A Powerful Prediction Framework of Fracture Parameters for Hydraulic Fracturing Incorporating eXtreme Gradient Boosting and Bayesian Optimization

Abstract

In the last decade, low-quality unconventional oil and gas resources have become the primary source for domestic oil and gas storage and production, and hydraulic fracturing has become a crucial method for modifying unconventional reservoirs. This paper puts forward a framework for predicting hydraulic fracture parameters. It combines eXtreme Gradient Boosting and Bayesian optimization to explore data-driven machine learning techniques in fracture simulation models. Analyzing fracture propagation through mathematical models can be both time-consuming and costly under conventional conditions. In this study, we predicted the physical parameters and three-dimensional morphology of fractures across multiple time series. The physical parameters encompass fracture width, pressure, proppant concentration, and inflow capacity. Our results demonstrate that the fusion model applied can significantly improve fracture morphology prediction accuracy, exceeding 0.95, while simultaneously reducing computation time. This method enhances standard numerical calculation techniques used for predicting hydraulic fracturing while encouraging research on the extraction of unconventional oil and gas resources.

Keywords

• hydraulic fracture
• fracture parameters
• machine learning
• eXtreme Gradient Boosting model
• unconventional reservoir