The Science and Information (SAI) Organization
  • Home
  • About Us
  • Journals
  • Conferences
  • Contact Us

Publication Links

  • IJACSA
  • Author Guidelines
  • Publication Policies
  • Digital Archiving Policy
  • Promote your Publication
  • Metadata Harvesting (OAI2)

IJACSA

  • About the Journal
  • Call for Papers
  • Editorial Board
  • Author Guidelines
  • Submit your Paper
  • Current Issue
  • Archives
  • Indexing
  • Fees/ APC
  • Reviewers
  • Apply as a Reviewer

IJARAI

  • About the Journal
  • Archives
  • Indexing & Archiving

Special Issues

  • Home
  • Archives
  • Proposals
  • Guest Editors
  • SUSAI-EE 2025
  • ICONS-BA 2025
  • IoT-BLOCK 2025

Future of Information and Communication Conference (FICC)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Computing Conference

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Intelligent Systems Conference (IntelliSys)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Future Technologies Conference (FTC)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact
  • Home
  • Call for Papers
  • Editorial Board
  • Guidelines
  • Submit
  • Current Issue
  • Archives
  • Indexing
  • Fees
  • Reviewers
  • Subscribe

DOI: 10.14569/IJACSA.2025.01603108
PDF

A Novel Paradigm for Parameter Optimization of Hydraulic Fracturing Using Machine Learning and Large Language Model

Author 1: Chunxi Yang
Author 2: Chuanyou Xu
Author 3: Yue Ma
Author 4: Bang Qu
Author 5: Yiquan Liang
Author 6: Yajun Xu
Author 7: Lei Xiao
Author 8: Zhimin Sheng
Author 9: Zhenghao Fan
Author 10: Xin Zhang

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 16 Issue 3, 2025.

  • Abstract and Keywords
  • How to Cite this Article
  • {} BibTeX Source

Abstract: Hydraulic fracturing is a common practice in the oil and gas industry meant to increase the production of oil and natural gas. In this process, appropriate fracturing design parameters are important to maximize the efficiency of fracture propagation. However, conventional fracturing parameter design methods often rely on expert experience or fail to take into account complex geological conditions, resulting in suboptimal parameter design schemes. Therefore, this paper presents PPOHyFrac, a novel paradigm for optimizing hydraulic fracturing parameters with large language model and machine learning, which aims to automatically extract, assess and optimize fracturing parameters. PPOHyFrac uses advanced large language model to perform the extraction of key parameters from hundreds of fracturing design documents, and then refines the extracted data using statistical methods such as missing value imputation and feature normalization. Besides, the techniques in correlation analysis are utilized to identify key influencing factors and finally machine learning methods are implemented to optimize and predict the key influencing factors. This paper also presents a comparative study of five machine learning methods. Experiments show that random forest is the best choice for parameter optimization and can improve the prediction and optimization accuracy of key parameters.

Keywords: Hydraulic fracturing; parameter optimization; large language model; machine learning

Chunxi Yang, Chuanyou Xu, Yue Ma, Bang Qu, Yiquan Liang, Yajun Xu, Lei Xiao, Zhimin Sheng, Zhenghao Fan and Xin Zhang, “A Novel Paradigm for Parameter Optimization of Hydraulic Fracturing Using Machine Learning and Large Language Model” International Journal of Advanced Computer Science and Applications(IJACSA), 16(3), 2025. http://dx.doi.org/10.14569/IJACSA.2025.01603108

@article{Yang2025,
title = {A Novel Paradigm for Parameter Optimization of Hydraulic Fracturing Using Machine Learning and Large Language Model},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.01603108},
url = {http://dx.doi.org/10.14569/IJACSA.2025.01603108},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {3},
author = {Chunxi Yang and Chuanyou Xu and Yue Ma and Bang Qu and Yiquan Liang and Yajun Xu and Lei Xiao and Zhimin Sheng and Zhenghao Fan and Xin Zhang}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

IJACSA

Upcoming Conferences

IntelliSys 2025

28-29 August 2025

  • Amsterdam, The Netherlands

Future Technologies Conference 2025

6-7 November 2025

  • Munich, Germany

Healthcare Conference 2026

21-22 May 2026

  • Amsterdam, The Netherlands

Computing Conference 2026

9-10 July 2026

  • London, United Kingdom

IntelliSys 2026

3-4 September 2026

  • Amsterdam, The Netherlands

Computer Vision Conference 2026

15-16 October 2026

  • Berlin, Germany
The Science and Information (SAI) Organization
BACK TO TOP

Computer Science Journal

  • About the Journal
  • Call for Papers
  • Submit Paper
  • Indexing

Our Conferences

  • Computing Conference
  • Intelligent Systems Conference
  • Future Technologies Conference
  • Communication Conference

Help & Support

  • Contact Us
  • About Us
  • Terms and Conditions
  • Privacy Policy

© The Science and Information (SAI) Organization Limited. All rights reserved. Registered in England and Wales. Company Number 8933205. thesai.org