doi.org/10.2118/205171-ms

Abstract:
Imbibition is one of the most common physical phenomena in nature, and it plays an important role in enhanced oil recovery, hydrology, and environmental engineering. For the tight reservoirs, the imbibition method has an obvious advantage in fracturing, shut-in, and huff-puff development. Although the current imbibition studies focus on oil recovery, and the inertial effect in imbibition is neglected and its mechanism is also unclear. In this paper, the inertial effect on spontaneous oil-water imbibition at micron-scale is studied by molecular kinetic theory (MKT). The frictional coefficient in the model is a fitted parameter to match the experimental data during the total imbibition process. Then, the simulation of the initial imbibition stage is conducted and the inertial effect on imbibition is identified by the difference between the model considering the inertial effect (CI) and the model neglecting the inertial effect (NI), or by the proportion of inertial force to the total resistance. Results show that (i) with an increase in the water phase viscosity, the inertial effect time shortens, maximum imbibition height and rate decrease, and thus the inertial effect on imbibition weakens; (ii) with an increase in the oil phase viscosity, the inertial effect time changes little, the maximum imbibition height and rate decrease slightly, namely, the inertial effect depends slightly on the oil phase. (iii) with an increase in the capillary wettability (hydrophilicity), the inertial effect time shortens, the maximum imbibition rate first increases and then decreases, and the inertial effect on imbibition weakens. This work sheds light on the inertial effect on oil-water imbibition by MKT, considering the effects of dynamic contact angle, water phase viscosity, oil phase viscosity, and wettabilities, which is helpful to understand the role of inertia in the oil-water or oil-fracturing fluid imbibition process.
Noor Arnida Abdul Talip, Mohd Hafiz Muhamad Pikri, Shahrul Azman Zainal Abidin, Hasnor Hassaruddin Hashim
Day 3 Wed, October 20, 2021; https://doi.org/10.2118/205166-ms

Abstract:
Methane emission affects advocacy on natural gas as low carbon fuel as it has a global warming potential of 25 times GWP compared to CO2. In promoting natural gas against coal and address concerns of stakeholders, it is critical for an Oil & Gas Company to manage the methane emissions across gas value chain for it be qualified as a cleaner fuel in the energy transition. Methane emission is usually quantified from key intended emission sources such as venting, flaring and combustion. With this greenhouse gas (GHG) emissions monitoring enables gradual reduction of large intended methane sources. However, unintended fugitive methane emission as well as those from other small intended sources such as compressor seals are usually not quantified and reported. In supporting energy transition, there is a need to step-up in accurate quantification and reduction of methane emissions and determine long term reduction target in driving competitiveness of natural gas as low carbon fuel. Hence, an initiative was taken to measure baseline data for methane emission for gas processing facilities and gas transmission and regasification unit by utilizing accurate measurement tools and methodologies for detection and quantification.
Fatai Adesina Anifowose
Day 1 Mon, October 18, 2021; https://doi.org/10.2118/208875-ms

Abstract:
The petroleum industry has continued to show more interest in the application of artificial intelligence (AI). Most professional gatherings now have sub-themes to highlight AI applications. Similarly, the number of publications featuring AI applications has increased. The industry is facing the challenge of scaling up the applications to practical and impactful levels. Most of the applications end up in technical publications and narrow proofs of concept. For the industry's digital transformation objective to be fully achieved, efforts are required to overcome the current limitations. This paper discusses possible causes of the prevailing challenges and prescribes a number of recommendations to overcome them. The recommendations include ways to handle data shortage and unavailability issues, and how AI projects can be designed to provide more impactful solutions, regenerate missing or incomplete logs, and provide alternative workflows to estimate certain reservoir properties. The results of three successful applications are presented to demonstrate the efficacy of the recommendations. The first application estimates a log of reservoir rock cementation factors from wireline data to overcome the limitation of the conventional approach of using a constant value. The second application used the machine learning methodology to regenerate missing logs possibly due to tool failure or bad hole conditions. The third application provides an alternative approach to estimate reservoir rock grain size to overcome the challenges of the conventional core description. Tips on how these applications can be integrated to create a bigger impact on exploration and production (E&P) workflows are shared. It is hoped that this paper will enrich the current AI implementation strategy and practice. It will also encourage increased synergy and collaborative integration of domain expertise and AI methods to make better impact and achieve the digital transformation of E&P business goals.
Nasser Faisal Al-Khalifa, Mohammed Farouk Hassan, Deepak Joshi, Asheshwar Tiwary, Ihsan Taufik Pasaribu, Mahmoud Siam, Soaziq Leveque, Salih Noreldin Osman
Day 4 Thu, October 21, 2021; https://doi.org/10.2118/205218-ms

Abstract:
The Umm Gudair (UG) Field is a carbonate reservoir of West Kuwait with more than 57 years of production history. The average water cut of the field reached closed to 60 percent due to a long history of production and regulating drawdown in a different part of the field, consequentially undulating the current oil/water contact (COWC). As a result, there is high uncertainty of the current oil/water contact (COWC) that impacts the drilling strategy in the field. The typical approach used to develop the field in the lower part of carbonate is to drill deviated wells to original oil/water contact (OOWC) to know the saturation profile and later cement back up to above the high-water saturation zone and then perforate with standoff. This method has not shown encouraging results, and a high water cut presence remains. An innovative solution is required with a technology that can give a proactive approach while drilling to indicate approaching current oil/water contact and geo-stop drilling to give optimal standoff between the bit and the detected water contact (COWC). Recent development of electromagnetic (EM) look-ahead resistivity technology was considered and first implemented in the Umm Gudair (UG) Field. It is an electromagnetic-based signal that can detect the resistivity features ahead of the bit while drilling and enables proactive decisions to reduce drilling and geological or reservoir risks related to the well placement challenges.
Fatai Adesina Anifowose, Mokhles Mustafa Mezghani, Saeed Saad Shahrani
Day 2 Tue, October 19, 2021; https://doi.org/10.2118/205156-ms

Abstract:
Reservoir rock textural properties such as grain size are typically estimated by direct visual observation of the physical texture of core samples. Grain size is one of the important inputs to petrophysical characterization, sedimentological facies classification, identification of depositional environments, and saturation models. A continuous log of grain size distribution over targeted reservoir sections is usually required for these applications. Core descriptions are typically not available over an entire targeted reservoir section. Physical core data may also be damaged during retrieval or due to plugging. Alternative methods proposed in literature are not sustainable due to their limitations in terms of input data requirements and inflexibility to apply them in environments with different geological settings. This paper presents the preliminary results of our investigation of a new methodology based on machine learning technology to complement and enhance the traditional core description and the alternative methods. We developed and optimized supervised machine learning models comprising K-nearest neighbor (KNN), support vector machines (SVM), and decision tree (DT) to indirectly estimate reservoir rock grain size for a new well or targeted reservoir sections from historical wireline logs and archival core descriptions. We used anonymized datasets consisting of nine wells from a clastic reservoir. Seven of the wells were used to train and optimize the models while the remaining two were reserved for validation. The grain size types range from clay to pebbles. The performance of the models confirmed the feasibility of this approach. The KNN, SVM, and DT models demonstrated the capability to estimate the grain size for the test wells by matching actual data with a minimum of 60% and close to 80% accuracy. This is an accomplishment taking into account the uncertainties inherent in the core analysis data. Further analysis of the results showed that the KNN model is the most accurate in performance compared to the other models. For future studies, we will explore more advanced classification algorithms and implement new class labeling strategies to improve the accuracy of this methodology. The attainment of this objective will further help to handle the complexity in the grain size estimation challenge and reduce the current turnaround time for core description.
Ermeng Zhao, Jian Hou, Yunkai Ji, Lu Liu, Yongge Liu, Yajie Bai
Day 4 Thu, October 21, 2021; https://doi.org/10.2118/205119-ms

Abstract:
Natural gas hydrate is widely distributed in the permafrost and marine deposits, and is regarded as an energy resource with great potential. The low-frequency electric heating assisted depressurization (LF-EHAD) has been proven to be an efficient method for exploiting hydrate sediments, which involves complex multi-physics processes, i.e. current conduction, multiphase flow, chemical reaction and heat transfer. The physical properties vary greatly in different hydrate sediments, which may profoundly affect the hydrate decomposition in the LF-EHAD process. In order to evaluate the influence of hydrate-bearing sediment properties on the gas production behavior and energy utilization efficiency of the LF-EHAD method, a geological model was first established based on the data of hydrate sediments in the Shenhu Area. Then, the influence of permeability, porosity, thermal conductivity, specific heat capacity, hydrate saturation and hydrate-bearing layer (HBL) thickness on gas production behavior is comprehensively analyzed by numerical simulation method. Finally, the energy efficiency ratio under different sediment properties is compared. Results indicate that higher gas production is obtained in the high-permeability hydrate sediments during depressurization. However, after the electric heating is implemented, the gas production first increases and then tends to be insensitive as the permeability decreases. With the increasing of porosity, the gas production during depressurization decreases due to the low effective permeability; while in the electric heating stage, this effect is reversed. High thermal conductivity is beneficial to enhance the heat conduction, thus promoting the hydrate decomposition. During depressurization, the gas production is enhanced with the increase of specific heat capacity. However, more heat is consumed to increase the reservoir temperature during electric heating, thereby reducing the gas production. High hydrate saturation is not conducive to depressurization because of the low effective permeability. After electric heating, the gas production increases significantly. High HBL thickness results in a higher gas production during depressurization, while in the electric heating stage, the gas production first increases and then remains unchanged with the increase of thickness, due to the limited heat supply. The comparison results of energy efficiency suggest that electric heating is more advantageous for hydrate sediments with low permeability, high porosity, high thermal conductivity, low specific heat capacity, high hydrate saturation and high HBL thickness. The findings in this work can provide a useful reference for evaluating the application of the LF-EHAD method in gas hydrate sediments.
Ahmed M. S. Elgendy, Simone Ricci, Elena I. Cojocariu, Claudio Geloni
Day 3 Wed, October 20, 2021; https://doi.org/10.2118/205128-ms

