Optimization of Tracer Injection Schemes for Improved History Matching
Published: 15 September 2021
Abstract: Interwell tracers are powerful reservoir surveillance tools that provide direct reservoir flow paths and dynamics, which, when integrated with near real-time production optimization, can greatly improve recovery factor, and return on investment, the so-called "Advanced Tracers System" (ATS). Applying full field ATS is attractive for resource-holders, especially for those with large waterflood operations. However, to scale up ATS to cover large fields with potentially tens to hundreds of injectors and producers, the required unique tracer variations ("barcodes") and materials and associated analysis may increase rapidly. Here, we explore different tracer injection schemes that can acquire the most information while using reduced numbers of tracers, thereby controlling costs in field operations. We tested the designs of various modified tracer injection schemes with reservoir simulations. Numerical experiments were performed on synthetic fields with multiple injector and producer wells in waterflooding patterns. Two tracer injection schemes were considered: In Scheme 1, all injectors were injected with unique tracers representing the most information-rich case. In Scheme 2, some injectors were injected with the same tracers ("recycling" the same barcodes), and some injectors received no tracer injection ("null" barcodes). Production and tracer breakthrough data was collected for history matching after waterflooding simulations on the synthetic fields. The ensemble smoother with multiple data assimilation with tracers algorithm was used for history matching. We calculated the root-mean-square errors (RMSE) between the reference data and the history matched production simulation data. To improve the statistics, 20 independent testing reference synthetic fields were constructed by randomizing the number and locations of high permeability zones crossing different injectors and producers. In all cases, the history matching algorithms largely reduced the RMSE thereby enhancing reservoir characterization. Analyzing the statistical significance with p-values among testing cases, first, as expected, the data mismatch is highly significantly lower after history matching than before history matching (p < 0.001). Second, the data mismatch is even lower when history matching with tracers (both in Scheme 1 and 2) than without tracers (p < 0.05), demonstrating clearly that tracers can provide extra information for the reservoir dynamics. Finally, and most importantly, history matching with tracers in Scheme 1 or in Scheme 2 result in statistically the same data mismatch (p > 0.05), indicating the cost-saving "recycling" and "null" tracer barcodes can provide equally competent reservoir information. To the best of our knowledge, this is the first study that evaluated the history matching qualities deriving from different tracer injection schemes. We showed that through optimal designs of the tracer injections, we can acquire very similar information with reduced tracer materials and barcodes, thus reducing costs and field operational complexities. We believe this study facilitates the deployment of large-scale reservoir monitoring and optimization campaigns using tracers such as ATS.
Keywords: modeling & simulation / upstream oil & gas / barcode / chemical tracer / tracer test analysis / data mismatch / reservoir simulation / injector / tracer / scheme 1
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