Author:
Ashraf Umar,Anees Aqsa,Zhang Hucai,Ali Muhammad,Thanh Hung Vo,Yuan Yujie
Abstract
AbstractThe oil and gas industry relies on accurately predicting profitable clusters in subsurface formations for geophysical reservoir analysis. It is challenging to predict payable clusters in complicated geological settings like the Lower Indus Basin, Pakistan. In complex, high-dimensional heterogeneous geological settings, traditional statistical methods seldom provide correct results. Therefore, this paper introduces a robust unsupervised AI strategy designed to identify and classify profitable zones using self-organizing maps (SOM) and K-means clustering techniques. Results of SOM and K-means clustering provided the reservoir potentials of six depositional facies types (MBSD, DCSD, MBSMD, SSiCL, SMDFM, MBSh) based on cluster distributions. The depositional facies MBSD and DCSD exhibited high similarity and achieved a maximum effective porosity (PHIE) value of ≥ 15%, indicating good reservoir rock typing (RRT) features. The density-based spatial clustering of applications with noise (DBSCAN) showed minimum outliers through meta cluster attributes and confirmed the reliability of the generated cluster results. Shapley Additive Explanations (SHAP) model identified PHIE as the most significant parameter and was beneficial in identifying payable and non-payable clustering zones. Additionally, this strategy highlights the importance of unsupervised AI in managing profitable cluster distribution across various geological formations, going beyond simple reservoir characterization.
Funder
National Natural Science Foundation of China
Publisher
Springer Science and Business Media LLC
Reference72 articles.
1. Abid M, Geng J (2020) Effective attributes quantification to bridge gap between elastic properties and reservoir parameters in self-resource rocks. Sci Rep 10:2534
2. Ahmad N, Fink P, Sturrock S, Mahmood T, Ibrahim M (2004) Sequence stratigraphy as predictive tool in lower goru fairway, lower and middle Indus platform, Pakistan. PAPG, ATC 1:85–104
3. Akbar MNA, Nugraha ST (2018) K-mean cluster analysis for better determining the sweet spot intervals of the unconventional organic-rich shale: a case study. Contemp Trends Geosci 7:200–213
4. Ali J, Ashraf U, Anees A, Peng S, Umar MU, Vo Thanh H, Khan U, Abioui M, Mangi HN, Ali M (2022a) Hydrocarbon potential assessment of carbonate-bearing sediments in a meyal oil field, Pakistan: Insights from logging data using machine learning and quanti elan modeling. ACS Omega 7:39375–39395
5. Ali M, Zhu P, Jiang R, Huolin M, Ashraf U (2024a) Improved prediction of thin reservoirs in complex structural regions using post-stack seismic waveform inversion: a case study in the Junggar Basin. Can Geotech J. https://doi.org/10.1139/cgj-2023-0384
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