Abstract:
Dynamic-geochemical model is a powerful instrument to evaluate the geochemical effects on CO2storage capacity, injectivity and long-term containment. The study objective is to apply an integrated multi-step workflow to a carbon capture and storage (CCS) candidate field (offshore), namely hereinafter H field. From experimental analyses, a comprehensive real data-tailored reactive transport model (RTM) has been built to capture the dynamics and the geochemical phenomena (e.g., water vaporization, CO2solubility, mineral alteration) occurring during and after the CO2injection in sedimentary formations. The proposed integrated workflow couples lab activities and numerical simulations and it is developed according to the following steps: Mineralogical-chemical characterization (XRD, XRF and SEM-EDX experimental techniques) of field core samples; Data elaboration and integration to define the conceptual geochemical model; Synthetic brine reconstruction by means of 0D geochemical models; Numerical geochemical modelling at different complexity levels. Field rocks chosen for CO2injection have been experimentally characterized, showing a high content of Fe in clayey, micaceous and carbonate mineralogical phases. New-defined, site-specific minerals have been characterized, starting from real XRD, XRF and SEM-EDX data and by calculation of their thermochemical parameters with a proprietary procedure. They are used to reconstruct synthetic formation water chemical composition (at equilibrium with both rock mineralogy and gas phase), subsequently used in RTM. CO2injection is simulated using 2D radial reactive transport model(s) built in a commercial compositional reservoir simulator. The simulations follow a step-increase in the complexity of the model by adding CO2solubility, water vaporization and geochemical reactions. Geochemical processes impact on CO2storage capacity and injectivity is quantitatively analyzed. The results show that neglecting the CO2solubility in formation water may underestimate the max CO2storage capacity in H field by around 1%, maintaining the same pressure build-up profile. Sensitivities on the impact of formation water salinity on the CO2solubility are presented. In a one thousand years’ time-scale, changes in reservoir porosity due to mineral alteration, triggered by CO2-brine-rock interactions, seem to be minimal in the near wellbore and far field. However, it has been seen that water vaporization with the associated halite precipitation inclusion in the simulation models is recommended, especially at high-level of formation brine salinity, for a reliable evaluation of CO2injectivity related risks. The proposed workflow provides a new perspective in geochemical application for CCS studies, which relies on novel labs techniques (analyses automation), data digitalization, unification and integration with a direct connection to the numerical models. The presented procedure can be followed to assess the geochemical short-and long-term risks in carbon storage projects.
Tianhua Zhang, Shiduo Yang, Chandramani Shrivastava, Adrian A, Nadege Bize-Forest
Day 4 Thu, October 21, 2021; https://doi.org/10.2118/205222-ms

Abstract:
With the advancement of LWD (Logging While Drilling) hardware and acquisition, the imaging technology becomes not only an indispensable part of the drilling tool string, but also the image resolution increases to map layers and heterogeneity features down to less than 5mm scale. This shortens the geological interpretation turn-around time from wireline logging time (hours to days after drilling) to semi-real time (drilling time or hours after drilling). At the same time, drilling motion is complex. The depth tracking is on the surface referenced to the surface block movement. The imaging sensor located downhole can be thousands of feet away from the surface. Mechanical torque and drag, wellbore friction, wellbore temperature and weight on bit can make the downhole sensor movement motion not synchronized with surface pipe depth. This will cause time- depth conversion step generate image artifacts that either stop real-time interpretation of geological features or mis-interpret features on high resolution images. In this paper, we present several LWD images featuring distortion mechanism during the drilling process using synthetic data. We investigated how heave, depth reset and downhole sensor stick/slip caused image distortions. We provide solutions based on downhole sensor pseudo velocity computation to minimize the image distortion. The best practice in using Savitsky-Golay filter are presented in the discussion sections. Finally, some high-resolution LWD images distorted with drilling-related artifacts and processed ones are shown to demonstrate the importance of image post-processing. With the proper processed images, we can minimize interpretation risks and make drilling decisions with more confidence.
Bei Wei, Jian Hou, Ermeng Zhao
Day 4 Thu, October 21, 2021; https://doi.org/10.2118/205225-ms

Abstract:
The flow dynamics of non-Newtonian fluid in porous media is much different from the Newtonian fluid. In this work, we establish a lattice Boltzmann model for polymer flooding taking into both the power law fluid properties and viscoelastic fluid properties. Using this model, we investigate the viscosity distribution in porous media, the local apparent permeability in porous media, and the effect of elastic force on the remaining oil in dead ends. Firstly, we build a single phase lattice Boltzmann model to evolve the fluid velocity field. Then the viscosity and shear rate in each lattice can be calculated based on the relaxation time and velocity field. We further make the fluid viscosity change with the shear rate according to the power-law fluid constitutive equation, consequently establish the lattice Boltzmann model for power law fluid. Moreover, we derive the Maxwell viscoelastic fluid model in integral form using Boltzmann superposition principle, and the elastic force is calculated from the divergence of the stress tensor. We then couple the elastic force into the lattice Boltzmann model by Newton's second law, and finally establish the lattice Boltzmann model of the viscoelastic fluid. Both the models are validated against analytical solutions. The simulation results show that when the power-law index is smaller than 1, the fluid viscosity shows a distribution of that viscosity is higher in pore center and lower near the wall; while when the index is larger than 1, the fluid viscosity shows a opposite distribution. This is because the pore center has a high velocity but a low shear rate, while the boundary has a low velocity but a high shear rate. Moreover, the local apparent permeability decreases with the power law index, and the number of hyper-permeable bands also decreases. In addition, the local permeability shows pressure gradient dependence. Considering the viscoelasticity effects, the displacement fluid has a clear tendency to sweep deeply into the dead end, which improves the oil washing efficiency of the dead end. The model provides a pore scale simulation tool for polymer flooding and help understand the flow mechanisms and enhanced oil recovery mechanisms during polymer flooding.
Lilibeth Chiquinquira Perdomo, Carlos Alvarez, Maria Edith Gracia, Guillermo Danilo Salomone, Gilberto Ventuirini, Gustavo Adolfo Selva
Day 2 Tue, October 19, 2021; https://doi.org/10.2118/205215-ms

Abstract:
As other companies registered in the US stock market, the company reports oil and gas reserves, in compliance with the definitions of the Securities and Exchange Commission (SEC). In addition, it complies internally with the guidelines established by the Petroleum Resources Management System to certify its resources. The PRMS focuses on supporting consistent evaluation of oil resources based on technically sound industry practices, providing fundamental principles for the assessment and classification of oil reserves and resources, but does not provide specific guidance for the classification and categorization of quantities associated with IOR projects. Recently, the company has implemented EOR pilot projects, and their results seem to show commerciality for future development or expansion to new areas, displaying multiple opportunities and proposals to incorporate reserves and resources. So far, the pilot projects and their expansions have been addressed only from the point of view of incremental projects, as an improvement over the previous secondary recovery. The company does not have sufficient track record in booking reserves or resources from EOR projects, their quantities have been incorporated following bibliographic references and results of EOR projects with proven commerciality around the world. For this reason, the need arose to have a tool that provides the company with methodological criteria to evaluate the resources and reserves inherent in this type of project, that incorporate the "best practices" of the industry and that respect the guidelines and definitions of PRMS for incremental projects. That was how, the need to meet this challenging goal led company to develop its "EOR Resources and Reserves Assessment Guide" with the advice of a renowned consulting company. Although the Guide is not intended to be a review of the large body of existing IOR literature, it contains several useful references that serve as a starting point for understanding the IOR project for assessment process of resources and reserves. This document shows the process of development and implementation of the EOR guide, complementing the existing guides within the corporation and providing the company with a positive result within the internal processes of Audit, reserves and resources for this type of projects.
Mohamed Ibrahim Mohamed, Ahmed Mahmoud El-Menoufi, Eman Abed Ezz El-Regal, Abd Allah Moustafa Hegazy, Khaled Mohamed Mansour, Mohamed Nagy Negm, Hatem Mohamed Hussein
Day 3 Wed, October 20, 2021; https://doi.org/10.2118/205217-ms

Abstract:
Oil and gas operators must measure or calculate the shut-in wellhead pressure for well integrity applications. Some operators adopt a method that gives satisfactory results for dry gas and lean gas condensate. They are using the steady-state simulator to calculate the wellhead pressure at a very low gas rate. The friction losses become negligible, and the only losses are due to hydrostatic head simulating (to some extent) the shut-in condition. This method again can work well with oil producers with low GOR/Bubble point pressure as the production string will be nearly a single phase. The problem is that this method is inaccurate for high GOR/CGR wells because of phase redistribution and the error can be significantly high. Phase redistribution occurs After shut-in, liquid droplets will accumulate at the bottom of the well. The interface liquid/gas will move up with sometimes liquid cushion is being re-injected back in the reservoir due to gravity or gas expansion in the tubing while the gas/liquid interface will move down a little. Many factors affect the behavior, including the well deviation, fluid properties, and the productivity and the injectivity of the formation. Thus, simulating this behavior requires a dynamic multiphase simulation. As some of the fluids might return to the formation, as a result of compressibility, coupling with a numerical reservoir simulation to model the near wellbore is also necessary. In this paper, we applied a dynamic multiphase model to predict the shut-in wellhead pressure. We used an uncertainty analysis approach to investigate the effect of many parameters on the accuracy of the results. We presented all recommended calculation procedures with a guide to minimizing the uncertainty associated. We presented our approach to three actual wells with different configurations and fluid properties with a deviation of +-10% of the real measurements.
Andrew Creegan, Michael Roberts
Day 1 Mon, October 18, 2021; https://doi.org/10.2118/208638-ms

Abstract:
The usage of Artificial Intelligence (AI) in the arena of drilling optimization is a rapidly evolving endeavor and is becoming increasingly prevalent. In many applications the goal is process automation and optimization with the intent to reduce cost, improve yield/outcome and address risk. Real-world experience, however, has taught us that the correct application, configuration, and realtime management of an AI system is equally as important as the underlying algorithms. This paper poses that the implementation of an automated AI drilling system must consider the human element of acceptance in order to succeed. Proper onboarding and user acceptance is requisite to proper system configuration and performance. This paper sets forth guidelines that can be considered standard for initiating an AI drilling program.
Nasser M. Al-Hajri, Akram R. Barghouti, Sulaiman T. Ureiga
Day 4 Thu, October 21, 2021; https://doi.org/10.2118/205198-ms

