Prediction of the Rate of Penetration while Drilling Horizontal Carbonate Reservoirs Using the Self-Adaptive Artificial Neural Networks Technique

Ahmad Al-AbdulJabbar, Salaheldin Elkatatny, Ahmed Abdulhamid Mahmoud, Tamer Moussa, Dhafer Al-Shehri, Mahmoud Abughaban and Abdullah Al-Yami
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Ahmad Al-AbdulJabbar: Petroleum Department, College of Petroleum Engineering & Geosciences, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia
Salaheldin Elkatatny: Petroleum Department, College of Petroleum Engineering & Geosciences, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia
Ahmed Abdulhamid Mahmoud: Petroleum Department, College of Petroleum Engineering & Geosciences, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia
Tamer Moussa: Petroleum Department, College of Petroleum Engineering & Geosciences, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia
Dhafer Al-Shehri: Petroleum Department, College of Petroleum Engineering & Geosciences, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia
Mahmoud Abughaban: EXPEC Advanced Research Center (ARC), Dhahran 31311, Saudi Arabia
Abdullah Al-Yami: EXPEC Advanced Research Center (ARC), Dhahran 31311, Saudi Arabia

Sustainability, 2020, vol. 12, issue 4, 1-19

Abstract: Rate of penetration (ROP) is one of the most important drilling parameters for optimizing the cost of drilling hydrocarbon wells. In this study, a new empirical correlation based on an optimized artificial neural network (ANN) model was developed to predict ROP alongside horizontal drilling of carbonate reservoirs as a function of drilling parameters, such as rotation speed, torque, and weight-on-bit, combined with conventional well logs, including gamma-ray, deep resistivity, and formation bulk density. The ANN model was trained using 3000 data points collected from Well-A and optimized using the self-adaptive differential evolution (SaDE) algorithm. The optimized ANN model predicted ROP for the training dataset with an average absolute percentage error (AAPE) of 5.12% and a correlation coefficient (R) of 0.960. A new empirical correlation for ROP was developed based on the weights and biases of the optimized ANN model. The developed correlation was tested on another dataset collected from Well-A, where it predicted ROP with AAPE and R values of 5.80% and 0.951, respectively. The developed correlation was then validated using unseen data collected from Well-B, where it predicted ROP with an AAPE of 5.29% and a high R of 0.956. The ANN-based correlation outperformed all previous correlations of ROP estimation that were developed based on linear regression, including a recent model developed by Osgouei that predicted the ROP for the validation data with a high AAPE of 14.60% and a low R of 0.629.

Keywords: rate of penetration; drilling optimization; carbonate reservoir; horizontal wells (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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