Surrogate-assisted level-based learning evolutionary search for heat extraction optimization of enhanced geothermal system

by   Guodong Chen, et al.

An enhanced geothermal system is essential to provide sustainable and long-term geothermal energy supplies and reduce carbon emissions. Optimal well-control scheme for effective heat extraction and improved heat sweep efficiency plays a significant role in geothermal development. However, the optimization performance of most existing optimization algorithms deteriorates as dimension increases. To solve this issue, a novel surrogate-assisted level-based learning evolutionary search algorithm (SLLES) is proposed for heat extraction optimization of enhanced geothermal system. SLLES consists of classifier-assisted level-based learning pre-screen part and local evolutionary search part. The cooperation of the two parts has realized the balance between the exploration and exploitation during the optimization process. After iteratively sampling from the design space, the robustness and effectiveness of the algorithm are proven to be improved significantly. To the best of our knowledge, the proposed algorithm holds state-of-the-art simulation-involved optimization framework. Comparative experiments have been conducted on benchmark functions, a two-dimensional fractured reservoir and a three-dimensional enhanced geothermal system. The proposed algorithm outperforms other five state-of-the-art surrogate-assisted algorithms on all selected benchmark functions. The results on the two heat extraction cases also demonstrate that SLLES can achieve superior optimization performance compared with traditional evolutionary algorithm and other surrogate-assisted algorithms. This work lays a solid basis for efficient geothermal extraction of enhanced geothermal system and sheds light on the model management strategies of data-driven optimization in the areas of energy exploitation.


page 8

page 18

page 25

page 30


Rank-Based Learning and Local Model Based Evolutionary Algorithm for High-Dimensional Expensive Multi-Objective Problems

Surrogate-assisted evolutionary algorithms have been widely developed to...

Voronoi-based Efficient Surrogate-assisted Evolutionary Algorithm for Very Expensive Problems

Very expensive problems are very common in practical system that one fit...

Heat Source Layout Optimization Using Automatic Deep Learning Surrogate and Multimodal Neighborhood Search Algorithm

In satellite layout design, heat source layout optimization (HSLO) is an...

A Federated Data-Driven Evolutionary Algorithm for Expensive Multi/Many-objective Optimization

Data-driven optimization has found many successful applications in the r...

Data-driven evolutionary algorithm for oil reservoir well-placement and control optimization

Optimal well placement and well injection-production are crucial for the...

Opposition-based Learning Harris Hawks Optimization with Advanced Transition Rules: Principles and Analysis

Harris hawks optimizer (HHO) is a recently developed, efficient meta-heu...

Surrogate-assisted performance tuning of knowledge discovery algorithms: application to clinical pathway evolutionary modeling

The paper proposes an approach for surrogate-assisted tuning of knowledg...

Please sign up or login with your details

Forgot password? Click here to reset