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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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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Permeability Prediction and Potential Site Assessment for CO₂ Storage from Core Data and Well-Log Data in Malay Basin Using Advanced Machine Learning Algorithms

ACS Omega, Journal Year: 2025, Volume and Issue: 10(6), P. 5430 - 5448

Published: Feb. 5, 2025

Establishing a potential site characterization for carbon dioxide (CO2) storage in geological formations anticipates the appropriate reservoir properties, such as porosity, permeability, and so forth. Well logs seismic data were utilized to determine key including volume of shale, water saturation. These properties cross validated with core sets ensure accuracy. To enhance permeability estimation, sophisticated machine learning (ML) methods employed, categorizing into five classes ranging from extremely good (0) very low (4). Two ML models, Naïve Bayes (NB) multilayer perceptron (MLP), applied predict permeability. The MLP model outperformed NB model, achieving 99% training accuracy 93% testing accuracy, compared 78 73%, respectively, model. resulting comprehensive revealed distribution across three stratigraphic layers: B100 zone exhibited suitable caprock, while D35-1 D35-2 zones demonstrated excellent indicating CO2 reservoirs. "X" field reservoir, located at depths exceeding 1300 m, meets depth requirements (1000–1500 m) storage. Our integrated approach, combining empirical ML-based calculations well logs, proved effective characterizing reservoir. lithological defined nonreservoir sections between clay silt lines, identifying important caprocks interbedded shale/clay intervals. Seismic profiling confirmed continuous caprock overlying D group zone, crucial preventing upward migration. This analysis supports Malay Basin viable storage, contributing ongoing efforts capture research.

Language: Английский

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Porosity prediction of tight reservoir rock using well logging data and machine learning

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: April 16, 2025

Language: Английский

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