A dynamic solvent chamber propagation estimation framework using RNN for warm solvent injection in heterogeneous reservoirs

Journal Article · 11 October 2024 · Geoenergy Science and Engineering

DOI: https://doi.org/10.1016/j.geoen.2024.213405 · OSTI ID:2472590

Ma, Zhiwei  ^[1]; Yuan, Qingwang ^[2]; Xu, Ziming  ^[3]; Leung, Juliana Y.  ^[3]

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1. Los Alamos National Laboratory (LANL), Los Alamos, NM (United States); Univ. of Alberta, Edmonton, AB (Canada)
2. Texas Tech Univ., Lubbock, TX (United States)
3. Univ. of Alberta, Edmonton, AB (Canada)

Warm solvent injection (WSI), injecting low-temperature solvent into formations to reduce the viscosity of heavy oil, is a clean technology for heavy oil production through reducing greenhouse gas emissions and water usage. The success of WSI operation depends on the uniform development and propagation of solvent chambers in reservoirs. However, reservoir heterogeneity stemming from shale barriers plays a detrimental role in the conformance of solvent chamber development and oil production rate. In this work, we developed a novel recurrent neural network (RNN)-based framework with the capability of efficiently tracking and estimating the solvent chamber positions in heterogeneous reservoirs based on only production time-series data. The developed estimation model utilizes the “sequence-to-sequence" mapping methodology to correlate observed production time-series sequence and solvent chamber edge sequence via a long short-term memory (LSTM) algorithm. The trained RNN models exhibit high accuracy, evidenced by the predicted dynamic solvent chamber locations match the corresponding true locations from numerical simulation, with a high coefficient of determination (R²) and a low mean squared error. Specifically, the achieved R² values exceed 0.98 on both the training and testing data. The developed RNN-based workflow was tested via several cases from both regularly- and irregularly-shaped shale barriers, and the results were promising. The predicted solvent chambers showed strong agreement with those obtained from numerical simulations. The major benefits of this workflow include reducing computational time and saving overall monitoring and tracking costs for conventional techniques. In conclusion, the present work would provide a good demonstration of the capability of practical integration of machine learning methods in solving engineering problems.

• Research Organization: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
• Sponsoring Organization: USDOE
• Grant/Contract Number: 89233218CNA000001
• OSTI ID: 2472590
• Report Number(s): LA-UR--24-24100
• Journal Information: Geoenergy Science and Engineering, Journal Name: Geoenergy Science and Engineering Vol. 244; ISSN 2949-8910
• Publisher: ElsevierCopyright Statement
• Country of Publication: United States
• Language: English