Evaluation of the Total Organic Carbon (TOC) Using Different Artificial Intelligence Techniques
Open Access
- 13 October 2019
- journal article
- research article
- Published by MDPI AG in Sustainability
- Vol. 11 (20), 5643
- https://doi.org/10.3390/su11205643
Abstract
Total organic carbon (TOC) is an essential parameter used in unconventional shale resources evaluation. Current methods that are used for TOC estimation are based, either on conducting time-consuming laboratory experiments, or on using empirical correlations developed for specific formations. In this study, four artificial intelligence (AI) models were developed to estimate the TOC using conventional well logs of deep resistivity, gamma-ray, sonic transit time, and bulk density. These models were developed based on the Takagi-Sugeno-Kang fuzzy interference system (TSK-FIS), Mamdani fuzzy interference system (M-FIS), functional neural network (FNN), and support vector machine (SVM). Over 800 data points of the conventional well logs and core data collected from Barnett shale were used to train and test the AI models. The optimized AI models were validated using unseen data from Devonian shale. The developed AI models showed accurate predictability of TOC in both Barnett and Devonian shale. FNN model overperformed others in estimating TOC for the validation data with average absolute percentage error (AAPE) and correlation coefficient (R) of 12.02%, and 0.879, respectively, followed by M-FIS and SVM, while TSK-FIS model showed the lowest predictability of TOC, with AAPE of 15.62% and R of 0.832. All AI models overperformed Wang models, which have recently developed to evaluate the TOC for Devonian formation.This publication has 31 references indexed in Scilit:
- Application of Real-Time Field Data to Optimize Drilling Hydraulics Using Neural Network ApproachJournal of Energy Resources Technology, 2015
- Critical considerations when assessing hydrocarbon plays using Rock-Eval pyrolysis and organic petrology data: Data quality revisitedInternational Journal of Coal Geology, 2015
- Artificial dispersion via high-order homogenization: magnetoelectric coupling and magnetism from dielectric layersProceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 2013
- Quantitative evaluation of TOC, organic porosity and gas retention distribution in a gas shale play using petroleum system modeling: Application to the Mississippian Barnett ShaleMarine and Petroleum Geology, 2013
- Effect of organic-matter type and thermal maturity on methane adsorption in shale-gas systemsOrganic Geochemistry, 2012
- Casing collapse risk assessment and depth prediction with a neural network system approachJournal of Petroleum Science and Engineering, 2009
- Impact of mass balance calculations on adsorption capacities in microporous shale gas reservoirsFuel, 2007
- Geologic framework of the Mississippian Barnett Shale, Barnett-Paleozoic total petroleum system, Bend arch–Fort Worth Basin, TexasAAPG Bulletin, 2007
- URANIUM SPECTRAL GAMMA‐RAY RESPONSE AS A PROXY FOR ORGANIC RICHNESS IN BLACK SHALES: APPLICABILITY AND LIMITATIONSJournal of Petroleum Geology, 2003
- Determination of Organic Content of Appalachian Devonian Shales from Formation-Density Logs: GEOLOGIC NOTESAAPG Bulletin, 1979