Rate of Penetration Prediction in Drilling Operation in Oil and Gas Wells by K-nearest Neighbors and Multi-layer Perceptron Algorithms | ||
| Journal of Mining and Environment | ||
| مقاله 2، دوره 14، شماره 3، مهر 2023، صفحه 755-770 اصل مقاله (3 M) | ||
| نوع مقاله: Original Research Paper | ||
| شناسه دیجیتال (DOI): 10.22044/jme.2023.12694.2306 | ||
| نویسندگان | ||
| Yahia ElSayed Khamis* ؛ Shady Galal El-Rammah؛ Adel M Salem | ||
| Department of Petroleum Engineering, Faculty of Petroleum and Mining Engineering, Suez University, Suez, Egypt | ||
| چکیده | ||
| The rate of penetration plays a key role in maximizing drilling efficiency, so it is essential for the drilling process optimization and management. Traditional mathematical models have been used with some success to predict the rate of penetration in drilling. Due to the high complexity and non-linear behavior of drilling parameters with the rate of penetration, these mathematical models cannot accurately and comprehensively predict the rate of penetration. Machine learning (ML) seems to be an attractive alternative to model this complicated physical process. This research paper introduces new data-driven models used to predict ROP using different parameters such as (depth, weight on bit (WOB), revolution per minute (RPM), Torque (T), standpipe pressure (SPP), flow in pump (pumping flow rate(Q), mud weight, hours on bit (HOB), revolutions on bit, bit diameter, total flow area (TFA), pore pressure, overburden pressure, and pit volume). Data-driven models are built using two different machine learning techniques, using 1771 raw real field data. The coding is built using the python programming language. The k-nearest neighbors (KNN) model predicting ROP for the training dataset show a correlation coefficient (R2) of 0.94. The multi-layer perceptron (MLP) model predicting ROP for the training dataset show a correlation coefficient (R2) of 0.98. We can conclude that MLP has a better accuracy, and removing outliers enhances model performance. | ||
| کلیدواژهها | ||
| rate of penetration؛ machine learning؛ drilling؛ multilayer perceptron؛ k-nearest neighbors | ||
| مراجع | ||
|
[1]. Ahmed, A., Ali, A., Elkatatny, S., and Abdulraheem, A. (2019). New artificial neural networks model for predicting rate of penetration in deep shale formation. Sustainability (Switzerland). 11 (22): 6527.
[2]. Salmani, B., Alanazi, B., Al-Askari, M., Masihi, M., and Paiaman, A. (2009). Effect of Drilling Fluid Properties on Rate of Penetration. Nafta Scientific Journal. 60 (3): 129–134.
[3]. Speer, J.W. (1959). A Method for Determining Optimum Drilling Techniques. Gulf Coast Drilling and Production Meeting.
[4]. Cunningham, R.A. (1960). Laboratory Studies of the Effect of Rotary Speed on Rock-bit Performance and Drilling Cost. Drilling and Production Practice.
[5]. Bingham, M.G. (1964). A New Approach to Interpreting Rock Drillability. Petroleum Publishing Company Co.
[6]. Bourgoyne, A.T. and Young, F.S. (1974). A Multiple Regression Approach to Optimal Drilling and Abnormal Pressure Detection. Society of Petroleum Engineers Journal. 14 (4): 371–384.
[7]. Hareland, G. and Rampersad, P.R. (1994). Drag - Bit Model Including Wear. SPE Journal. 657–667.
[8]. Bourgoyne, A.T., Millheim, K.K., Chenevert, M.E., and Young, F.S. (1986). Applied Drilling Engineering. SPE Textbook, 250 P .
[9]. Anemangely, M., Ramezanzadeh, A., Tokhmechi, B., Molaghab, A., and Mohammadian, A. (2018). Drilling rate prediction from petrophysical logs and mud logging data using an optimized multilayer perceptron neural network. Journal of Geophysics and Engineering. 15 (4): 1146–1159.
[10]. Rabia, H. (2002). Well Engineering & Construction Hussain Rabia, 789 P .
[11]. Ataei, M., KaKaie, R., Ghavidel, M., and Saeidi, O. (2015). Drilling rate prediction of an open pit mine using the rock mass drillability index. International Journal of Rock Mechanics and Mining Sciences. 73 130–138.
[12]. Farahat, M.S. (2001). Oil Well Drilling Engineering 2, 200 P .
