A novel hybrid group method of data handling and Levenberg Marquardt model for estimating total organic carbon in source rocks with explainable artificial intelligence

Engineering Applications of Artificial Intelligence, Journal Year: 2025, Volume and Issue: 144, P. 110137 - 110137

Published: Jan. 27, 2025

Language: Английский

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A novel hybrid machine learning and explainable artificial intelligence approaches for improved source rock prediction and hydrocarbon potential in the Mandawa Basin, SE Tanzania

International Journal of Coal Geology, Journal Year: 2025, Volume and Issue: unknown, P. 104699 - 104699

Published: Jan. 1, 2025

Language: Английский

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Improved Reservoir Porosity Estimation Using an Enhanced Group Method of Data Handling with Differential Evolution Model and Explainable Artificial Intelligence

SPE Journal, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 19

Published: Feb. 1, 2025

Summary Reservoir characterization is critical to the oil and gas industry, influencing field development, production optimization, hydraulic fracturing, reserves estimation decisions. Accurately estimating porosity crucial for reservoir characterization, well planning, optimization in industry. Traditional determination methods, such as porosimetry, geostatistical, core analysis, often involve complex geological geophysical models, which are expensive time-consuming. This study used integrated machine learning model of differential evolution (DE) with group method data handling (GMDH-DE) estimate using log from Mpyo field, Uganda. The GMDH-DE demonstrates superior performance compared conventional GMDH, support vector regression (SVR), random forest (RF), achieving a coefficient (R2) 0.9925 root mean square error (RMSE) 0.0017 during training, an R² 0.9845 RMSE 0.0121 testing, when validated R2 was 0.9825 0.00018. A key novelty this work integration Shapley additive explanations (SHAP), provides interpretable analysis model’s input features. SHAP reveals that bulk density (RHOB) neutron (NPHI) most parameters estimation, offering valuable insight into features importance. proposed represent novel independent approach accurate interpretability, significantly enhancing efficiency reliability hydrocarbon exploration development.

Language: Английский

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Enhancing reservoir characterization: A novel machine learning approach for automated detection and reconstruction of outliers-affected well log curves

Physics of Fluids, Journal Year: 2025, Volume and Issue: 37(3)

Published: March 1, 2025

The drilling process can result in irregular measurements due to unconsolidated geological formations, affecting the accuracy of wireline logging devices. This impacts precision elastic log measurements, such as velocity and density profiles, which are essential for reservoir characterization. reliability wireline-logging tool is crucial preventing inaccuracies when assessing rock properties. Previous studies have focused on applying machine learning (ML) techniques logging, but these methods limited applicability, particularly outlier detection reconstruction. In response, this study integrates both supervised unsupervised ML enhance responses Initially, density-based spatial clustering applications with noise was applied detection, followed by feature selection identify correlated logs reconstructing log. A random forest regression model, optimized particle swarm optimization (PSO), then trained using selected features. comparative analysis showed a significant improvement porosity estimation from reconstructed compared core data. Specifically, comparison between original bulk yielded an R2 0.95 root mean squared error (RMSE) 0.012. contrast, rebuilt resulted 0.98 RMSE 0.007. integration advanced PSO-optimized models represents considerable advancement field approach enhances also saves time reduces manual effort, highlighting potential petroleum exploration production.

Language: Английский

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Key Controlling Factors of Hydrocarbon Accumulation of Fine-Grained Mixed Sequence in a Saline Lacustrine Basin: An Integrated Research of Petroleum System in the Northwestern Qaidam Basin, Qinghai–Tibet Plateau

Natural Resources Research, Journal Year: 2025, Volume and Issue: unknown

Published: April 27, 2025

Language: Английский

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