Model Development for Brittleness Index Estimation and Depth Determination in Hydraulic Fracturing Operations in Shale Gas Reservoirs Using Machine Learning Algorithms DOI
Grant Charles Mwakipunda,

Norga Alloyce Komba,

Edwin Twum Ayimadu

и другие.

SPE Journal, Год журнала: 2025, Номер unknown, С. 1 - 22

Опубликована: Май 1, 2025

Summary Accurate estimation of the brittleness index (BI) is critical for optimizing hydraulic fracturing operations in shale gas reservoirs, as it directly influences fracture propagation and recovery efficiency. The BI quantifies resistance rock to fracturing, a key factor determining optimal depth stimulation. Prior methods estimating BI, such empirical correlations other utilized machine learning (ML) techniques, often suffer from limited accuracy generalizability, particularly complex geological formations like Fuling field. To address these limitations, ML techniques have gained prominence due their ability capture complex, nonlinear relationships within large data sets, improving predictive accuracy. In this study, we propose novel approach that utilizes hybrid group method handling based on discrete differential evolution (GMDH-DDE) predict BI. GMDH-DDE model was compared with (GMDH), random forest (RF), multilayer perceptron (MLP). results demonstrate significantly outperforms models, achieving coefficient determination (R2) 0.9984, root mean square error (RMSE) 0.2895, absolute (MAE) 0.02543 unseen data. GMDH ranked second estimation, an R2 0.9805, RMSE 0.4635, MAE 0.04224. It followed by RF model, 0.9599, 0.6034, 0.0997. MLP however, had lowest performance, 0.9263, 0.9566, 0.1256. Additionally, demonstrates superior computational efficiency, requiring only 1.12 seconds. This significant advantage over methods, taking 4.82 seconds, 11.23 27.45 These findings highlight potential providing accurate computationally efficient estimations. improved efficiency are expected contribute more effective cost-efficient operations, ultimately enhancing economic viability reservoirs.

Язык: Английский

A novel hybrid machine learning and explainable artificial intelligence approaches for improved source rock prediction and hydrocarbon potential in the Mandawa Basin, SE Tanzania DOI
Christopher N. Mkono, Chuanbo Shen,

Alvin K. Mulashani

и другие.

International Journal of Coal Geology, Год журнала: 2025, Номер unknown, С. 104699 - 104699

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

2
Enhancing reservoir characterization: A novel machine learning approach for automated detection and reconstruction of outliers-affected well log curves DOI
Wakeel Hussain, Miao Luo, Muhammad Ali

и другие.

Physics of Fluids, Год журнала: 2025, Номер 37(3)

Опубликована: Март 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.

Язык: Английский

Процитировано

2
A novel hybrid group method of data handling and Levenberg Marquardt model for estimating total organic carbon in source rocks with explainable artificial intelligence DOI
Christopher N. Mkono, Chuanbo Shen,

Alvin K. Mulashani

и другие.

Engineering Applications of Artificial Intelligence, Год журнала: 2025, Номер 144, С. 110137 - 110137

Опубликована: Янв. 27, 2025

Язык: Английский

Процитировано

1
Improved Reservoir Porosity Estimation Using an Enhanced Group Method of Data Handling with Differential Evolution Model and Explainable Artificial Intelligence DOI
Christopher N. Mkono, Chuanbo Shen,

Alvin K. Mulashani

и другие.

SPE Journal, Год журнала: 2025, Номер unknown, С. 1 - 19

Опубликована: Фев. 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.

Язык: Английский

Процитировано

1
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 DOI

Dehao Feng,

Chenglin Liu, Jixian Tian

и другие.

Natural Resources Research, Год журнала: 2025, Номер unknown

Опубликована: Апрель 27, 2025

Язык: Английский

Процитировано

0
Model Development for Brittleness Index Estimation and Depth Determination in Hydraulic Fracturing Operations in Shale Gas Reservoirs Using Machine Learning Algorithms DOI
Grant Charles Mwakipunda,

Norga Alloyce Komba,

Edwin Twum Ayimadu

и другие.

SPE Journal, Год журнала: 2025, Номер unknown, С. 1 - 22

Опубликована: Май 1, 2025

Summary Accurate estimation of the brittleness index (BI) is critical for optimizing hydraulic fracturing operations in shale gas reservoirs, as it directly influences fracture propagation and recovery efficiency. The BI quantifies resistance rock to fracturing, a key factor determining optimal depth stimulation. Prior methods estimating BI, such empirical correlations other utilized machine learning (ML) techniques, often suffer from limited accuracy generalizability, particularly complex geological formations like Fuling field. To address these limitations, ML techniques have gained prominence due their ability capture complex, nonlinear relationships within large data sets, improving predictive accuracy. In this study, we propose novel approach that utilizes hybrid group method handling based on discrete differential evolution (GMDH-DDE) predict BI. GMDH-DDE model was compared with (GMDH), random forest (RF), multilayer perceptron (MLP). results demonstrate significantly outperforms models, achieving coefficient determination (R2) 0.9984, root mean square error (RMSE) 0.2895, absolute (MAE) 0.02543 unseen data. GMDH ranked second estimation, an R2 0.9805, RMSE 0.4635, MAE 0.04224. It followed by RF model, 0.9599, 0.6034, 0.0997. MLP however, had lowest performance, 0.9263, 0.9566, 0.1256. Additionally, demonstrates superior computational efficiency, requiring only 1.12 seconds. This significant advantage over methods, taking 4.82 seconds, 11.23 27.45 These findings highlight potential providing accurate computationally efficient estimations. improved efficiency are expected contribute more effective cost-efficient operations, ultimately enhancing economic viability reservoirs.

Язык: Английский

Процитировано

0