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Research Papers: Petroleum Engineering

A Robust Rate of Penetration Model for Carbonate Formation

[+] Author and Article Information
Ahmad Al-AbdulJabbar

Department of Petroleum Engineering,
King Fahd University of
Petroleum and Minerals,
P.O. Box 5049,
Dhahran 31261, Saudi Arabia
e-mail: g200679600@kfupm.edu.sa

Salaheldin Elkatatny

Department of Petroleum Engineering,
King Fahd University of
Petroleum and Minerals,
P.O. Box 5049,
Dhahran 31261, Saudi Arabia;
Petroleum Department,
Cairo University,
Cairo12613, Egypt
e-mail: elkatatny@kfupm.edu.sa

Mohamed Mahmoud

Department of Petroleum Engineering,
King Fahd University of
Petroleum and Minerals,
P.O. Box 5049,
Dhahran 31261, Saudi Arabia
e-mail: mmahmoud@kfupm.edu.sa

Khaled Abdelgawad

Department of Petroleum Engineering,
King Fahd University of
Petroleum and Minerals,
P.O. Box 5049,
Dhahran 31261, Saudi Arabia
e-mail: abouzidan@kfupm.edu.sa

Abdulaziz Al-Majed

Department of Petroleum Engineering,
King Fahd University of
Petroleum and Minerals,
Dhahran 31261, Saudi Arabia
e-mail: aamajed@kfupm.edu.sa

1Corresponding author.

Contributed by the Petroleum Division of ASME for publication in the JOURNAL OF ENERGY RESOURCES TECHNOLOGY. Manuscript received April 19, 2018; final manuscript received October 23, 2018; published online November 30, 2018. Assoc. Editor: Daoyong (Tony) Yang.

J. Energy Resour. Technol 141(4), 042903 (Nov 30, 2018) (9 pages) Paper No: JERT-18-1278; doi: 10.1115/1.4041840 History: Received April 19, 2018; Revised October 23, 2018

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. ROP is defined as the speed at which the drill bit breaks the rock to deepen the hole, and it is measured in units of feet per hour or meters per hour. ROP prediction is very challenging before drilling, because it depends on many parameters that should be optimized. 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 of 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 (SPP), uniaxial compressive strength (UCS), and mud properties (density and viscosity) using 7000 real-time data measurements. In addition, the relative importance of drilling fluid properties, rock strength, and drilling parameters to ROP is determined. The obtained results showed that the ROP is highly affected by WOB, RPM, T, and horsepower (HP), where the coefficient of determination (T2) was 0.71, 0.87, 0.70, and 0.92 for WOB, RPM, T, and HP, respectively. ROP also showed a strong function of mud fluid properties, where R2 was 0.70 and 0.70 for plastic viscosity (PV) and mud density, respectively. No clear relationship was observed between ROP and yield point (YP) for more than 500 field data points. The new model predicts the ROP with average absolute percentage error (AAPE) of 5% and correlation coefficient (R) 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 their compressive strength range to predict the ROP. Clustering yielded accurate ROP prediction compared to the field ROP.

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Figures

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Fig. 2

Effect of mechanical parameters on ROP: (a) WOB versus ROP, (b) RPM versus ROP, (c) torque versus ROP, (d) SPP versus ROP, (e) Q versus ROP, and (f) HSI versus ROP

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Fig. 1

Formation tops and wells location

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Fig. 3

Effect of mud properties on ROP: (a) mud density versus ROP, (b) PV versus ROP, and (c) YP versus ROP

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Fig. 4

Rate of penetration prediction using Eq. (13)

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Fig. 5

Rate of penetration prediction using Eq. (13) after applying the clustering effect for well-1

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Fig. 6

b Exponent prediction from UCS

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Fig. 7

Rate of penetration prediction for well-2

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Fig. 8

Rate of penetration prediction for well-3

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Fig. 10

New ROP model yielded the lowest AAPE

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Fig. 9

Comparison of all ROP models

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