Novel Approach for Machine Learning Assisted Carbonates Reservoirs Saturation Height Modeling and Automated Pore Network Characterization
Authors:   ADNOC Upstream, Gerges Nader , Barillas Cortez Luisa Ana ,   SLB, Madhavan Midhun ,   SLB, Kuruba Sushmita ,   SLB, Almarzooqi Lulwa ,   SLB, Makarychev Gennady ,   SLB, Maarouf Alaa ,   SLB, Darous Christophe ,   SLB, Mustapha Hussein ,   SLB, Kloucha Chakib Kada ,   ADNOC
Published: November 2024

Key data sets for permeability modeling, rock typing, reservoir characterization, and saturation height modeling (SHM) include capillary pressure (Pc) measurements and conventional core analysis (CCA). The Pc data QA/QC and modeling are time consuming and sensitive to the intricate pore network in Carbonate rocks. In this study, we automate the Pc-based pore network characterization and use machine learning (ML) for capillary pressure modeling in an effort to improve workflow efficiency and lessen interpretation bias. Additionally, a framework for advanced analytics was developed to enable interactive quality control and user visualization. We developed and tested the proposed algorithms on real data from a large carbonate oil field in the Middle East. There are around 500 MICP samples of good to exceptional quality. The parent-plug porosity and permeability are also available for all samples. The suggested methodology considerably enhances the Pc-based pore network characterization workflow, according to preliminary testing. We can automatically identify low-quality data and outlier samples using sophisticated data science outliers’ identification methods. These outlier samples are removed, eliminating data-quality artifacts in the interpretation. The automated pore network characterization method makes it simple to comprehend the porosity modality and partition. The petrophysical grouping at the MICP level is built, honoring this characterization. We provide a thorough comparison of the outcomes of standard approaches to petrophysical rock type with the outcomes attained utilizing the suggested pore network characterization methodology. Although the focus of this study is to improve the petrophysical grouping component of the rock typing, further integration with geology and diagenetic overprints is required to improve the overall workflow. We use ML methods to compute the final saturation height functions and automatically create the optimum Pc analytical model. Compared to the currently available conventional methodologies in commercial petrophysical software, this unique workflow enables more complex mathematical modeling. The overall results provide an enhanced mathematical solution limited by the reservoir physical constrains. Traditional techniques have been used in the petrophysical approach to core-based saturation modeling for several years. The newest developments in data science workflows are utilized in this novel method to QA/QC Pc and core data, characterize the reservoir, and build models. The suggested methodology provides a considerable increase in terms of efficiency as well as in the characterization of carbonate reservoirs and the accuracy of saturation modeling. With the new information extracted from the Pc arrays, more representative rock-typing schemes are defined, which will greatly improve the saturation modeling workflows and 3D volumetric calculations using advanced ML techniques.