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research-article

A Robust Rate of Penetration Model for Carbonate Formation

[+] Author and Article Information
Ahmed Al-AbdulJabbar

Department of Petroleum Engineering, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia
g200679600@kfupm.edu.sa

Salaheldin Elkatatny

Department of Petroleum Engineering, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia; Petroleum Department, Cairo University, Cairo, Egypt, Post Code: 12613
elkatatny@kfupm.edu.sa

Mohamed Mahmoud

Department of Petroleum Engineering, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia
mmahmoud@kfupm.edu.sa

Khaled Abdelgawad

Department of Petroleum Engineering, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia
abouzidan@kfupm.edu.sa

Abdulaziz Al-Majed

Department of Petroleum Engineering, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia
aamajed@kfupm.edu.sa

1Corresponding author.

ASME doi:10.1115/1.4041840 History: Received April 19, 2018; Revised October 23, 2018

Abstract

During the drilling operations, optimizing the rate of penetration (ROP) is very crucial because it can significantly reduce the overall cost of the drilling process. Several models have been developed in the literature to predict ROP. Most of the developed models used drilling parameters such as weight on bit (WOB), pumping rate (Q), and string revolutions per minute (RPM). Few researchers considered the effect mud properties on ROP by including a small number of actual field measurements. This paper introduces a new robust model to predict the ROP using both drilling parameters (WOB, Q, ROP, torque (T), standpipe pressure, uniaxial compressive strength (UCS), and mud properties (density and viscosity) using 7000 real-time data measurements. The obtained results showed that the ROP is highly affected by WOB, RPM, T, and horsepower, where the coefficient of determination (R2) was 0.71, 0.87,0.70, and 0.92, respectively. ROP also showed a strong function of mud fluid properties, where R2 was 0.70 and 0.70 for plastic viscosity and mud density, respectively. No clear relationship was observed between ROP and yield point. The new model predicts the ROP with average absolute percentage error of 5% and correlation coefficient of 0.93. in addition, the new model outperformed three existing ROP models. The novelty in this paper is the application of the clustering technique in which the formations are clustered based on UCS range to predict the ROP. Clustering yielded accurate ROP prediction compared to the field ROP.

Copyright (c) 2018 by ASME
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