Abstract:
This paper will present an alternative calculation technique to predict wellbore crossflow rate in a water injection well resulting from a casing leak. The method provides a self-governing process for wellbore related calculations inspired by the fourth industrial revolution technologies. In an earlier work, calculations techniques were presented which do not require the conventional use of downhole flowmeter (spinner) to obtain the flow rate. Rather, continuous surface injection data prior to crossflow development and shut-in well are used to estimate the rate. In this alternative methodology, surface injection data post crossflow development are factored in to calculate the rate with the same accuracy. To illustrate the process an example water injector well is used. To quantify the casing leak crossflow rate, the following calculation methodology was applied:Generate a well performance model using pre-crossflow injection data. Normal modeling techniques are applied in this step to obtain an accurate model for the injection well as a baseline case.Generate an imaginary injection well model: An injection well mimicking the flow characteristics and properties of the water injector is envisioned to simulate crossflow at flowing (injecting) conditions. In this step, we simulate an injector that has total depth up to the crossflow location only and not the total depth of the example water well.Generate the performance model for the secondary formation using post crossflow data: The total injection rate measured at surface has two portions: one portion goes into the shallower secondary formation and another goes into the deeper (primary) formation. The modeling inputs from the first two steps will be used here to obtain the rate for the downhole formation at crossflow conditions.Generate an imaginary production well model: The normal model for the water injector will be inversed to obtain a production model instead. The inputs from previous steps will be incorporated in the inverse modeling.Obtaining the crossflow rate at shut-in conditions: Performance curves generated from step 3 & 4 will be plotted together to obtain an intersection that corresponds to the crossflow rate at shut-in conditions. This numerical methodology was analytically derived and the prediction results were verified on syntactic field data with very high accuracy. The application of this model will benefit oil operators by avoiding wireline logging costs and associated safety risks with mechanical intervention.
Ahmed Wasel Alsmaeil, Mohamed Amen Hammami, Amr Ismail Abdel-Fattah, Mazin Yousef Kanj, Emmanuel P Giannelis
Day 4 Thu, October 21, 2021; https://doi.org/10.2118/205175-ms

Abstract:
Developing nanocarriers deliver molecules to targeted locations has received widespread attention in different fields ranging from biomedical to oil and gas industries. Mesoporous Silica Nanoparticles (MSNs), where the pore size diameter ranges from 2-50 nm, have become attractive in many fields including biomedicine. One advantage is the ability to control the size, morphology of the particles, and the internal and external surfaces properties which enable encapsulating molecules of different size and charges. Moreover, it is possible to functionalize the pores and the surface of the MSNs, which make them suitable to host different molecules and release them in situ in a controlled manner. Despite the numerous studies of MSNs, little has been devoted to subsurface applications. This review will highlight some of the interesting characteristics of MSNs that make them promising carriers of molecules for slow and/or stimuli-responsive delivery for oil field applications. For example, they could be utilized for the controlled release of surfactants for enhanced oil recovery applications to minimize surfactant losses near the well-bore area. The mesoporous materials can be designed to harvest the ions normally present in oil field water, and the high temperatures encountered when travelling deep in the reservoir to release the surfactant. The ion exchange process makes it possible to engineer the MSNs to release their cargo for efficient and stimuli responsive delivery applications. The ion-responsive release was analyzed by the interfacial tension behavior between crude oil and high salinity water (HSW). It is concluded that the interfacial tension could be reduced up to 0.0045 mN/m when the mesoporous silica particles are suspended in HSW in comparison to 0.9 mN/m when suspended in DI water.
Dennis Alexis, Gayani Pinnawala, Do Hoon Kim, Varadarajan Dwarakanath, Ruth Hahn, Marlon Solano, Emily Tao, Greg Winslow, Sophany Thach, Adam Jackson, et al.
Day 4 Thu, October 21, 2021; https://doi.org/10.2118/205179-ms

Abstract:
The work described in this paper details the development of a single stimulation package that was successfully used for treating an offshore horizontal polymer injection well to improve near wellbore injectivity in the Captain field, offshore UK. The practice was to pump these concentrated surfactant streams using multiple pumps from a stimulation vessel which is diluted with the polymer injection stream in the platform to be injected downhole. The operational challenges were maintaining steady injection rates of the different liquid streams which was exacerbated by the viscous nature of the concentrated surfactants that would require pre-dilution using cosolvent or heating the concentrated solutions before pumping to make them flowable. We have developed a single, concentrated liquid blend of surfactant, polymer and cosolvent that was used in near-wellbore remediation. This approach significantly simplifies the chemical remediation process in the field while also ensuring consistent product quality and efficiency. The developed single package is multiphase, multicomponent in nature that can be readily pumped. This blend was formulated based on the previous stimulation experience where concentrated surfactant packages were confirmed to work. Commercial blending of the single package was carried out based on lab scale to yard scale blending and dilution studies. About 420 MT of the blend was manufactured, stored, and transported by rail, road and offshore stimulation vessel to the field location and successfully injected.
Patrick Bangert
Day 1 Mon, October 18, 2021; https://doi.org/10.2118/208634-ms

Abstract:
A practical data science, machine learning, or artificial intelligence project benefits from various organizational and managerial prerequisites. The effective collaboration between various data scientists and domain experts is perhaps the most important, which is discussed here. Based on practical experience, the principal theses put forward here are that (1) data science projects require domain expertise, (2) domain expertise and data science expertise generally cannot be provided by the same individual, (3) effective communication between the various experts is essential for which everyone requires some limited understanding of the others’ expertise and real-world experience, and (4) management must acknowledge these aspects by reserving sufficient project time and budget for communication and change management.
Paul Jacob Van Den Hoek, Jorik Willem Poessé
Day 3 Wed, October 20, 2021; https://doi.org/10.2118/205122-ms

Abstract:
Both for the oil & gas and geothermal industry, induced seismicity caused by field development and operation can pose a risk, in particular when the reservoir (or overburden / underburden) is intersected by faults. The mechanisms by which faults can be reactivated (potentially leading to seismicity) include pressure effects (reservoir depletion, or pressure rise over large areas as a result of injection) or thermal effects (cooling such as in geothermal operations or heating such as in steam flooding). Earlier, we proposed a simple methodology to assess seismic risk for geothermal reservoirs that can also be applied to hydrocarbon reservoirs. This methodology uses an elastoplastic finite element model of the reservoir in question. However, its application turned out to be laborious. Therefore, we developed an exact analytical solution for the stress changes induced by cooling, depletion and /or pressurization along (a) representative fault(s). This solution is a generalisation of the Goodier analytical solution for the situation of non-vertical faults. The analytical solution can be used to quickly evaluate a number of different scenarios related to temperature and /or pressure distributions in the reservoir. In the case of fault activation, maximum fault displacements (slip) can be computed by linking the results to elastic finite element calculations for similar load conditions. Using published standard correlations, the seismic magnitude can subsequently be estimated from the computed fault displacements. The analytical model was applied to different fault geometries, reservoir temperature distributions and depletions. It turns out that certain fault geometries (dip angles, offsets) are far more prone to activation than other fault geometries. An explanation of this result is provided. Furthermore, for non-critically stressed faults, the risk of activation is far less for geothermal operations than for situations where large parts of the reservoir are depleted or pressurized. This can be explained by the fact that the extent of the cooled zone in geothermal operations is generally limited, even after 30 years of operation. Consequently, cooling-induced stress changes along the fault are significantly reduced because of arching by the adjacent non-cooled areas. Finally, one geothermal field example in The Netherlands is presented where the above methodology was applied to demonstrate that there exists no seismic risk over the entire field life.
Singh Anurag Yadav, Imran Muhammad Chohan
Day 4 Thu, October 21, 2021; https://doi.org/10.2118/205152-ms

Abstract:
In oil and gas drilling, consistency of performance delivery heavily depends upon rig capability and its ability to maintain performance assurance through its execution cycle. It's not an uncommon occurrence that a rig is found with an underperforming top drive, one such scenario was observed in an in-fill oil well drilling project. This project was essentially drilling horizontal wells with bottom hole assemblies which had primary drive mechanism as a top drive. The rig in question was struggling to provide not only the requisite RPM but also not been able to deliver consistent torque needed to drill the well. This study analyzes how severe rig limitations were overcome through an optimization plan in which most optimal BHA was designed and drilling practices were customized for safe and successful execution of wells. In order to understand root cause of the challenge, an offset well analysis was conducted, it identified that high torque was mostly generated while drilling through inter-bedded formations which typically caused top drive to stall. In addition, multiple tool failures were encountered due to the high stick slip which rig couldn't mitigate due to the low RPM yield of the top drive. To manage the rpm and torque limitations, a motorized RSS BHA was designed as a solution. Further, based on micro-stall events of motor only BHA's across the inter-bedded formations in the field, a stick slip management tool was placed below the motor so that a potential twist-off and/or motor damages can be avoided. Also, different bottom hole assembly's drilling dynamics response were analyzed to come up with optimal stabilization and connection practices to avoid back reaming while trip outs. This paper would showcase actual results which highlight improvements achieved in stagnant drilling performance of the project. The analysis would demonstrate how multiple wells were drilled in one run following the risk assessment developed from the optimization study and subsequent real time monitoring of mitigating actions while execution. The comprehensive bottoms-up drilling optimization approach helped save 4 planned days for each well, this really paves way to pursue applied-engineering solutions to achieve step change in drilling performances, especially on rigs which are severely limited either due to capacity or malperformance issues. The bottoms up approach taken to understand the drilling challenges followed by a methodical approach to address each of the challenges demonstrate importance of effective pre-job planning. Learnings from this study can be adopted as a template to mitigate similar drilling challenges.
Dalal Al-Subaiei, Mariam Jamal, Jassim Barki, Ibrahim Al-Azmi, Mohammad Al-Husaini
Day 3 Wed, October 20, 2021; https://doi.org/10.2118/205197-ms

Abstract:
Lower Burgan (LB) is one of the most mature reservoirs in north Kuwait divided in to 3 layers, first layer under partial depletion drive and suffer from low reservoir pressure, the second layer under edge-partial water drive and the last layer under active water drive. Increasing trend of water production and high GOR has become a big challenge to control the reservoir production, several of studies done to evaluate the best strategy to enhance the reservoir performance. A comprehensive review of the performance of the wells was conducted to diagnose the specific reason and necessary remedial measures to be adopted. The review included the assessment of the results of the material balance and numerical simulation studies, depletion strategies, wettability/ relative permeability footprints; proximity to the OWC; past completion practices, well integrity and the time lapse PNC/PLT/ Well testing data. Workflows were developed for the sublayers within the hydraulic unit for systematic water cut diagnostics and preventive steps. Identification of suitable technology to address water cut and GOR management was also done. The typical trend of water cut and GOR performance with time and the depletion strategy was established to add value to the ongoing production activities and well allowable for each hydraulic unit. Rate sensitive performance was analyzed for the integration into the production plans. Cyclic production is identified as one of the new ways to reduce the water production and maintain the production for the wells producing below the bubble point. The concept was tried at high water cut wells successfully to revive the well from about 100% to 87% water cut with a closure cycle for 3 month and sustaining the production for high GOR wells with good ESP performance. In addition, suitable candidates have been identified for Coning Control Completions to weaken the water encroachment into the downhole wellbore. The overall water cut for the reservoir has been stabilized during last one year, thus helping the water handling constraints at the gathering facility. This paper will discuss the successful approach to control coning and water encroachment for active Bottom and Edge-Water drive layer and how this approached helped to sustain the production on high GOR wells running below bubble point with necessary diagnostics and remedial measures.
Bjørn Egil Ludvigsen, Mohan Sharma
Day 3 Wed, October 20, 2021; https://doi.org/10.2118/205212-ms

