Prediction of compressive strength and elastic modulus of carbonate rocks

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• 作者：N. Madhubabu^a ; P.K. Singh^b ; ^{prakashks@iitb.ac.in" class="auth_mail" title="E-mail the corresponding author} ; Ashutosh Kainthola^b ; Bankim Mahanta^b ; A. Tripathy^b ; T.N. Singh^b
• 关键词：Young&rsquo ; s modulus ; Porosity ; Ultrasonic velocity ; Carbonate rocks ; Artificial Neural Network
• 刊名：Measurement
• 出版年：2016
• 出版时间：June 2016
• 年：2016
• 卷：88
• 期：Complete
• 页码：202-213
• 全文大小：3946 K

文摘

Uniaxial Compressive Strength (UCS) and Modulus of elasticity (E) of carbonate rocks are very critical properties in petroleum, mining and civil industries. UCS is the measure of the strength of the rock and E depicts the stiffness, together they control the deformational behavior. But the heterogeneity introduced as a result of fractures, dissolution and dependency on pH and temperature makes them a difficult material to study. Complex diagenesis and resulting pore system makes the job even more daunting. So, an attempt is made to predict these properties using simple index parameters such as Porosity, Density, P-wave velocity, Poisson’s ratio and Point load index. Multiple Linear Regression Analysis (MVRA) and Artificial Neural Networking (ANN) have been used for predicting the two properties and the accuracy is tested by root mean square error. The results show that ANN has a better predictive efficiency than MVRA and they can be applied for predicting UCS and Young’s modulus of carbonate rocks with reasonable confidence.