Estimation of Static Young’s Modulus for Sandstone Formation Using Artificial Neural Networks

Ahmed Abdulhamid Mahmoud, Salaheldin Elkatatny, Abdulwahab Ali and Tamer Moussa
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Ahmed Abdulhamid Mahmoud: College of Petroleum Engineering and Geosciences, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia
Salaheldin Elkatatny: College of Petroleum Engineering and Geosciences, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia
Abdulwahab Ali: Center of Integrative Petroleum Research, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia
Tamer Moussa: College of Petroleum Engineering and Geosciences, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia

Energies, 2019, vol. 12, issue 11, 1-15

Abstract: In this study, we used artificial neural networks (ANN) to estimate static Young’s modulus (E static ) for sandstone formation from conventional well logs. ANN design parameters were optimized using the self-adaptive differential evolution optimization algorithm. The ANN model was trained to predict E static from conventional well logs of the bulk density, compressional time, and shear time. The ANN model was trained on 409 data points from one well. The extracted weights and biases of the optimized ANN model was used to develop an empirical relationship for E static estimation based on well logs. This empirical correlation was tested on 183 unseen data points from the same training well and validated using data from three different wells. The optimized ANN model estimated E static for the training dataset with a very low average absolute percentage error (AAPE) of 0.98%, a very high correlation coefficient (R) of 0.999 and a coefficient of determination (R 2 ) of 0.9978. The developed ANN-based correlation estimated E static for the testing dataset with a very high accuracy as indicated by the low AAPE of 1.46% and a very high R and R 2 of 0.998 and 0.9951, respectively. In addition, the visual comparison of the core-tested and predicted E static of the validation dataset confirmed the high accuracy of the developed ANN-based empirical correlation. The ANN-based correlation overperformed four of the previously developed E static correlations in estimating E static for the validation data, E static for the validation data was predicted with an AAPE of 3.8% by using the ANN-based correlation compared to AAPE’s of more than 36.0% for the previously developed correlations.

Keywords: static young’s modulus; artificial neural networks; self-adaptive differential evolution algorithm; sandstone reservoirs (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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