Abstract:
Well performance calibration after history matching a reservoir simulation model ensures that the wells give realistic rates during the prediction phase. The calibration involves adjusting well model parameters to match observed production rates at specified backpressure(s). This process is usually very time consuming such that the traditional approaches using one reservoir model with hundreds of high productivity wells would take months to calibrate. The application of uncertainty-centric workflows for reservoir modeling and history matching results in many acceptable matches for phase rates and flowing bottom-hole pressure (BHP). This makes well calibration even more challenging for an ensemble of large number of simulation models, as the existing approaches are not scalable. It is known that Productivity Index (PI) integrates reservoir and well performance where most of the pressure drop happens in one to two grid blocks around well depending upon the model resolution. A workflow has been setup to fix transition by calibrating PI for each well in a history matched simulation model. Simulation PI can be modified by changing permeability-thickness (Kh), skin, or by applying PI multiplier as a correction. For a history matched ensemble with a range in water-cut and gas-oil ratio, the proposed workflow involves running flowing gradient calculations for a well corresponding to observed THP and simulated rates for different phases to calculate target BHP. A PI Multiplier is then calculated for that well and model that would shift simulation BHP to target BHP as local update to reduce the extent of jump. An ensemble of history matched models with a range in water-cut and gas-oil ratio have a variation in required BHPs unique to each case. With the well calibration performed correctly, the jump observed in rates while switching from history to prediction can be eliminated or significantly reduced. The prediction thus results in reliable rates if wells are run on pressure control and reliable plateau if the wells are run on group control. This reduces the risk of under/over-predicting ultimate hydrocarbon recovery from field and the project's cashflow. Also, this allows running sensitivities to backpressure, tubing design, and other equipment constraints to optimize reservoir performance and facilities design. The proposed workflow, which dynamically couple reservoir simulation and well performance modeling, takes a few seconds to run for a well, making it fit-for-purpose for a large ensemble of simulation models with a large number of wells.
Ahmed Alghamdi Abdullah Ghamdi, Daniel Opoku, Abeeb Awotunde, Mohamed Mahmoud, Qinzhuo Liao
Day 3 Wed, October 20, 2021; https://doi.org/10.2118/205125-ms

Abstract:
The Capacitance-Resistance Model, commonly known as CRM, is a data-driven model derived from the material balance equation, and only requires production and injection data for history matching and prediction of reservoir performance. The CRM has two model parameters: The input and output are related the first parameter is the connectivity (also called gain, or weight), which is a dimensionless number that quantifies the connectivity between producers and injectors (i.e. how much of the input is supporting the output). The second parameter is the time delay (also called time constant) and is a function of pore volume, total compressibility, and productivity indices, and it represents the time it takes for the input (injection) to result in an output (production). Since the CRM inception in 2005, several authors have further developed it to increase its range of applications. When CRM was first introduced, it was suited most for single-phase reservoirs. A recent improvement of the CRM added two-phase capability. In this project, Two-phase CRM was utilized to test how this tool performed in waterflooding optimization. The main hypothesis in CRM is that the several reservoir characteristics can be inferred from analyzing production and injection data only. These reservoir characteristics are the connectivity, which can be thought of as an analog to permeability, and the time constant, which is a measure of the pore volume and compressibility. CRM does not require core data, logs, seismic, or any rock or fluids properties. This hypothesis, that reservoir characteristics can be inferred from injection and production data, can be challenged easily since most reservoirs have gradients of fluid properties, multi-porosity systems, and heterogeneous formations with different wettability presences. Regardless, several publications have shown that CRM can result in high certainty output. To test the two-phase CRM, three synthetic heterogeneous reservoirs were created. Model 1 was developed with nearly stabilized injection and production data. Model 2 had more fluctuations in the injection data than model 1. And model 3 had extreme fluctuations in injection data compared to model 2 with lower rock and fluid compressibilities. The results presented in this project show that the CRM ability to match field production depends largely on two aspects: first is the compressibility of the system. When the compressibility was lowered in model 3, the CRM achieved excellent results. The second aspect is the degree of the fluctuations in injection rate the CRM is developed upon. Model 2 with a higher degree of injection rate fluctuations than model 1 has achieved a better future prediction performance. CRM model 3 was used to optimize the field waterflooding injection rates subject to two constraints, The first constraint is a set value for maximum field injection rate at any time step while the second constraint limits each injector maximum injection rate. The optimization of the annual injection rates has added 290,000 bbls of oil produced.
Jawaher Almorihil, Aurélie Mouret, Isabelle Hénaut, Vincent Mirallés, Abdulkareem AlSofi
Day 4 Thu, October 21, 2021; https://doi.org/10.2118/205161-ms

Abstract:
Gravity settling represents the main oil-water separation mechanism. Many separation plants rely only on gravity settling with the aid of demulsifiers (direct or reverse breakers) and others chemicals such as water clarifiers if they are required. Yet, other complementary separation methods exist including filtration, flotation, and centrifugation. In terms of results and more specifically with respect to the separated produced-water, the main threshold on its quality is the dispersed oil content. Even with zero discharge and reinjection into hydrocarbon formations, the presence of residual oil in the aqueous phase represents a concern. High oil content results into formation damage and losses in injectivity which necessitates formation stimulations and hence additional operational expenses. In this work, we investigated the effects of different separation techniques on separated water quality. Based on the results, we identified potential improvements to the existing separation process. We used synthetic well-characterized emulsions. The emulsions were prepared at the forecast water:oil ratio using dead crude oil and synthetic representative brine. To clearly delineate and distinguish the effectiveness of different separation methods, we exacerbated the conditions by preparing very tight emulsions compared with what is observed on site. With that, we investigated three separation techniques: gravity settling, centrifugation, and filtration. First, we used jar tests to study gravity settling, then a benchtop centrifuge at two speeds to evaluate centrifugation potential. Finally, for filtration, we tested two options: membrane and deep-bed filtrations. Concerning the water quality, we performed solvent extraction followed by UV analyses to measure the residual oil content as well as light transmission measurements in order to compare the efficiency of different separation methods. The results of analyses suggest that gravity settling was not efficient in removing oil droplets from water. No separation occurred after 20 minutes in every tested condition. However, note that investigated conditions were severe, tighter emulsions are more difficult to separate compared to those currently observed in the actual separation plant. On the other hand, centrifugation significantly improved light transmission through the separated water. Accordingly, we can conclude that the water quality was largely improved by centrifugation. In terms of filtration, very good water quality was obtained after membrane filtration. However, significant fouling was observed. With deep-bed filtration, produced water quality remained good and fouling was no longer observed. On the basis of those results, we conclude that for our case study, centrifugation and deep-bed filtration techniques can significantly improve quality of the separated and eventually reinjected water. Thereby, integration of any of the two methods in the separation plant will lead to more efficient produced-water reinjection, eliminating formation damage and frequent stimulations. Yet, it is important to note that economics should be further assessed.
Hans Christian Walker, Anton Shchipanov, Harald Selseng
Day 3 Wed, October 20, 2021; https://doi.org/10.2118/205148-ms

Abstract:
The Johan Sverdrup field located on the Norwegian Continental Shelf (NCS) started its production in October 2019. The field is considered as a pivotal development in the view of sustainable long-term production and developments on the NCS as well as creating jobs and revenue. The field is operated with advanced well and reservoir surveillance systems including Permanent Downhole Gauges (PDG), Multi-Phase Flow-Meters (MPFM) and seismic Permanent Reservoir Monitoring (PRM). This provides an exceptional basis for reservoir characterization and permanent monitoring. This study focuses on reservoir characterization to improve evaluations of sand permeability-thickness and fault transmissibility. Permanent monitoring of the reservoir with PDG / MPFM has provided an excellent basis for applying different methods of Pressure Transient Analysis (PTA) including analysis of well interference and time-lapse PTA. Interpretation of pressure transient data is today based on both analytical and numerical reservoir simulations (fit-for-purpose models). In this study, such models of the Johan Sverdrup reservoir regions have been assembled, using geological and PVT data, results of seismic interpretations and laboratory experiments. Uncertainties in these data were used to guide and frame the scope of the study. The interference analysis has confirmed communication between the wells located in the same and different reservoir regions, thus revealing hydraulic communication through faults. Sensitivities using segment reservoir simulations of the interference tests with different number of wells have shown the importance of including all the active wells, otherwise the interpretation may give biased results. The estimates for sand permeability-thickness as well as fault leakage obtained from the interference analysis were further applied in simulations of the production history using the fit-for-purpose reservoir models. The production history contains many pressure transients associated with both flowing and shut-in periods. Time-lapse PTA was focused on extraction and history matching of these pressure transients. The simulations have provided reasonable match of the production history and the time-lapse pressure transients including derivatives. This has confirmed the results of the interference analysis for permeability-thickness and fault leakage used as input for these simulations. Well interference is also the dominating factor driving the pressure transient responses. Drainage area around the wells is quickly established for groups of the wells analyzed due to the extreme permeability of the reservoir. It was possible to match many transient responses with segment models, however mismatch for some wells can be explained by the disregard of wells outside the segments, especially injectors. At the same time, it is a useful indication of communication between the regions. The study has improved reservoir characterization of the Johan Sverdrup field, also contributing to field implementation of combined PTA methods.
Sukru Merey, Tuna Eren, Can Polat
Day 4 Thu, October 21, 2021; https://doi.org/10.2118/205223-ms

Abstract:
Since the 2000s, the number of gas hydrate wells (i.e., exploration wells, production test wells) has increased. Moreover, in the marine environment, gas hydrate zones are drilled in conventional hydrocarbon wells. Different than conventional hydrocarbon wells, the heat released with cement hydration cannot be ignored because gas hydrates are heat sensitive. In this study, by analyzing different cement compositions (conventional cement compositions and novel low-heat of hydration cement), it is aimed to investigate the effect of the heat of cement hydration on gas hydrate zones near the wellbore. For this purpose, numerical simulations with TOUGH+HYDRATE simulator were conducted in the conditions of the Nankai Trough gas hydrates. According to the numerical simulations in this study, if the increase in temperature in the cemented layer is above 30°C, significant gas hydrate dissociation occurs, and free gas evolved in the porous media. This might cause gas channeling and poor cement bond. The heat released with cement hydration generally affects the interval between the cemented layer and 0.25 m away from the cemented layer. Within a few days after cementing, pressure, temperature, gas hydrate saturation, and gas saturation returned to almost their original values.
Rafael Zambrano, Yevhen Makar, Michael Sadivnyk, Andriy Butenko, Oleksandr Doroshenko, Volodymyr Novikov, Miljenko Cimic, Chiara Cavalleri, Samira Ahmad, Yernur Akashev
Day 4 Thu, October 21, 2021; https://doi.org/10.2118/205214-ms

