Performance of water injection and CO۲ injection into oil reservoirs based on field data: using ANNs to predict in the selected scenario
محل انتشار: مجله علوم و فن آوری نفت، دوره: 12، شماره: 35
سال انتشار: 1401
نوع سند: مقاله ژورنالی
زبان: انگلیسی
مشاهده: 27
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شناسه ملی سند علمی:
JR_JPSTR-12-35_001
تاریخ نمایه سازی: 30 آذر 1402
چکیده مقاله:
As carbon dioxide emissions rise worldwide, the world is still experiencing many consequences of these emissions. This challenge can be addressed using carbon capture, utilization, and storage (CCUS). Energy transfer generally requires a good program in which CCUS plays a crucial role. CO۲-EOR, which allows for storing carbon dioxide (CO۲), is a suitable option in this area. It provides economic returns from oil that could not be recovered before without this method and has environmental benefits, which shows its importance compared to other EOR methods. In this study, an oil reservoir is simulated using field data to compare this method with the water injection method and natural depletion method of the reservoir. Water and CO۲ injection increased oil recovery by ۸.۴% and ۱۲.۷%, compared to natural depletion. The surrogate reservoir model was built using the machine learning (ML) technique by choosing the scenario of CO۲ injection to reduce the computational load and the possibility of using it in optimization tasks. Therefore, using the data-driven model, we can reproduce the data related to the CO۲-EOR process in a much shorter period of time, thereby allowing us to select the most efficient parameters and their ranges for different processes. The numerical simulator was run ۲۵۰ times to extract the necessary data. The ANN is applied to the data and trained after the database is built and the hyper-parameters have been optimized. ANN consists of two hidden layers with ۸۱ and ۵۱ neurons, respectively, and a ۰.۰۵ learning rate after optimization. The trained two-objective ANN was a MAPE of less than ۲.۵% in the test data for both objectives, i.e., oil recovery and carbon dioxide storage. To further validate and ensure the accuracy of the trained ANN, the numerical simulator was run randomly ten times and compared with the values predicted by the ANN. MAPE for both objectives was less than ۲.۶%. Therefore, the ANN that makes predictions in a fraction of a second has a suitable accuracy that can be used as a surrogate reservoir model.
کلیدواژه ها:
Enhanced Oil Recovery ، CO۲-EOR ، Simulation ، Oil Reservoirs ، Artificial Neural Networks (ANNs) ، Water Injection
نویسندگان
Bardiya Yazdani
Petroleum Engineering Department, Faculty of Chemical Engineering, Tarbiat Modares University, Tehran, Iran
Pouya Vaziri
Institute of Petroleum Engineering, Department of Chemical Engineering, College of Engineering, University of Tehran, Tehran, Iran
Behnam Sedaee Sola
Institute of Petroleum Engineering, Department of Chemical Engineering, College of Engineering, University of Tehran, Tehran, Iran
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