LAPSE:2023.25300
Published Article
LAPSE:2023.25300
Optimization of Fracturing Parameters with Machine-Learning and Evolutionary Algorithm Methods
Zhenzhen Dong, Lei Wu, Linjun Wang, Weirong Li, Zhengbo Wang, Zhaoxia Liu
March 28, 2023
Oil production from tight oil reservoirs has become economically feasible because of the combination of horizontal drilling and multistage hydraulic fracturing. Optimal fracture design plays a critical role in successful economical production from a tight oil reservoir. However, many complex parameters such as fracture spacing and fracture half-length make fracturing treatments costly and uncertain. To improve fracture design, it is essential to determine reasonable ranges for these parameters and to evaluate their effects on well performance and economic feasibility. In traditional analytical and numerical simulation methods, many simplifications and assumptions are introduced for artificial fracture characterization and gas percolation mechanisms, and their implementation process remains complicated and computationally inefficient. Most previous studies on big data-driven fracturing parameter optimization have been based on only a single output, such as expected ultimate recovery, and few studies have integrated machine learning with evolutionary algorithms to optimize fracturing parameters based on time-series production prediction and economic objectives. This study proposed a novel approach, combining a data-driven model with evolutionary optimization algorithms to optimize fracturing parameters. We established a significant number of static and dynamic data sets representing the geological and developmental characteristics of tight oil reservoirs from numerical simulation. Four production-prediction models based on machine-learning methods—support vector machine, gradient-boosted decision tree, random forest, and multilayer perception—were constructed as mapping functions between static properties and dynamic production. Then, to optimize the fracturing parameters, the best machine-learning-based production predictive model was coupled with four evolutionary algorithms—genetic algorithm, differential evolution algorithm, simulated annealing algorithm, and particle swarm optimization—to investigate the highest net present value (NPV). The results show that among the four production-prediction models established, multilayer perception (MLP) has the best prediction performance. Among the evolutionary algorithms, particle swarm optimization (PSO) not only has the fastest convergence speed but also the highest net present value. The optimal fracturing parameters for the study area were identified. The hybrid MLP-PSO model represents a robust and convenient method to forecast the time-series production and to optimize fracturing parameters by reducing manual tuning.
Keywords
evolutionary algorithms, fracturing parameter optimization, Machine Learning, net present value, production prediction
Suggested Citation
Dong Z, Wu L, Wang L, Li W, Wang Z, Liu Z. Optimization of Fracturing Parameters with Machine-Learning and Evolutionary Algorithm Methods. (2023). LAPSE:2023.25300
Author Affiliations
Dong Z: Petroleum Engineering Department, Xi’an Shiyou University, Xi’an 710065, China
Wu L: Petroleum Engineering Department, Xi’an Shiyou University, Xi’an 710065, China [ORCID]
Wang L: Petroleum Engineering Department, Xi’an Shiyou University, Xi’an 710065, China
Li W: Petroleum Engineering Department, Xi’an Shiyou University, Xi’an 710065, China
Wang Z: Research Institute of Petroleum Exploration and Development, PetroChina, Beijing 100083, China
Liu Z: Research Institute of Petroleum Exploration and Development, PetroChina, Beijing 100083, China
Journal Name
Energies
Volume
15
Issue
16
First Page
6063
Year
2022
Publication Date
2022-08-21
Published Version
ISSN
1996-1073
Version Comments
Original Submission
Other Meta
PII: en15166063, Publication Type: Journal Article
Record Map
Published Article

LAPSE:2023.25300
This Record
External Link

doi:10.3390/en15166063
Publisher Version
Download
Files
[Download 1v1.pdf] (6.6 MB)
Mar 28, 2023
Main Article
License
CC BY 4.0
Meta
Record Statistics
Record Views
61
Version History
[v1] (Original Submission)
Mar 28, 2023
 
Verified by curator on
Mar 28, 2023
This Version Number
v1
Citations
Most Recent
This Version
URL Here
https://psecommunity.org/LAPSE:2023.25300
 
Original Submitter
Auto Uploader for LAPSE
Links to Related Works
Directly Related to This Work
Publisher Version