Abstract:
The Sakhalin Field is located in the Dnieper-Donets Basin, east of Ukraine, and has been producing 7.7 billion cubic meters of natural gas in place from carboniferous rocks since the 1980s. Notwithstanding, it is strongly believed that significant untapped resources remain in the field, specifically those classified as tight intervals. Advances in wireline logging technology have brought, besides better accuracy on measurements behind the casing, a new measurement called fast neutron cross-section (FNXS), which has proved to be sensitive enough to the volume of gas in low-porosity formations. This enabled a quantitative interpretation for a better understanding of where these additional resources may lie in the Sakhalin Field. The methodology is based on advanced pulsed neutron spectroscopy logs to assess the essential formation properties such as lithology, porosity, and gas saturation and reduce the evaluation uncertainty in potential tight gas intervals. The advanced technology combines measurements from multiple detectors that represent independent formation properties such as formation sigma, thermal neutron porosity, FNXS, and elemental fractions. To address the lithology, the tool measures directly the rock elements required to determine representative mineralogy and matrix properties, which in turn are used to compensate for the matrix effects and obtain a reliable porosity and gas volume estimation. The methodology was tested on the upper Visean productive zones (Mississippian epoch) characterized by its low porosity (<10 pu) and permeability (<10 mD). In the past, those intervals have been overlooked because of inconclusive petrophysical interpretation based on basic openhole logs and their low production in some areas of the field. The necessity to finding new reserves has motivated the re-evaluation of possible bypassed tight-gas intervals by logging of mature wells behind casing in different sectors of the field. Advanced pulsed neutron spectroscopy logging behind casing uniquely identifies reserves in tight-gas intervals where basic open-hole interpretations were ambiguous. The gas production obtained from the perforated intervals supports the formation evaluation parameters estimated from the standalone interpretation of the pulsed neutron data. This work describes in detail the application of the alternative methodology and interpretation workflow to evaluate the formation through the casing. A concrete example is presented to illustrate the effectiveness of this approach in the revealing and development of tight gas reservoirs in mature fields in the Dnieper-Donets Basin.
Mengyuan Chen, Jin Tang, Ding Zhu, Alfred Daniel Hill
Day 2 Tue, October 19, 2021; https://doi.org/10.2118/205136-ms

Abstract:
Distributed acoustic sensing (DAS) has been used in the oil and gas industry as an advanced technology for surveillance and diagnostics. Operators use DAS to monitor hydraulic fracturing activities, to examine well stimulation efficacy, and to estimate complex fracture system geometries. Particularly, low-frequency DAS can detect geomechanical events such as fracture-hits as hydraulic fractures propagate and create strain rate variations. Analysis of DAS data today is mostly done post-job and subject to interpretation methods. However, the continuous and dense data stream generated live by DAS offers the opportunity for more efficient and accurate real-time data-driven analysis. The objective of this study is to develop a machine learning-based workflow that can identify and locate fracture-hit events in simulated strain rate response that is correlated with low-frequency DAS data. In this paper, "fracture-hit" refers to a hydraulic fracture originated from a stimulated well intersecting an offset well. We start with building a single fracture propagation model to produce strain rate patterns observed at a hypothetical monitoring well. This model is then used to generate two sets of strain rate responses with one set containing fracture-hit events. The labeled synthetic data are then used to train a custom convolutional neural network (CNN) model for identifying the presence of fracture-hit events. The same model is trained again for locating the event with the output layer of the model replaced with linear units. We achieved near-perfect predictions for both event classification and localization. These promising results prove the feasibility of using CNN for real-time event detection from fiber optic sensing data. Additionally, we used image analysis techniques, including edge detection, for recognizing fracture-hit event patterns in strain rate images. The accuracy is also plausible, but edge detection is more dependent on image quality, hence less robust compared to CNN models. This comparison further supports the need for CNN applications in image-based real-time fiber optic sensing event detection.
Stefan Dinger, Andrei Casali, Frank Lind, Azwan Hadi Keong, Johnny Bårdsen, Bjørn Engvald Staveland Nilsen, Kjell Tore Nesvik, Eleanor Bell, Terje Sporstøl
Day 4 Thu, October 21, 2021; https://doi.org/10.2118/205157-ms

Abstract:
Coiled tubing (CT) operations in the Norwegian continental shelf (NCS) often require a long and large-outside-diameter pipe due to big diameter completions, deep wells, and the need for high annular velocity during fluid circulation. However, getting the CT string onboard becomes a challenge when the crane lifting limit is 35 t, and using a standalone crane barge increases the cost of the operation. The alternative is spooling the CT from a vessel to the platform. Boat spooling is done by placing the CT string on a floating vessel with dynamic positioning while the standard CT injector head is secured at the edge of the platform to pull the pipe from the vessel to an empty CT reel on the platform. The boat is equipped with a CT guide; special tension clamps; and an emergency disconnect system, which consists of a standard CT shear-seal blowout preventer. The technique requires careful study of the platform structure for placement of the injector head support frame, metocean data of the field, and equipment placement on the vessel and platform. The boat spooling operation of a 7,700-m long, 58.7-t, 2.375-in.-outside-diameter CT string was successfully executed for a platform at 70-m height from mean sea level. The total operating time from hooking up the vessel to successfully spooling the string only took 12 hours. Historically for the region, the method has been attempted in sea state of up to 4-m wave height and 16 knots maximum wind speed. For this operation, the spooling was carried out during an average sea state of 2-m wave height and 15-knot wind speed. The continuous CT string allows a telemetry cable to be installed inside the pipe after the CT is spooled onto the platform reel, enabling real-time downhole measurements during the intervention. Such installation is not possible or presents high risk if the CT string is taken onboard by splicing two sections of pipe together with a spoolable connector or butt welding. From a cost perspective, the boat-spooling operation had up to 80% direct cost saving for the operator when compared to other methods of lifting a single CT string onboard, such as using a motion-compensated barge crane. The planning for the boat spooling included several essential contingency plans. Performing a CT boat spooling operation in a complex environment is possible and opens new opportunities to use longer and heavier CT strings, with lower mobilization costs. Such strings enable more advanced and efficient interventions, with the option of using real-time CT downhole measurements during the execution of a wide range of production startup work. This, in turn, is critical to support the drilling of more extended reach wells, which allow access to untapped reservoirs.
Richard Patience, Mark Bastow, Martin Fowler, Julian Moore, Craig Barrie
Day 4 Thu, October 21, 2021; https://doi.org/10.2118/205130-ms

Abstract:
Production allocation from petroleum geochemistry is defined here as the quantitative determination of the amount or portion of a commingled fluid to be assigned to two or more individual fluid sources (e.g., a pipeline, field, reservoir, well) at a particular moment in time, based on the fluid chemistry. It requires: i) knowledge of the original chemical compositions of each of the fluids prior to mixing (referred to here as the "end members"), and ii) that statistically valid differences in their chemistries can be identified. Petroleum geochemical-based methods for production monitoring and allocation are much lower cost than using production logging tools, as there is no additional rig time or extra personnel required at the well site. Additionally, no intervention to the production of hydrocarbons from a well is required and, hence, there is none of the risk entailed in additional operational activity. Geochemical methods are applicable to a wide range of fields, irrespective of pressure, temperature, reservoir quality and reservoir fluid type. The method has been in existence for over 30 years, during which time a number of different analytical methods, data pre-processing and treatment approaches have been applied. This paper summarises these approaches, and provides examples, but also describes a "best practice" which is not a "one size fits all" approach, as is sometimes seen in the literature. A successful production allocation study consists of the following steps: i) Selection of end member samples that contribute to the commingled production fluid; ii) Determination of the differences in chemical composition of the end members through laboratory analysis of the end members (e.g. by WO-GC), replicate analyses of samples and statistical treatment of the data (e.g. PCA); iii) If statistically significant differences exist, laboratory analysis of the end members and commingled fluids with appropriate replicate analyses of samples; iv) Data selection, pre-processing (e.g. selection of ratios or concentrations of components); v) Determination of end member contributions by solving equations (e.g. least squares best fit) and uncertainty estimation (e.g. Monte Carlo or Bootstrap methods). The differences in approach for conventional versus unconventional plays are also discussed.
Mostafa Sa'Eed Yakoot, Adel Mohamed Salem Ragab, Omar Mahmoud
Day 2 Tue, October 19, 2021; https://doi.org/10.2118/205134-ms

Abstract:
Constructing and maintaining integrity for different types of wells requires accurate assessment of posed risk level, especially when one barrier element or group of barriers fails. Risk assessment and well integrity (WI) categorization is conducted typically using traditional spreadsheets and in-house software that contain their own inherent errors. This is mainly because they are subjected to the understanding and the interpretation of the assigned team to WI data. Because of these limitations, industrial practices involve the collection and analysis of failure data to estimate risk level through certain established probability/likelihood matrices. However, those matrices have become less efficient due to the possible bias in failure data and consequent misleading assessment. The main objective of this work is to utilize machine learning (ML) algorithms to develop a powerful model and predict WI risk category of gas-lifted wells. ML algorithms implemented in this study are; logistic regression, decision trees, random forest, support vector machines, k-nearest neighbors, and gradient boosting algorithms. In addition, those algorithms are used to develop physical equation to predict risk category. Three thousand WI and gas-lift datasets were collected, preprocessed, and fed into the ML model. The newly developed model can predict well risk level and provide a unique methodology to convert associated failure risk of each element in the well envelope into tangible value. This shows the total potential risk and hence the status of well-barrier integrity overall. The implementation of ML can enhance brownfield asset operations, reduce intervention costs, better control WI through the field, improve business performance, and optimize production.
Arjang Gandomkar, David Katz, Ricardo Gomez, Anders Gundersen
Day 4 Thu, October 21, 2021; https://doi.org/10.2118/205221-ms

Abstract:
Casing Deformation has presented itself in numerous unconventional basins. Severe deformation interferes with multistage fracturing, in particular with plug-and-perforation (also known as plug-and-perf) operations, the most common stage isolation method in unconventional development. Casing Deformation can greatly impact 20-30% of field productivity of horizontal wells in certain US shale and tight oil fields (Jacobs, 2020). Reservoir accessibility and well integrity are the two separate issues when considering casing deformation. In this paper, the impact of geomechanically driven casing deformation on reservoir accessibility that in turn affects production and economics, will be discussed. Origin of casing deformation within a target zone lies in natural fractures placed in highly anisotropic stress regimes. When these fractures are perturbed by hydraulic stimulation, slow slip or dynamic failure of the rock may occur. This phenomenon is intensified by active tectonics, high anisotropic in-situ stresses, and poor completion practices, i.e., poor cement. This paper evaluates these processes by demonstrating failure conditions of wellbores in different stress states and well orientations representative of unconventional basins. It reviews how these conditions can be evaluated in the reservoir, so risk can be estimated. The mitigation procedures to reduce casing deformation impact to operations through either well planning or completions design are discussed. Finally, this paper will also review an alternative completion method to plug-and-perf that allows limited entry completion technique in restricted ID casing due to casing deformation with a field case study.
Ahmed Abdullah Alghamdi, Nawaf Saud Almutairi, Ali Muslim, Humoud Khaldi, Abdulazeez Abdulraheem
Day 4 Thu, October 21, 2021; https://doi.org/10.2118/205163-ms

