Reservoir characterization

Physics-Informed Machine Learning Enhances Permeability Prediction in Carbonate Reservoirs

This study integrates physics-based constraints into machine-learning models, thereby improving their predictive accuracy and robustness.

Fig. 1—Workflow for the developed PIML permeability-prediction methodology.
Fig. 1—Workflow for the developed PIML permeability-prediction methodology.
Source: OTC 35892.

Physics-informed machine-learning (PIML) techniques enhance permeability prediction in carbonate reservoirs. Accurate permeability estimation is crucial for reservoir characterization, fluid-flow modeling, and oil and gas production optimization. However, traditional empirical models and conventional ML techniques often fail to capture the complex nonlinear relationships governing permeability, particularly in heterogeneous carbonate formations. To address this challenge, the authors of this paper integrate physics-based constraints into ML models, thereby improving predictive accuracy and robustness.

×
SPE_logo_CMYK_trans_sm.png
Continue Reading with SPE Membership
SPE Members: Please sign in at the top of the page for access to this member-exclusive content. If you are not a member and you find JPT content valuable, we encourage you to become a part of the SPE member community to gain full access.