Enhancing Breakdown Pressure Predictions in Ultra-Tight Formations through Robust Machine Learning Techniques

Ayyaz Mustafa, Zeeshan Tariq, Manojkumar Gudala, Bicheng Yan, Shuyu Sun, Mohamed Mahmoud

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Accurately estimating rock breakdown pressure is crucial for designing effective hydraulic fracturing operations, particularly in unconventional ultra-tight reservoirs where hydrocarbon extraction is challenging. However, conducting experimental studies on hydraulic fracturing is both time-consuming and costly. To address this, we employed robust machine learning (ML) tools to estimate the breakdown pressure. The research comprised two stages: an extensive experimental phase followed by the development of ML prediction models using the obtained data. The ML models were trained using experimental factors such as injection rate, confining stress, fluid viscosity, and rock characteristics, including unconfined compressive strength, Poisson's ratio, tensile strength, porosity, permeability, and bulk density. Six machine learning techniques-K-Nearest Neighbor (KNN), Random Forest (RF), Decision Trees (DT), artificial neural networks (ANN), gradient boosting (GB), and adaptive gradient boosting (Adaboost)-were employed to construct the prediction models. With the optimal settings for the ML models, the breakdown pressure of the tight formations was accurately predicted with a 99% accuracy. The proposed ML approaches not only offer significant cost savings but also serve as a quick evaluation tool to assess the development prospects of tight rocks.

Original languageEnglish (US)
Title of host publication57th US Rock Mechanics/Geomechanics Symposium
PublisherAmerican Rock Mechanics Association (ARMA)
ISBN (Electronic)9780979497582
StatePublished - 2023
Event57th US Rock Mechanics/Geomechanics Symposium - Atlanta, United States
Duration: Jun 25 2023Jun 28 2023

Publication series

Name57th US Rock Mechanics/Geomechanics Symposium


Conference57th US Rock Mechanics/Geomechanics Symposium
Country/TerritoryUnited States

Bibliographical note

Publisher Copyright:
© 2023 57th US Rock Mechanics/Geomechanics Symposium. All Rights Reserved.

ASJC Scopus subject areas

  • Geochemistry and Petrology
  • Geophysics


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