Enhancing hydraulic fracturing efficiency through machine learning
Abstract Hydraulic fracturing (HF) is a technique employed in the oil and gas industry to extract hydrocarbon resources from shale formations and low-permeability rocks. This process involves the creation of new fractures and the extension of existing ones within the rock by injecting a high-pressur...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2025-01-01
|
| Series: | Journal of Petroleum Exploration and Production Technology |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s13202-024-01914-4 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1823863284194869248 |
|---|---|
| author | Ali Karami Ali Akbari Yousef Kazemzadeh Hamed Nikravesh |
| author_facet | Ali Karami Ali Akbari Yousef Kazemzadeh Hamed Nikravesh |
| author_sort | Ali Karami |
| collection | DOAJ |
| description | Abstract Hydraulic fracturing (HF) is a technique employed in the oil and gas industry to extract hydrocarbon resources from shale formations and low-permeability rocks. This process involves the creation of new fractures and the extension of existing ones within the rock by injecting a high-pressure fracturing fluid, which enhances the flow of hydrocarbons. To assess the efficiency and safety of HF, various factors such as fracture orientation, geometry, length, distribution, and stimulated reservoir volume are analyzed. In the complex field of HF, the accurate assessment of fracture parameters is crucial for optimizing operational efficiency and ensuring environmental safety. This study investigates the role of machine learning (ML) methodologies in enhancing the precision of these assessments, with a particular focus on fracture width a critical factor in the effectiveness of HF. By utilizing a comprehensive dataset from hydrocarbon fields, in this study, the application of several advanced ML models was investigated, including Artificial Neural Networks (ANNs), Random Forest (RF), and K-Nearest Neighbors (KNNs). These models were specifically employed to predict and assess fracture width based on a variety of geological and operational parameters: geometric factors (γ), Shear Modulus (G), Poisson’s ratio (ν), viscosity of the fracturing fluid (µ in centipoise), crack height (h), fluid efficiency (η), injection time (t), and crack length (x). This study evaluated the performance of ANN, RF, and KNN models, achieving accuracies of 0.978, 0.979, and 0.893, respectively, which underscores their strong predictive modeling capabilities. The results were meticulously documented for each methodology. The RMSE values for ANN, KNN, and RF were 7.552, 15.711, and 6.194, respectively. Notably, the RF approach demonstrated superior performance with an RMSE of 6.194, establishing it as the most accurate method. This study highlights the transformative potential of ML in the pre-stimulation planning phase of HF, enabling enhanced real-time decision-making and operational optimization, which ultimately results in more effective and efficient fracturing operations. |
| format | Article |
| id | doaj-art-8881243c2a514c7f8519139d434f3ed6 |
| institution | Kabale University |
| issn | 2190-0558 2190-0566 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Petroleum Exploration and Production Technology |
| spelling | doaj-art-8881243c2a514c7f8519139d434f3ed62025-02-09T12:13:19ZengSpringerOpenJournal of Petroleum Exploration and Production Technology2190-05582190-05662025-01-0115111610.1007/s13202-024-01914-4Enhancing hydraulic fracturing efficiency through machine learningAli Karami0Ali Akbari1Yousef Kazemzadeh2Hamed Nikravesh3Department of Petroleum EngineeringFaculty of Petroleum, Gas, and Petrochemical EngineeringPersian Gulf UniversityDepartment of Petroleum EngineeringFaculty of Petroleum, Gas, and Petrochemical EngineeringPersian Gulf UniversityDepartment of Petroleum EngineeringFaculty of Petroleum, Gas, and Petrochemical EngineeringPersian Gulf UniversityDepartment of Petroleum EngineeringFaculty of Petroleum, Gas, and Petrochemical EngineeringPersian Gulf UniversityAbstract Hydraulic fracturing (HF) is a technique employed in the oil and gas industry to extract hydrocarbon resources from shale formations and low-permeability rocks. This process involves the creation of new fractures and the extension of existing ones within the rock by injecting a high-pressure fracturing fluid, which enhances the flow of hydrocarbons. To assess the efficiency and safety of HF, various factors such as fracture orientation, geometry, length, distribution, and stimulated reservoir volume are analyzed. In the complex field of HF, the accurate assessment of fracture parameters is crucial for optimizing operational efficiency and ensuring environmental safety. This study investigates the role of machine learning (ML) methodologies in enhancing the precision of these assessments, with a particular focus on fracture width a critical factor in the effectiveness of HF. By utilizing a comprehensive dataset from hydrocarbon fields, in this study, the application of several advanced ML models was investigated, including Artificial Neural Networks (ANNs), Random Forest (RF), and K-Nearest Neighbors (KNNs). These models were specifically employed to predict and assess fracture width based on a variety of geological and operational parameters: geometric factors (γ), Shear Modulus (G), Poisson’s ratio (ν), viscosity of the fracturing fluid (µ in centipoise), crack height (h), fluid efficiency (η), injection time (t), and crack length (x). This study evaluated the performance of ANN, RF, and KNN models, achieving accuracies of 0.978, 0.979, and 0.893, respectively, which underscores their strong predictive modeling capabilities. The results were meticulously documented for each methodology. The RMSE values for ANN, KNN, and RF were 7.552, 15.711, and 6.194, respectively. Notably, the RF approach demonstrated superior performance with an RMSE of 6.194, establishing it as the most accurate method. This study highlights the transformative potential of ML in the pre-stimulation planning phase of HF, enabling enhanced real-time decision-making and operational optimization, which ultimately results in more effective and efficient fracturing operations.https://doi.org/10.1007/s13202-024-01914-4Artificial neural network (ANN)Random Forest (RF)K-Nearest neighbors (KNNs)Hydraulic fracturing (HF)Machine learning (ML) |
| spellingShingle | Ali Karami Ali Akbari Yousef Kazemzadeh Hamed Nikravesh Enhancing hydraulic fracturing efficiency through machine learning Journal of Petroleum Exploration and Production Technology Artificial neural network (ANN) Random Forest (RF) K-Nearest neighbors (KNNs) Hydraulic fracturing (HF) Machine learning (ML) |
| title | Enhancing hydraulic fracturing efficiency through machine learning |
| title_full | Enhancing hydraulic fracturing efficiency through machine learning |
| title_fullStr | Enhancing hydraulic fracturing efficiency through machine learning |
| title_full_unstemmed | Enhancing hydraulic fracturing efficiency through machine learning |
| title_short | Enhancing hydraulic fracturing efficiency through machine learning |
| title_sort | enhancing hydraulic fracturing efficiency through machine learning |
| topic | Artificial neural network (ANN) Random Forest (RF) K-Nearest neighbors (KNNs) Hydraulic fracturing (HF) Machine learning (ML) |
| url | https://doi.org/10.1007/s13202-024-01914-4 |
| work_keys_str_mv | AT alikarami enhancinghydraulicfracturingefficiencythroughmachinelearning AT aliakbari enhancinghydraulicfracturingefficiencythroughmachinelearning AT yousefkazemzadeh enhancinghydraulicfracturingefficiencythroughmachinelearning AT hamednikravesh enhancinghydraulicfracturingefficiencythroughmachinelearning |