Machine-Learning-Based Multi-Surrogate-Assisted Joint Optimization for Hydraulic Fracturing Design and Production Control
Abstract
: The distribution of hydraulic fractures and production control strategies have significant influences on the fluid flow and production performance of low-permeability waterflooding reservoirs. Traditional approaches typically focus solely on fracture parameters while overlooking production control. To address this limitation, this work proposes a joint optimization framework that simultaneously integrates hydraulic fracturing design and production control. However, the joint optimization of hydraulic fracture distribution and production control requires a large amount of reservoir numerical simulation. To solve this problem, a novel adaptive multi-surrogate-assisted differential evolution (AMSADE) algorithm is developed. The AMSADE algorithm utilizes a surrogate model pool comprising radial basis functions, polynomial response surfaces, and deep neural networks. Additionally, an embedded discrete fracture model (EDFM) is adopted for simulation of fractured reservoir flow and optimization evaluation. The proposed method was applied to a two-dimensional waterflooding reservoir model. The results demonstrate that the algorithm converges rapidly, requiring only 200 numerical simulations to achieve optimal performance. Compared with the classical differential evolution algorithm, the net present value was improved by 17.5%. Overall, the proposed joint optimization framework based on the AMSADE algorithm successfully and simultaneously determines the optimal fracturing and production control parameters.