Abstract:
Objective/Scope Accurate well production rate measurement is critical for reservoir management. The production rate measurement is carried out using surface devices, such as orifice flow meter and venturi flow meter. For large offshore fields development with a high number of wells, the installation and maintenance costs of these flowmeters can be significant. Therefore, an alternative solution needs to be developed. This paper described the successful implementation of Artificial Intelligence in predicting the production rate of big-bore gas wells in an offshore field. Methods, Procedures, Process Successful application of AI depends on capitalizing on a large set of data. Therefore, flowing parameters data were collected for more than 30 gas wells and totaling over 100,000 data points. These wells are producing gas with slight solid production from a high-pressure high-temperature field. In addition, these wells are equipped with a multistage choke that reduces the noise and vibration levels. An Artificial Neural Network is trained on the data using Gradient Descent method as the optimization algorithm. The network takes as an input the upstream and downstream pressure and temperature, and the choke size. The output is the gas rate measured in MMscf/day. Results, Observations, Conclusions The data set was divided into 70% for training the neural network and 30% for validation. Artificial Neural Network (ANN) was used and the developed model compared exceptionally well with the gas rates measured from the calibrated venturi meters. The gas rate estimation was within a 5% error. The model was developed for two types of completions: 7" and 9-5/8" production tubing. One of the challenges was how to estimate the choke wear which plays a major role in the quality of the choke size data. A linear choke wear deterioration is applied in this case, while work in progress is taking place for acquiring acoustic data that can significantly improve the choke wear modeling. Novel/Additive Information The novel approach presented in this paper capitalizes on Al analytics for estimating accurate gas flow rate values. This approach has improved the reservoir data management by providing accurate production rate values which has drastically improved the reservoir simulation. Moreover, the robustness of the AI model has forced us to rethink the conventional design of installing a flow meter for every well. As shown in this paper, the AI model served as an alternative to conventional venturi meters. We believe that the application of AI models to other aspects of production surveillance will lead to a shift into how operators design production facilities.
Simon Berry, Zahid Khan, Diego Corbo, Tom Marsh, Alexandra Kidd, Elliot Moore
Day 4 Thu, October 21, 2021; https://doi.org/10.2118/205204-ms

Abstract:
Redevelopment of a mature field enables reassessment of the current field understanding to maximise its economic return. However, the redevelopment process is associated with several challenges: 1) analysis of large data sets is a time-consuming process, 2) extrapolation of the existing data on new areas is associated with significant uncertainties, 3) screening multiple potential scenarios can be tedious. Traditional workflows have not combatted these challenges in an efficient manner. In this work, we suggest an integrated approach to combine static and dynamic uncertainties to streamline evaluating of multiple possible scenarios is adopted, while quantifying the associated uncertainties to improve reservoir history matching and forecasting. The creation of a fully integrated automated workflow which includes geological and fluid models is used to perform Assisted History Matching (AHM) that allows the screening of different parameter combinations whilst also calibrating to the historical data. An ensemble of history matched models is then selected using dimensionality reduction and clustering techniques. The selected ensemble is used for reservoir predictions and represents a spread of possible solutions accounting for uncertainty. Finally, well location optimisation under uncertainty is performed to find the optimal well location for multiple equiprobable scenarios simultaneously. The suggested workflow was applied to the Northern Area Claymore (NAC) field. NAC is a structurally complex, Lower Cretaceous stacked turbidite, composed of three reservoirs, which have produced ~170 MMbbls of oil since 1978 from an estimated STOIIP of ~500 MMstb. The integrated workflow helps to streamline the redevelopment project by allowing geoscientists and engineers to work together, account for multiple scenarios and quantify the associated uncertainties. Working with static and dynamic variables simultaneously helps to get a better insight into how different properties and property combinations can help to achieve a history match. Using powerful hardware, cloud-computing and fully parallel software allow to evaluate a range of possible solutions and work with an ensemble of equally probable matched models. As an ultimate outcome of the redevelopment project, several prediction profiles have been produced in a time-efficient manner, aiming to improve field recovery and accounting for the associated uncertainty. The current project shows the value of the integrated approach applied to a real case to overcome the shortcomings of the traditional approach. The collaboration of experts with different backgrounds in a common project permits the assessment of multiple hypotheses in an efficient manner and helps to get a deeper understanding of the reservoir. Finally, the project provides evidence that working with an ensemble of models allows to evaluate a range of possible solutions and account for potential risks, providing more robust predictions for future field redevelopment.
Daniel Rodrigues Santos, André Ricardo Fioravanti, Antonio Alberto Souza Santos, Denis José Schiozer
Day 4 Thu, October 21, 2021; https://doi.org/10.2118/205207-ms

Abstract:
Although several studies have shown that life-cycle well control strategies can significantly improve a field's economic return, the industry often relies on short-term strategies. One drawback of traditional parameterization, adopted for well control life-cycle numerical optimization, is that it often generates control strategies that yield impractical abrupt changes in production curves. Another issue, especially in cases with a large number of decision variables, is the local optima convergence related to the non-convex optimization problems. In this context, we proposed and compared four life-cycle well control parameterizations to maximize the net present value (NPV) of the field under uncertainties, which are able to mitigate both the above-mentioned problems. The first parameterization optimizes the apportionment of well rates at the beginning of the field management and well shut-in time. The other three are based on optimizing the coefficients of parametric equations (first-and second-order polynomials, and logistic equation) to guide the bottom-hole pressure (BHP) over time. We executed each parameterization five times in a deterministic reservoir scenario and compared them with well control short-term strategy that prioritizes production in wells with higher oil-water ratio and that aimed to replicate the general industry practice. In this strategy, the wells’ priority rank was updated at every 30-simulation days. Subsequently, the best parameterization was used to select the well control life-cycle strategy under reservoir uncertainties and this strategy was applied to the reference model representing a real reservoir. The results showed that all the proposed parametrizations significantly improved the NPV in comparison to the well control short-term strategy, while simultaneously ensuring a smooth well production curve. The logistic equation presented the best result among all parameterizations, as it delivered both the highest average of NPV and the smallest dispersion over the five experiment repetitions. This parameterization also produced similar results when applied under uncertainties and for the reference model. These results endorse the importance of not only relying on a short-term strategy, but also planning it for the life-cycle.
Recep Bakar, Erdal Ozkan, Hossein Kazemi
Day 3 Wed, October 20, 2021; https://doi.org/10.2118/205174-ms

Abstract:
Diagnostic fracture injection tests (DFIT) are used as an indirect method to determine closure pressure and formation effective permeability in unconventional reservoirs as a first step in formation evaluation. The information obtained from DFIT is particularly useful because it is obtained before any production for a given well is available. In DFIT, a small fracture is created by injecting few barrels of completion fluid until formation breaks down and a fracture is initiated and propagates a short distance into the reservoir. Then, injection is stopped, and the pressure decline (or falloff) is monitored. From this pressure decline, the effective permeability of the formation is estimated by Nolte's G-function, log-log plot, or square root of time analysis. In this research, the viability of the common DFIT analysis techniques was investigated for unconventional reservoirs with and without micro-fractures by using a numerical hydraulic fracturing simulator, CFRAC. The results of numerical simulations were investigated to assess the impact of permeability, residual fracture aperture, and complex fracture networks on conventional DFIT interpretations. For the example considered in this work, the commonly used G-function analysis yielded estimates of permeability over an order of magnitude higher than the simulated matrix permeability. Error in the G-function estimates of permeability were higher for higher matrix permeability and in the existence of a fracture network. On the other hand, straight-line analysis of Ap versus G-time yielded much closer (in the same order of magnitude) estimates of permeability.
Smith Edward Leggett, Ding Zhu, Alfred Daniel Hill
Day 4 Thu, October 21, 2021; https://doi.org/10.2118/205178-ms

Abstract:
Fiber-optic cables cemented outside of the casing of an unconventional well measure cross-well strain changes during fracturing of neighboring wells with low-frequency distributed acoustic sensing (LF-DAS). As a hydraulic fracture intersects an observation well instrumented with fiber-optic cables, fracture fluid injected at ambient temperatures can cool a section of the sensing fiber. Often, LF-DAS and distributed temperature sensing (DTS) cables are run in tandem, enabling the detection of such cooling events. The increasing use of LF-DAS for characterizing unconventional hydraulic fracture completions demands an investigation of the effects of temperature on the measured strain response by LF-DAS. Researchers have demonstrated that LF-DAS can be used to extract the temporal derivative of temperature for use as a differential-temperature-gradient sensor. However, differential-temperature-gradient sensing is predicated on the ability to filter strain components out of the optical signal. In this work, beginning with an equation for optical phase shift of LF-DAS signals, a model relating strain, temperature, and optical phase shift is explicitly developed. The formula provides insights into the relative strength of strain and temperature effects on the phase shift. The uncertainty in the strain-rate measurements due to thermal effects is estimated. The relationship can also be used to quantify uncertainties in differential-temperature-gradient sensors due to strain perturbations. Additionally, a workflow is presented to simulate the LF-DAS response accounting for both strain and temperature effects. Hydraulic fracture geometries are generated with a 3D fracture simulator for a multi-stage unconventional completion. The fracture width distributions are imported by a displacement discontinuity method program to compute the strain-rates along an observation well. An analytic model is used to approximate the temperature in the fracture. Using the derived formulae for optical phase shift, the model outputs are then used to compute the LF-DAS response at a fiber-optic cable, enabling the generation of waterfall plots including both strain and thermal effects. The model results suggest that before, during, and immediately following a fracture intersecting a well instrumented with fiber, the strain on the fiber drives the LF-DAS signal. However, at later times, as completion fluid cools the observation well, the temperature component of the LF-DAS signal can equal or exceed the strain component. The modeled results are compared to a published field case in an attempt to enhance interpretation of LF-DAS waterfall plots. Finally, we propose a sensing configuration in order to identify the events when "wet fractures" (fractures with fluids) intersect the observation well.
Mohammed Alghazal, Dimitrios Krinis
Day 2 Tue, October 19, 2021; https://doi.org/10.2118/205146-ms

