SPE Journal, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 27

Published: April 1, 2025

Summary Accurate prediction of porosity and permeability in fractured vuggy carbonate reservoirs is crucial for optimizing hydrocarbon recovery but remains challenging due to their extreme heterogeneity anisotropy. Traditional methods often struggle capture the complex geological variability, leading suboptimal reservoir characterization. To address this, we propose a novel hybrid machine learning (ML) framework that integrates particle swarm optimization (PSO), mixed-effects random forest (MERF), ensemble models, such as light gradient boosting (LightGBM), (XGBoost), (RF). These models were trained validated using leave-one well-out cross-validation (LOO-CV) train-test split method, leveraging geophysical well-log data from Tarim Basin’s reservoirs. Among three PSO-MERF-LightGBM outperformed others, achieving an R² 0.9752 root mean square error (RMSE) 0.0606 R2 0.9983 RMSE 0.00473 during testing. Moreover, model demonstrates exceptional computational efficiency, completing processing just 11 seconds 9 seconds, respectively. This marks significant reduction computation time compared with other making it highly efficient alternative. results confirm its superior ability nonlinear relationships spatial variability. The study how advanced ML techniques can enhance characterization, improving decision-making subsurface resource management. Future research should extend this settings validate broader applicability.

Language: Английский