A novel high-dimension shale gas reservoir hydraulic fracture network parameters optimization framework

Abstract

Optimization design of hydraulic fracture network parameters significantly reduces the high investment risk in the production of shale gas. This is also a numerical simulation-based optimization problem. Surrogate-assisted optimization method which adopts a simple-yet-accurate approximate model to facilitate the optimization workflow is one of the most promising methods to lessen the computational burden. However, due to multi-stage fracturing and “well factory” production model, the dimension of variables in hydraulic fracture network parameters optimization increases sharply, and a significant number of data are required to ensure the performance of the surrogate model. To address this problem, a novel surrogate-assisted multi-objective optimization method using the tri-training strategy and variable-length non-dominated sorting genetic algorithm-II (VL-NSGA-II) is proposed for high-dimension hydraulic fracture network parameters optimization in shale gas reservoir. The proposed method is called HDWHF. In this HDWHF method, the tri-training strategy is adopted to infill solutions with the highest-confidence fitness as new samples to retrain the surrogate model during each iteration while VL-NSGA-II is adopted as the optimizer. This strategy enables the increase of samples without numerical simulation runs, which realizes a decrease of the numerical simulation runs and enhance the performance of the surrogate model. The superior performance of the HDWHF against conventional surrogate-assisted methods is verified using three high-dimension examples with various numbers of wells and fracture types. This proposed HDWHF workflow provides an intelligent means for efficient optimization and decision-making of high-dimension hydraulic fracture network parameters in shale gas reservoirs.