TY - GEN
T1 - Enhancing Breakdown Pressure Predictions in Ultra-Tight Formations through Robust Machine Learning Techniques
AU - Mustafa, Ayyaz
AU - Tariq, Zeeshan
AU - Gudala, Manojkumar
AU - Yan, Bicheng
AU - Sun, Shuyu
AU - Mahmoud, Mohamed
N1 - Publisher Copyright:
© 2023 57th US Rock Mechanics/Geomechanics Symposium. All Rights Reserved.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85177818854&partnerID=8YFLogxK
U2 - 10.56952/ARMA-2023-0756
DO - 10.56952/ARMA-2023-0756
M3 - Conference contribution
AN - SCOPUS:85177818854
T3 - 57th US Rock Mechanics/Geomechanics Symposium
BT - 57th US Rock Mechanics/Geomechanics Symposium
PB - American Rock Mechanics Association (ARMA)
T2 - 57th US Rock Mechanics/Geomechanics Symposium
Y2 - 25 June 2023 through 28 June 2023
ER -