Abstract:
Fluid saturation data obtained from core analysis are used as control points for log calibration, saturation modeling and sweep evaluation. These lab-derived data are often viewed as ground-truth values without fundamentally understanding the key limitations of experimental procedures or scrutinizing the accuracy of measured lab data. This paper presents a unique assessment of sponge core data through parameterization, uncertainty analysis and Monte-Carlo modeling of critical variables influencing lab-derived saturation results. This work examines typical lab data and reservoir information that could impact final saturation results in sponge coring. We dissected and analyzed ranges of standard raw data from Dean-Stark and spectrometric analysis (including, gravimetric weights, distilled water volumes, pore volumes and sponge's absorbance), input variables of fluid and rock properties (such as, water salinity, formation volume factors, plug's dimension and stress corrections), governing equations (including, salt correction factors, water density correlations and lab mass balance equations) and other factors (for instance, sources of water salinity, filtrate invasion, bleeding by gas liberation and water evaporation). Based on our investigation, we have identified and statistically parameterized 11 key variables to quantify the uncertainty in lab-derived fluid saturation data in sponge cores. The variables' uncertainties were mapped into continuous distributions and randomly sampled by Monte-Carlo simulation to generate probabilistic saturation models for sponge cores. Simulation results indicate the significance of the water salinity parameter in mixed salinity environments, ranging between 20,000 to 150,000 ppm. This varied range of water salinity produces a wide uncertainty spectrum of core oil saturation in the range of +/- 3 to 10% saturation unit. Consequently, we developed two unique salinity variance models to capture the water salinity effect and minimize the uncertainty in the calculation of core saturation. The first model uses a material balance to solve for the salinity given the distilled water volume and gravimetric weight difference of the sample before and after leaching. The second model iteratively estimates the salinity required to achieve 100% of total fluids saturation at reservoir condition after correcting for the bleeding, stress and water evaporation effects. Our work shows that these derived models of water salinity are consistent with water salinity data from surface and bottomhole samples. Despite the prominence of applications of core saturation data in many aspects of the industry, thorough investigation into its quality and accuracy is usually overlooked. To the best of our knowledge, this is the first paper to present a novel analysis of the uncertainty coupled with Monte-Carlo simulation of lab-derived saturation's data from sponge cores. The modeling approach and results highlighted in this work provide the fundamental framework for modern uncertainty assessment of core data.
Bogdan-George Davidescu, Mathias Bayerl, Christoph Puls, Torsten Clemens
Day 4 Thu, October 21, 2021; https://doi.org/10.2118/205211-ms

Abstract:
Enhanced Oil Recovery pilot testing aims at reducing uncertainty ranges for parameters and determining operating conditions which improve the economics of full-field deployment. In the 8.TH and 9.TH reservoirs of the Matzen field, different well configurations were tested, vertical versus horizontal injection and production wells. The use of vertical or horizontal wells depends on costs and reservoir performance which is challenging to assess. Water cut, polymer back-production and pressures are used to understand reservoir behaviour and incremental oil production, however, these data do not reveal insights about changes in reservoir connectivity owing to polymer injection. Here, we used consecutive tracer tests prior and during polymer injection as well as water composition to elucidate the impact of various well configurations on sweep efficiency improvements. The results show that vertical well configuration for polymer injection and production leads to substantial acceleration along flow paths but less swept volume. Polymer injection does not only change the flow paths as can be seen from the different allocation factors before and after polymer injection but also the connected flow paths as indicated by a change in the skewness of the breakthrough tracer curves. For horizontal wells, the data shows that in addition to acceleration, the connected pore volume after polymer injection is substantially increased. This indicates that the sweep efficiency is improved for horizontal well configurations after polymer injection. The methodology leads to a quantitative assessment of the reservoir effects using different well configurations. These effects depend on the reservoir architecture impacting the changes in sweep efficiency by polymer injection. Consecutive tracer tests are an important source of information to determine which well configuration to be used in full-field implementation of polymer Enhanced Oil Recovery.
Fu Jin, Wang Xi, Ding Mingming, Yang Guobin, Zhang Shunyuan, Liu Bingshan, Chen Chen
Day 3 Wed, October 20, 2021; https://doi.org/10.2118/205167-ms

Abstract:
The crude oil price has been keeping at a low level in recent years, which made China's government put more efforts in the development of underground oil storages in depleted salt caverns. Under the initiative of "the Belt and Road", a more concrete concept which is "the Silk Road Economic Belt and the 21st-Century Maritime Silk Road" successfully connects Jiangsu Province in the east of China. Consisting of 20 depleted caverns, Huai'an project that is still under planning is one of the most successful examples that turn depleted salt caverns into underground crude oil storages in China. Each cavern takes up 24×104m3, while the project totally takes up 480×104m3. TDMA algorithm was adopted to solve the heat exchange model of oil, brine and surrounding rocks, revealing the relationship between temperature and cavern pressure. Salt rock safety factor, salt cavern shrinkage ratio, axial stress and ground subsidence were taken into consideration to establish a 3-dimension salt rock creep model for 19 depleted salt caverns, so that the caverns’ shapes were optimized. Hydrodynamics models were used to determine the oil's flow rate into and out of a 1000m deep cavern whose thermal field was simulated by software to reveal the temperature limit of oil and brine. Due to geothermal gradient and continuous heat transmission, the average temperature of oil and brine goes up from 35°C to 44.3°C within 7 years, while the inner pressure goes up from 12.96MPa to 21.93MPa in a depleted salt cavern. Salt creep ratio decreases as oil is stored in underground caverns for a longer period. Salt is hardly penetrated by oil, while the temperature change has a strong influence on caverns’ internal pressure. The thermal expansion factor and compressibility coefficient of crude oil and brine are both crucial to the temperature's effect on internal pressure. Caverns that have larger segments in their upper-middle or middle parts are more stable and resistant to salt creep than those that have larger segments in their lower parts. When oil is injected or pumped out, it is necessary to make the internal pressure lower than the static pressure of surrounding rocks. Hence, the most appropriate flow rate of crude oil is 4.5m/s. Crude oil that is stored in deep salt caverns may be heated up to 60°C due to the geothermal gradient, but the flammable gas in oil is rapidly gasified or even explodes when it is pumped out to the surface. To avoid accidents and air pollution, oil is cooled down before being delivered via pipelines. Oil tanks used to be applied by scale in China, however they are too obvious on the ground to comply with national strategic energy safety. Compared with oil tanks of similar volumes, the Huai'an underground oil storages may save the overall cost by 35.3%. It is the first time that the salt rock creep model is established in depleted salt caverns, while the conclusion overthrew the common preference of regular cylindrical caverns.
Abubakar Isah, Abdulrauf Rasheed Adebayo, Mohamed Mahmoud, Lamidi O. Babalola, Ammar El-Husseiny
Day 4 Thu, October 21, 2021; https://doi.org/10.2118/205176-ms

Abstract:
Capillary pressure (Pc) and electrical resistivity index (RI) curves are used in many reservoir engineering applications. Drainage capillary pressure curve represents a scenario where a non-wetting phase displaces a wetting phase such as (i) during gas injection (ii) gas storage in reservoirs (e.g. aquifer or depleted hydrocarbon reservoirs). The gas used for injection is typically natural gas, N2, or CO2. Gas storage principally used to meet requirement variations, and water injection into oil-wet reservoirs are drainage processes. Resistivity index (RI) curve which is used to evaluate the potential of oil recovery from a reservoir, is also an important tool used in log calibration and reservoir fluid typing. The pore drainage mechanism in a multimodal pore system is important for effective recovery of hydrocarbon reserves; enhance oil recovery (EOR) planning and underground gas storage. The understanding of pore structure and drainage mechanism within a multimodal pore system during petrophysical analysis is of paramount importance to reservoir engineers. Therefore, it becomes inherent to study and establish a way to relate these special core analyses laboratory (SCAL) methods with quick measurements such as the nuclear magnetic resonance (NMR) to reduce the time requirement for analysis. This research employed the use of nuclear magnetic resonance (NMR) to estimate saturation exponent (n) of rocks using nitrogen as the displacing fluid. Different rock types were used in this study that cover carbonates, sandstones, and dolomites. We developed an analytical workflow to separate the capillary pressure curve into capillary pressure curve for macropores and a capillary pressure curve for the micropores, and then used these pore scale Pc curves to estimate an NMR - capillary pressure - based electrical resistivity index - saturation (NMR-RI-Sw) curve for the rocks. We predicted the saturation exponent (n) for the rock samples from the NMR-RI-Sw curve. The NMR-based saturation exponent estimation method requires the transverse (T2) relaxation distribution of the rock - fluid system at various saturations. To verify the reliability of the new workflow, we performed porous plate capillary pressure and electrical resistivity measurements on the rock samples. The reliability of the results for the resistivity index curve and the saturation exponent was verified using the experimental data obtained from the SCAL method. The pore scale Pc curve was used to ascertain the drainage pattern and fluid contribution of the different pore subsystems. For bimodal rock system, the drainage mechanism can be in series, in parallel, or in series - parallel depending on the rock pore structure.
Tohoko Tajima
Day 3 Wed, October 20, 2021; https://doi.org/10.2118/205143-ms

Abstract:
Modeling of acid fracturing process is challenging because of the coupled complex effects of flow through porous media and fractures, chemical reaction in a geostatistical base, wormhole propagation, and reservoir heterogeneity. To avoid the complexity, decoupled approaches are commonly used; the reservoir effect is represented by leakoff with a constant leakoff coefficient, and analytical solutions for heat flux from a reservoir is used to avoid complexity. An acid fracturing numerical model is presented that is coupled with a single-phase black oil reservoir simulator for a vertical well in the carbonate reservoir. The coupled acid fracturing model considers fracture propagation, acid transport, and heat transfer. After simulating acid fracturing, the conductivity of the fracture is calculated using empirical correlations, and the productivity is computed by simulating the flow to the well. Non-isothermal condition is assumed to simulate the flow in both the fracture and reservoir because the acid reaction is temperature sensitive. Leakoff from fracture to reservoir is simulated with a reservoir flow model for pressure and leakoff velocity as functions of time and location. Wormhole propagation from the fracture is considered by using empirical equations for wormhole propagation based on leakoff velocity estimated from the reservoir simulation. The benefits of coupled modeling are evaluated by comparing the conventional acid fracturing model which uses a decoupled approach to the numerical acid fracturing model developed in this study. The results show that the coupling reservoir model improves the accuracy of estimated in fracture conductivity. It has been shown that the analytical equations for heat from a reservoir used in literature overestimates the final acid fracture conductivity. Thus, it is suggested to use fully numerically solve fluid flow and energy balance in a fracture and a reservoir. Complex leakoff due to pressure and temperature change with time and wormhole propagation was implemented in the simulator. The wormhole effect was added and the distribution of leakoff coefficient was reasonable. A comparison of simulation results with and without wormholes showed that the significant difference was not observed in acid concentration, but ideal width distribution was lower with wormholes. It is concluded based on the observation of the study that the leakoff from acid fracture represented by a reservoir model with wormhole propagation is important to correctly understand acid fracture efficiency. Simply using a constant leakoff coefficient can lead to significant error and misleading conclusions.
Jawaher Almorihil, Aurélie Mouret, Marie Marsiglia, Vincent Mirallès, Abdulkareem AlSofi
Day 2 Tue, October 19, 2021; https://doi.org/10.2118/205229-ms

