Development of new correlation of unconfined compressive strength for carbonate reservoir using artificial intelligence techniques

Zeeshan Tariq, S. M. Elkatatny, M. A. Mahmoud, A. Abdulraheem, A. Z. Abdelwahab, M. Woldeamanuel, I. M. Mohamed

Research output: Chapter in Book/Report/Conference proceedingConference contribution

15 Scopus citations

Abstract

Unconfined compressive strength (UCS) is the key parameter to; estimate the in situ stresses of the rock, alleviate drilling problems, design optimal fracture geometry and to predict optimum mud weight. Retrieving reservoir rock samples throughout the depth of the reservoir section and performing laboratory tests on them are extremely expensive as well as time consuming. Therefore, mostly UCS predicted from empirical correlations. Most of the empirical correlations for UCS prediction are based on elastic parameters or on compressional wave velocity. These correlations were developed using linear or non-linear regression techniques. This paper presents a rigorous empirical correlation based on the weights and biases of Artificial Neural Network to predict UCS. The testing of new correlation on real field data gave a less error between actual and predicted data, suggesting that the proposed correlation is very robust and accurate. Therefore, the developed correlation can serve as handy tool to help geo-mechanical engineers in order to determine the UCS.
Original languageEnglish (US)
Title of host publication51st US Rock Mechanics / Geomechanics Symposium 2017
PublisherAmerican Rock Mechanics Association (ARMA)info@armarocks.org
Pages1614-1619
Number of pages6
ISBN (Print)9781510857582
StatePublished - Jan 1 2017
Externally publishedYes

Bibliographical note

Generated from Scopus record by KAUST IRTS on 2023-09-20

Fingerprint

Dive into the research topics of 'Development of new correlation of unconfined compressive strength for carbonate reservoir using artificial intelligence techniques'. Together they form a unique fingerprint.

Cite this