[13]. Nascimento, A., Tamas Kutas, D., Elmgerbi, A., Thonhauser, G., and Hugo Mathias, M. (2015). Mathematical Modeling Applied to Drilling Engineering: An Application of Bourgoyne and Young ROP Model to a Presalt Case Study. Mathematical Problems in Engineering. 2015.
[14]. Hoseinie, S.H., Ataei, M., and Aghababaie, A. (2014). A laboratory study of rock properties affecting the penetration rate of pneumatic top hammer drills. Journal of Mining & Environment. 5 (1): 25–34.
[15]. Dipl.-Ing and Prassl, W.F. (2014). Drilling Engineering, Curtin University of Technology Department of Petroleum Engineering Master, 274 P.
[16]. Hughes, G.R., Hall, W., Pesenti, J., and Ward, P. (2017). Machine learning : the power and promise of computers that learn by example, 125 P .
[17]. Sah, S. (2020). Machine Learning : A Review of Learning Types.
[18]. Habibi Aghdam, H., Jahani Heravi, E., and AG, S.I.P. (2018). Guide to Convolutional Neural Networks A Practical Application to Traffic-Sign Detection and Classification, Springer, 282 P .
[19]. Swamynathan, M. (2017). Mastering Machine Learning with Python in Six Steps, 358 P.
[20]. Burkov Andriy .(2018). The hundred pages Machine learning. Handbook of Research on Cloud and Fog Computing Infrastructures for Data Science, 152 P.
[21]. Pandey, Y.N., Rastogi, A., Kainkaryam, S., Bhattacharya, S., and Saputelli, L. (2020). Machine Learning in the Oil and Gas Industry, 300 P .
[22]. Ali, J.K. (1994). Neural networks: a new tool for the petroleum industry, Proc, Eur. Pet. Comput. Conf. Soc. Pet. Eng.
[23]. Wu, W., Dandy, G.C., and Maier, H.R. (2014). Protocol for developing ANN models and its application to the assessment of the quality of the ANN model development process in drinking water quality modelling. Environmental Modelling and Software. (54): 108–127.
[24]. Ashrafi, S.B., Anemangely, M., Sabah, M., and Ameri, M.J. (2019). Application of hybrid artificial neural networks for predicting rate of penetration (ROP): A case study from Marun oil field. Journal of Petroleum Science and Engineering. 175 604–623.
[25]. Hazbeh, O., Aghdam, S.K. ye, Ghorbani, H., Mohamadian, N., Ahmadi Alvar, M., and Moghadasi, J. (2021). Comparison of accuracy and computational performance between the machine learning algorithms for rate of penetration in directional drilling well. Petroleum Research. 6 (3): 271–282.
[26]. Anemangely, M., Ramezanzadeh, A., Tokhmechi, B., Molaghab, A., and Mohammadian, A. (2020). Drilling rate prediction from petrophysical logs and mud logging data using optimized MLP neural network. J. Phys. Energy. 2 (1): 0–31.
[27]. Sabah, M., Talebkeikhah, M., Wood, D.A., Khosravanian, R., Anemangely, M., and Younesi, A. (2019). A machine learning approach to predict drilling rate using petrophysical and mud logging data. Earth Science Informatics. 12 (3): 319–339.
[28]. Moran, D., Ibrahim, H., Purwanto, A., and Osmond, J. (2010). Sophisticated ROP prediction technologies based on neural network delivers accurate drill time results, Proc, IADC/SPE Asia Pacific Drill. Technol. Conf. Exhib. Held Ho Chi Minh City, Vietnamm, 1–3 November, 100–108.
[29]. Elkatatny, S. (2018). New Approach to Optimize the Rate of Penetration Using Artificial Neural Network. Arabian Journal for Science and Engineering. 43 (11), 6297–6304.
[30]. Hawkins, D.M. (1980). Identification of Outliers. 1st ed. Springer Dordrecht, 188 P.
[31]. Brenjkar, E., Biniaz Delijani, E., and Karroubi, K. (2021). Prediction of penetration rate in drilling operations: a comparative study of three neural network forecast methods. Journal of Petroleum Exploration and Production. 11 (2), 805–818.
[32]. Zhao, Y., Noorbakhsh, A., Koopialipoor, M., Azizi, A., and Tahir, M.M. (2020). A new methodology for optimization and prediction of rate of penetration during drilling operations. Engineering with Computers. 36 (2), 587–595. | ||
|
آمار تعداد مشاهده مقاله: 457 تعداد دریافت فایل اصل مقاله: 558 |
||