Research Papers: Petroleum Engineering

Drilling Rate of Penetration Prediction of High-Angled Wells Using Artificial Neural Networks

[+] Author and Article Information
Ahmed K. Abbas

Iraqi Drilling Company,
Basra 61004, Iraq
e-mails: akayr4@mst.edu; ahmed.khudair.abbs@idc.gov.iq

Salih Rushdi

Department of Chemical Engineering,
University of Al-Qadisiyah,
Al-Qadisiyah 58002, Iraq
e-mail: salih.rushdi@qu.edu.iq

Mortadha Alsaba

Department of Petroleum Engineering,
Australian College of Kuwait,
Safat 13015, Kuwait
e-mail: m.alsaba@ack.edu.kw

Mohammed F. Al Dushaishi

Department of Petroleum Engineering,
Texas A&M International University,
Laredo, TX 78041
e-mail: mohammed.aldushaishi@tamiu.edu

1Corresponding author.

Contributed by the Petroleum Division of ASME for publication in the Journal of Energy Resources Technology. Manuscript received December 29, 2018; final manuscript received April 29, 2019; published online May 20, 2019. Assoc. Editor: Arash Dahi Taleghani.

J. Energy Resour. Technol 141(11), 112904 (May 20, 2019) (11 pages) Paper No: JERT-18-1920; doi: 10.1115/1.4043699 History: Received December 29, 2018; Accepted April 29, 2019

Predicting the rate of penetration (ROP) is a significant factor in drilling optimization and minimizing expensive drilling costs. However, due to the geological uncertainty and many uncontrolled operational parameters influencing the ROP, its prediction is still a complex problem for the oil and gas industries. In the present study, a reliable computational approach for the prediction of ROP is proposed. First, fscaret package in a R environment was implemented to find out the importance and ranking of the inputs’ parameters. According to the feature ranking process, out of the 25 variables studied, 19 variables had the highest impact on ROP based on their ranges within this dataset. Second, a new model that is able to predict the ROP using real field data, which is based on artificial neural networks (ANNs), was developed. In order to gain a deeper understanding of the relationships between input parameters and ROP, this model was used to check the effect of the weight on bit (WOB), rotation per minute (rpm), and flow rate (FR). Finally, the simulation results of three deviated wells showed an acceptable representation of the physical process, with reasonable predicted ROP values. The main contribution of this research as compared to previous studies is that it investigates the influence of well trajectory (azimuth and inclination) and mechanical earth modeling parameters on the ROP for high-angled wells. The major advantage of the present study is optimizing the drilling parameters, predicting the proper penetration rate, estimating the drilling time of the deviated wells, and eventually reducing the drilling cost for future wells.

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Fig. 1

An ANNs structure with one hidden layer

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Fig. 2

Predicted rock mechanical properties’ logs and laboratory measurements

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Fig. 3

Estimation of the pore pressure and in situ principal stress magnitudes at a single well location

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Fig. 4

Ranking of variables

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Fig. 5

Model outputs versus real data: (a) training dataset and (b) testing dataset

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Fig. 6

Error distribution statistics for the developed ANNs model: (a) training dataset and (b) testing dataset

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Fig. 7

Effect of operating parameters on the ROP: (a) effect of the WOB on the ROP, (b) effect of the rpm on the ROP, and (c) effect of the FR on the ROP

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Fig. 8

ROP prediction for well 1: (a) predicted and measured ROP along the depth and (b) residual errors of the predicted ROP

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Fig. 9

ROP prediction for well 2: (a) the predicted and measured ROP along the depth and (b) residual errors of the predicted ROP

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Fig. 10

ROP prediction for well 3: (a) the predicted and measured ROP along the depth and (b) residual errors of the predicted ROP



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