Enhancing Reservoir Simulation History Matching Using SHAP Value for Parameter Range Selection.
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| Title: | Enhancing Reservoir Simulation History Matching Using SHAP Value for Parameter Range Selection. |
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| Authors: | Eremyan, Grachik1 (AUTHOR), Magdeev, Adel M.1 (AUTHOR), Al-Shargabi, Mohammed1 (AUTHOR), Matveev, Ivan V.1 (AUTHOR), Smirnov, Ivan E.1 (AUTHOR), Shishaev, Gleb Y.1 (AUTHOR), Davoodi, Shadfar1 (AUTHOR) davoodis@hw.tpu.ru |
| Source: | Energies (19961073). May2026, Vol. 19 Issue 10, p2285. 34p. |
| Subject Terms: | *Shapley Additive Explanations, *Parameter estimation, *Optimization algorithms, *Sensitivity analysis, *Particle swarm optimization, *Machine learning, *Oil reservoir engineering |
| Abstract: | Accurate reservoir history matching is indispensable for reliable subsurface forecasting and optimal field development; however, it remains a formidable challenge due to the high dimensionality of geological parameter spaces, strong nonlinear dynamics, and the competing demands of computational efficiency and data fidelity. Conventional automated history matching workflows frequently rely on Tornado sensitivity analysis, which evaluates parameters in isolation and fails to capture critical interdependencies that govern reservoir response, leading to inefficient exploration and suboptimal convergence. This study, therefore, introduces a novel Shapley Additive Explanations (SHAP)-assisted adaptive history matching framework that integrates SHAP with a CatBoost (v1.0.0) regressor to enable quantitative, model-agnostic assessment of parameter contributions while explicitly resolving non-linear feature interactions unattainable with conventional methods. The workflow establishes a systematic methodology for refining parameter uncertainty ranges prior to optimization through SHAP dependence plots, which identify subspaces where the objective function decreases, yielding 25–68% range reductions across all cases without compromising geological plausibility. Both SHAP-assisted and Tornado-based workflows, coupled with identical Particle Swarm Optimization settings, are rigorously validated on two synthetic reservoirs (SRM-6, Egg) and a real Siberian field case. Results demonstrate that the SHAP-assisted approach achieves equivalent or superior history match quality while reducing required optimization cycles by 6–60%, depending on model complexity, delivering lower objective function values and improved alignment with observed data. Computational efficiency is further enhanced by reusing the Latin Hypercube Sampling dataset for surrogate training, sensitivity analysis, and range refinement, contrasting sharply with Tornado's requirement for 2N + 1 dedicated simulation runs with limited reusability. This framework advances interpretable, data-driven history matching by providing engineers with a systematic method to prioritize calibration efforts, reduce computational burden, and improve forecast reliability in complex reservoir systems. [ABSTRACT FROM AUTHOR] |
| Database: | Energy & Power Source |
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| Abstract: | Accurate reservoir history matching is indispensable for reliable subsurface forecasting and optimal field development; however, it remains a formidable challenge due to the high dimensionality of geological parameter spaces, strong nonlinear dynamics, and the competing demands of computational efficiency and data fidelity. Conventional automated history matching workflows frequently rely on Tornado sensitivity analysis, which evaluates parameters in isolation and fails to capture critical interdependencies that govern reservoir response, leading to inefficient exploration and suboptimal convergence. This study, therefore, introduces a novel Shapley Additive Explanations (SHAP)-assisted adaptive history matching framework that integrates SHAP with a CatBoost (v1.0.0) regressor to enable quantitative, model-agnostic assessment of parameter contributions while explicitly resolving non-linear feature interactions unattainable with conventional methods. The workflow establishes a systematic methodology for refining parameter uncertainty ranges prior to optimization through SHAP dependence plots, which identify subspaces where the objective function decreases, yielding 25–68% range reductions across all cases without compromising geological plausibility. Both SHAP-assisted and Tornado-based workflows, coupled with identical Particle Swarm Optimization settings, are rigorously validated on two synthetic reservoirs (SRM-6, Egg) and a real Siberian field case. Results demonstrate that the SHAP-assisted approach achieves equivalent or superior history match quality while reducing required optimization cycles by 6–60%, depending on model complexity, delivering lower objective function values and improved alignment with observed data. Computational efficiency is further enhanced by reusing the Latin Hypercube Sampling dataset for surrogate training, sensitivity analysis, and range refinement, contrasting sharply with Tornado's requirement for 2N + 1 dedicated simulation runs with limited reusability. This framework advances interpretable, data-driven history matching by providing engineers with a systematic method to prioritize calibration efforts, reduce computational burden, and improve forecast reliability in complex reservoir systems. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 19961073 |
| DOI: | 10.3390/en19102285 |
