Unconfined compressive strength estimation in the shale gas reservoir using data driven technique

Xian Shi, Yuanyuan Yang, Yong Liao, Daobing Wang, Songcai Han, Qi Gao

Research output: Chapter in Book/Conference paperConference paperpeer-review


Identifying and predicting rock unconfined compressive strength is critical to well design, wellbore stability, and hydraulic fracturing stimulation in shales. However, accurate prediction of rock mechanical properties is hard in the absence of high-resolution advanced geophysical logs (e.g., image logs) and laboratory mechanical core samples. This study focuses on rock mechanical parameter estimation using three data-driven techniques: backpropagation (BP) network, support vector machine (SVM), and extreme learning machine (ELM) models in a shale gas reservoir. Additionally, this study aimed to obtain accurate rock mechanical properties, which have proven challenging using conventional petrophysical methods in wells without downhole core data. A total of 350 samples from 22 wells with laboratory measurement data were used to train and validate the neural network. Robust algorithms were used to provide fast and accurate prediction results, which were verified by comparing them with other approaches. The trained machine-learning models were cross-validated to check their robustness. The network model was then applied to estimate rock unconfined compressive strength for the remaining wells. The predicted results had a good match well with the laboratory test conclusions. Based on the estimations, rock mechanical properties were mapped and analyzed in the target shale gas zone. This method is helpful for geomechanical modeling in shale gas reservoirs.

Original languageEnglish
Title of host publication57th US Rock Mechanics/Geomechanics Symposium
PublisherAmerican Rock Mechanics Association (ARMA)
Number of pages6
ISBN (Electronic)9780979497582
Publication statusPublished - 25 Jun 2023
Event57th US Rock Mechanics/Geomechanics Symposium - Atlanta, United States
Duration: 25 Jun 202328 Jun 2023

Publication series

Name57th US Rock Mechanics/Geomechanics Symposium


Conference57th US Rock Mechanics/Geomechanics Symposium
Country/TerritoryUnited States


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