Abstract:
In previous work, we demonstrated that EOR chemicals had minor effect on topside processes in terms of separation, corrosion and scale inhibition. Regarding the oil/water separation, the most noticeable effect was a deterioration in separated water quality that was deemed manageable. This paper will further investigate the impact of produced polymer and surfactant on the quality of separated water. To mimic the separation plant potential feed and operations, experimental work has been carried out by preparing oil/brine mixtures at different surfactant/polymer concentrations with oilfield additives. Three main parameters have been varied: surfactant/polymer (SP) concentration, temperature, and water cut. The final test matrix consists of 24 tests. We first assessed the impact of EOR additives on the type of generated emulsions (O/W or W/O). Then we performed bottle tests taking into account the different operating parameters in order to investigate the kinetics of water/oil separation. Finally, we carried out physical-chemical analyses on the separated water in order to evaluate its quality. In terms of concentration effects, the results suggest that SP concentration had minimal impact on pH and density of the aqueous phase. Bottle tests showed that phase inversion was obtained at intermediate and high SP concentrations for both water cuts. In addition, separated water quality deteriorated in systems of intermediate SP concentration and slightly improved at high concentration at 32 °C. At 54°C, higher SP concentrations resulted in poorer water quality. Kinetics of separation accelerated with higher SP concentrations. In terms of temperature effects, a slight decrease in both viscosity and density of emulsions was observed at higher temperatures. Kinetics of separation also improved with higher temperatures, as did the quality of the separated water. In terms of water cut effects, viscosity and density of the aqueous phase were not impacted. Moreover, phase inversion of the emulsion (from water-in-oil to oil-in-water) occurred when water cut increased from 75 to 85% without SP. With SP, oil-in-water emulsions were observed for both water cuts. Kinetics of oil/water separation increased with the higher water cut; however, no clear tendency on water quality was observed. with water cut. In conclusion, we reconfirm that SP production, at least for the investigated formulations, will have a negligible effect on separation. The result will lead to deterioration in separated water quality; however, the level of deterioration is manageable and would not affect conventional practices of disposal in oilfields for pressure maintaining purposes. At last, this study layout laboratory protocols to perform such process-assurance.
Khafiz Muradov, Akindolu Dada, Sultan Djabbarov
Day 3 Wed, October 20, 2021; https://doi.org/10.2118/205145-ms

Abstract:
Pressure Transient Analysis (PTA) methodology has long enabled well testing to become a standard routine. Modern, well and reservoir monitoring and management practices are now unthinkable without the well test-derived estimates of KH products, skin factors, radii of reservoir boundaries, etc. Temperature data, measured together with the pressure, is widely available. Multiple methods for Temperature Transient Analysis (TTA) have also been developed, but have not yet gained due recognition. Few examples of a systematic application of PTA and TTA (or, in general, Pressure and Temperature Transient Analysis PTTA) on a field scale have been published. Given that the TTA radius of investigation is much smaller than that for PTA, the TTA tends to explore the near-wellbore properties including the near-wellbore permeability profile, depth of damage, multi-layer parameters, fluid properties, etc. This complements the far-field estimates made by PTA, resulting in the PTTA providing a more holistic and complete picture of the state of the reservoir and fluids around the wellbore. This work demonstrates a case study of a systematic application of PTTA methods to wells in a green, oil field. The wells are equipped with a state-of-the-art, downhole, permanent monitoring equipment. A user-friendly, bespoke toolbox has been developed to carry out PTTA analysis in this field. Dozens of transient events that occurred in the first few years of the field production life have been analyzed using PTTA. There are multiple examples of this PTTA analysis demonstrating improved characterization of the reservoir, near-wellbore, fluid, and multi-layer properties. This work will be insightful to those looking to find out what additional, useful information (like reservoir and fluid properties) can be extracted from the traditional well-test, transient pressure and temperature measurements at no extra cost.
Cheng Jie Cheng, Bo Hai Liu, Qian Gao, Wei Yan, Lin Wen Chen, Chao Guo Liu, Quan Zhou, Hui Pei Han
Day 4 Thu, October 21, 2021; https://doi.org/10.2118/205164-ms

Abstract:
The blocks of polymer flooding have gradually entered into the stage of chase water flooding after polymer flooding in Daqing Oilfield and the comprehensive water cut is close to the exploitation limit of 98%. So it is urgent to develop some new technologies to further enhance oil recovery after polymer flooding. On the basis of laboratory research, a field test of alkali/surfactant/polymer flooding was carried out after polymer flooding in Daqing Oilfield in 2015, which achieved good development effect, but the polymer concentration was relatively large. Based on the field test of alkali/surfactant/polymer flooding, a new technology of the lower initial viscosity gel/alkali/surfactant/polymer flooding has further been studied according to the technical route combining plugging, adjusting and displacing, which can reduce the polymer dosage greatly under the premise of ensuring good oil displacing effect. In this paper, some laboratory studies are carried out, which realize significant technology breakthrough. Firstly, the adjusting and plugging agent of lower initial viscosity gel is screened out, which can be injected into the high permeability layers of low flow resistance like the water and migrate to the deep location of the high permeability layers and then gelatinize on spot. Therefore it can plug high permeability layers effectively and does not pollute the middle and low permeability layers at the same time. Secondly, the injection parameters of lower initial viscosity gel/alkali/surfactant/polymer system are optimized. The results of laboratory experiments show that the lower initial viscosity gel/alkali/surfactant/polymer system can enhance oil recovery by 13.5% OOIP under the optimal injection parameters, which is 1.2% OOIP more than that of the alkali/surfactant/polymer flooding and can save polymer dosage by 17.8%. In view of the good results obtained in laboratory experiments, the scenario design of field test is carried out and the incremental oil recovery is 10.1% OOIP predicted by numerical simulation. The field test is expected to start chemical flooding in 2021.
Mingyuan Wang, Gayan A. Abeykoon, Francisco J. Argüelles-Vivas, Ryosuke Okuno
Day 4 Thu, October 21, 2021; https://doi.org/10.2118/205154-ms

Abstract:
This paper presents an experimental study of improved oil recovery from fractured shale cores by huff-n-puff of the aqueous solutions of 3-pentanone. The huff-n-puff experiments with different 3-pentanone concentrations were analyzed by the material balance for components: oil, brine, and 3-pentanone. Naturally sulfate-rich brine of low salinity was used as the injection brine. Results show that the 3-pentanone solution recovered more oil from the shale matrix than the injection brine alone. The oil recovery increased when the 3-pentanone concentration increased from 0.56-wt% to 2.85-wt%. Huff-n-puff with the 2.85-wt% 3-pentanone solution showed the highest improved oil recovery by 3-pentanone. However, the huff-n-puff experiment with the 1.07-wt% 3-pentanone solution showed the highest efficiency measured by the mass ratio of the produced oil to the injected 3-pentanone. That is, an optimal concentration of 3-pentanone appeared to exist. The material balance analysis showed that 3-pentanone was efficiently imbibed into the shale matrix, and that oil was recovered from shale mainly by the displacement by brine after the wettability alteration by 3-pentanone.
Ruslan Kalabayev, Ekaterina Sukhova, Gadam Rovshenov, Roman Kontarev
Day 4 Thu, October 21, 2021; https://doi.org/10.2118/205123-ms

Abstract:
Successful sandstone matrix stimulation treatments require addressing complex mineralogy, correctly identifying formation damage, selecting the best stimulation fluids, and placing these fluids correctly. The objective of this paper is to demonstrate a workflow considering laboratory testing, advanced software modeling including acid and diverter fluid efficiency calibration using field experimental data, field execution, and relevant case studies in two oil fields located in the Cheleken block, offshore Caspian Sea. Implementation of the workflow has led to positive results. Matrix acidizing was selected as the primary method for restoring production of the oil wells drilled into sandstone reservoirs due to the reservoir characteristics. Deep Zhdanov wells and shallower Lam wells possess ~15 and ~250 md permeability and ~90 and ~50°C static reservoir temperature, respectively. The target rock mineralogy in both fields predominantly consists of quartz, chlorite, and carbonate minerals. Fluids selection, stimulation design and job execution followed the above mentioned workflow. Treatment modeling considered calibration factors derived from field testing and incorporated several acid and diverter systems. A mix of bullhead and coiled tubing placed treatments were employed. The first step of the workflow considered characterization of the rock mineralogy and selection of the best-fit treatment fluids. Rock dissolution and X-ray diffraction (XRD) tests were run to develop the optimum formulations for the treatment conditions. Further, the results of the laboratory testing were incorporated into the advanced matrix acidizing simulator to model and optimize the treatment schedules. The recently developed matrix stimulation software incorporates geochemical, thermal, and placement simulations calibrated with experimental data. Offset well stimulation treatment pressure match was done by calibrating the acid and diverter fluid efficiency, and those calibrated values were considered for design simulations for the following acid treatments. In this paper, the term "acid efficiency" is defined as a measure of the relative rate at which the acid can penetrate when it flows in the rock matrix as a function of matrix porosity and the overall acid reactivity. The term "diverter efficiency" is defined as a measure of the viscosity developed by a given diverter when it flows in the rock matrix. Such a calibration method accounts for the actual reservoir large-scale acid-rock reaction kinetics. Finally, diagnostic tests and main acid treatments were executed that enabled achieving the desired levels of skin reduction, reservoir placement, zone coverage, and hydrocarbon production rates. Several acid stimulation operations were conducted including three cases in which a low-temperature well with carbonate damage needed repeated acidizing and two additional cases that involved wells with deep, hot, and clay-rich pay zones. Several fluid schedules were applied including foam diversion technique. The above approach uses a unique method of acid efficiency calibration using field experimental data. It requires good knowledge of reservoir rock mineralogy, porosity, and permeability profiles in the zones of interest. Pretreatment skin is calibrated using production data prior to acid efficiency calibration based on matching the actual treatment pressures. The pressure behavior observed during the following treatments closely matched the design pressures confirming applicability of the approach.
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