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contributor authorAl-AbdulJabbar, Ahmad
contributor authorElkatatny, Salaheldin
contributor authorMahmoud, Mohamed
contributor authorAbdelgawad, Khaled
contributor authorAl-Majed, Abdulaziz
date accessioned2019-03-17T10:59:20Z
date available2019-03-17T10:59:20Z
date copyright11/30/2018 12:00:00 AM
date issued2019
identifier issn0195-0738
identifier otherjert_141_04_042903.pdf
identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4256496
description abstractDuring 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.
publisherThe American Society of Mechanical Engineers (ASME)
titleA Robust Rate of Penetration Model for Carbonate Formation
typeJournal Paper
journal volume141
journal issue4
journal titleJournal of Energy Resources Technology
identifier doi10.1115/1.4041840
journal fristpage42903
journal lastpage042903-9
treeJournal of Energy Resources Technology:;2019:;volume( 141 ):;issue: 004
contenttypeFulltext


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