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Updated: Sep 14, 2025

Evaluating the Impact of Hydraulic Fracturing on Streams using Microbial Molecular Signatures
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Evaluation of hydraulic fracturing using machine learning.

Ali Akbari1, Ali Karami2, Yousef Kazemzadeh3

  • 1Department of Petroleum Engineering, Faculty of Petroleum, Gas, and Petrochemical Engineering, Persian Gulf University, Bushehr, Iran. aliakbaripetroleum@gmail.com.

Scientific Reports
|July 24, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a machine learning framework to predict hydraulic fracturing (HF) efficiency, outperforming traditional methods. Random Forest (RF) achieved the highest accuracy, offering a practical tool for optimizing oil and gas recovery.

Keywords:
Hydraulic fracturingMachine learningNeural networks, PKN model, fracture propagation, hydrocarbon productionRandom forestSupport vector machine

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Area of Science:

  • Petroleum Engineering
  • Artificial Intelligence in Energy
  • Data Science for Reservoir Management

Background:

  • Traditional hydraulic fracturing (HF) evaluation methods struggle with complex, nonlinear interactions of operational and geological parameters.
  • Enhanced hydrocarbon recovery relies on optimizing HF efficiency, a significant challenge in the oil and gas industry.
  • Machine learning (ML) offers a promising avenue for more accurate HF performance prediction.

Purpose of the Study:

  • To develop and evaluate a machine learning-based framework for predicting hydraulic fracturing (HF) efficiency.
  • To compare the performance of Random Forest (RF), Support Vector Machine (SVM), and Neural Networks (NN) for HF efficiency prediction.
  • To assess model robustness across various data-splitting ratios and provide practical insights for field operations.

Main Methods:

  • Utilized a large-scale dataset of 16,000 records for hydraulic fracturing (HF) operations.
  • Applied advanced statistical characterization (mean, median, variance, skewness, quartiles) to explore data distribution.
  • Implemented and compared three machine learning algorithms: Random Forest (RF), Support Vector Machine (SVM), and Neural Networks (NN), evaluating robustness across varying train/test ratios.

Main Results:

  • Random Forest (RF) demonstrated superior performance, achieving a high coefficient of determination (R² = 0.9804).
  • RF exhibited the lowest Mean Absolute Deviation (MAD) and Root Mean Square Error (RMSE) in both training and testing phases.
  • The study confirmed RF's capability in handling complex subsurface data with high accuracy and efficiency.

Conclusions:

  • The proposed machine learning framework, particularly using Random Forest (RF), significantly enhances predictive accuracy for hydraulic fracturing (HF) efficiency.
  • The study provides a practical, data-driven tool for optimizing fracturing design and decision-making in reservoir engineering.
  • This integrated approach advances intelligent hydraulic fracturing practices in heterogeneous, data-rich environments.