Machine Learning-Based Accelerated Approaches to Infer Breakdown Pressure of Several Unconventional Rock Types

Zeeshan Tariq, Bicheng Yan, Shuyu Sun, Manojkumar Gudala, Murtada Saleh Aljawad, Mobeen Murtaza, Mohamad Mahmoud

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


Unconventional oil and gas reservoirs are usually classified by extremely low porosity and permeability values. The most economical way to produce hydrocarbons from such reservoirs is by creating artificially induced channels. To effectively design hydraulic fracturing jobs, accurate values of rock breakdown pressure are needed. Conducting hydraulic fracturing experiments in the laboratory is a very expensive and time-consuming process. Therefore, in this study, different machine learning (ML) models were efficiently utilized to predict the breakdown pressure of tight rocks. In the first part of the study, to measure the breakdown pressures, a comprehensive hydraulic fracturing experimental study was conducted on various rock specimens. A total of 130 experiments were conducted on different rock types such as shales, sandstone, tight carbonates, and synthetic samples. Rock mechanical properties such as Young’s modulus (E), Poisson’s ratio (ν), unconfined compressive strength, and indirect tensile strength (σt) were measured before conducting hydraulic fracturing tests. ML models were used to correlate the breakdown pressure of the rock as a function of fracturing experimental conditions and rock properties. In the ML model, we considered experimental conditions, including the injection rate, overburden pressures, and fracturing fluid viscosity, and rock properties including Young’s modulus (E), Poisson’s ratio (ν), UCS, and indirect tensile strength (σt), porosity, permeability, and bulk density. ML models include artificial neural networks (ANNs), random forests, decision trees, and the K-nearest neighbor. During training of ML models, the model hyperparameters were optimized by the grid-search optimization approach. With the optimal setting of the ML models, the breakdown pressure of the unconventional formation was predicted with an accuracy of 95%. The accuracy of all ML techniques was quite similar; however, an explicit empirical correlation from the ANN technique is proposed. The empirical correlation is the function of all input features and can be used as a standalone package in any software. The proposed methodology to predict the breakdown pressure of unconventional rocks can minimize the laboratory experimental cost of measuring fracture parameters and can be used as a quick assessment tool to evaluate the development prospect of unconventional tight rocks.
Original languageEnglish (US)
JournalACS Omega
StatePublished - Nov 4 2022

Bibliographical note

KAUST Repository Item: Exported on 2022-11-07
Acknowledged KAUST grant number(s): BAS/1/1351-01-01, BAS/1/1423-01-01, URF/1/4074-01-01
Acknowledgements: Z.T. and B.Y. thank the King Abdullah University of Science and Technology (KAUST) for the Research Funding through the grant BAS/1/1423-01-01, and Z.T. and S.S. thank the research funding provide by the KAUST, Saudi Arabia, through the grants BAS/1/1351-01-01 and URF/1/4074-01-01.


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