Enhancing hydraulic fracturing efficiency through machine learning

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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.