Pix2Geomodel: A Next-Generation Reservoir Geomodeling with Property-to-Property Translation
Abdulrahman Al-Fakih, Ardiansyah Koeshidayatullah, Nabil A. Saraih, Tapan Mukerji, Rayan Kanfar, Abdulmohsen Alali, SanLinn I. Kaka

TL;DR
Pix2Geomodel introduces a novel AI framework using cGANs to accurately predict and translate reservoir properties from complex subsurface data, improving geological modeling fidelity for reservoir management.
Contribution
The paper presents a new cGAN-based framework, Pix2Geomodel, for reservoir property prediction and translation, demonstrating high accuracy and spatial realism in complex geological settings.
Findings
High accuracy in facies and water saturation predictions
Effective property-to-property translation performance
Captured spatial variability and geological realism
Abstract
Accurate geological modeling is critical for reservoir characterization, yet traditional methods struggle with complex subsurface heterogeneity, and they have problems with conditioning to observed data. This study introduces Pix2Geomodel, a novel conditional generative adversarial network (cGAN) framework based on Pix2Pix, designed to predict reservoir properties (facies, porosity, permeability, and water saturation) from the Rotliegend reservoir of the Groningen gas field. Utilizing a 7.6 million-cell dataset from the Nederlandse Aardolie Maatschappij, accessed via EPOS-NL, the methodology included data preprocessing, augmentation to generate 2,350 images per property, and training with a U-Net generator and PatchGAN discriminator over 19,000 steps. Evaluation metrics include pixel accuracy (PA), mean intersection over union (mIoU), frequency weighted intersection over union (FWIoU),…
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