InterPore2026

A Variable-Dimension Evolutionary Transfer Optimization Framework for Well-Fracture Pattern Co-optimization of Fractured Horizontal Wells in Shale-Gas Reservoirs

22 May 2026, 15:15

15m

Oral Presentation (MS20) Special Session in Honor of Jun Yao MS20

Speaker

Dali Zhao (China University of Petroleum(East China))

Description

Horizontal well fracturing is widely regarded as the most effective technology for enhancing the recovery rate of shale-gas reservoirs. Due to the complex flow mechanisms and significant reservoir heterogeneity, the collaborative optimization of well-fracture pattern parameters is highly challenging. In multi-well development optimization, the number of wells itself cannot be predetermined, and parameters of individual horizontal wells and their corresponding fractures vary. Thus, well-fracture pattern optimization is inherently a dynamic variable-dimensional optimization problem. Existing meta-heuristic algorithms typically fix the dimension of optimization variables and cannot address such variable-dimensional problems. To tackle this issue, this paper proposes a variable-dimensional evolutionary transfer optimization (VDETO) framework, which incorporates a probability controlled dimension adaptive adjustment mechanism. By minimizing the characteristics differences among population particles, it enables knowledge transfer across dimensions, allowing for the collaborative optimization of the number of wells, individual well parameters, and fracture parameters, thereby achieving an integrated design of well-fracture pattern. The VDETO framework was validated using benchmark functions and compared with methods such as Particle Swarm Optimization (PSO), Variable-length Particle Swarm Optimization (VPSO), and Modified Variable-length Particle Swarm Optimization (MVPSO). Furthermore, a collaborative optimization study of well-fracture pattern was conducted on a 2D shale-gas reservoir mechanistic model. The results demonstrate that VDETO outperforms commonly used variable-dimensional algorithms in both convergence speed and accuracy. Compared to traditional uniform well placement or concentrated well placement only in high-permeability zones, this method optimizes well locations across different sweet spots, creating high-permeability channels through fracturing to effectively connect multiple sweet spots, thereby significantly improving the net present value. This framework provides a novel approach for the collaborative optimization of well-fracture pattern parameters.