A review on half a century of experience in rate of penetration management: Application of analytical, semi-analytical and empirical models

Abstract

Rate of penetration management is a matter of importance in drilling operations and it has been used in some research studies. Although conventional approaches for rate of penetration management are mainly focused on analytical and semi-analytical models, several correlations have also been developed for this purpose. The history of rate of penetration management studies extends back more than half a century and ever since, research interest in this concept has never declined, making it a focus of industry and academic studies. In this article, some of these studies are reviewed to achieve a better understanding of rate of penetration management concept, its financial benefits and also its research capacities. This review reveals the most common rate of penetration management methods which applied analytical, semi-analytical and empirical correlations in different fields around the world. In other words, the main purpose of this study is to evaluate the research studies in which different models and correlations have been used as the main approach for rate of penetration management. Based on the results of this review, the best models for performing rate of penetration management studies and the best objective functions for optimization algorithms are introduced.

Cited as: Najjarpour, M., Jalalifar, H., Norouzi-Apourvari, S. A review on half a century of experience in rate of penetration management: Application of analytical, semi-analytical and empirical models. Advances in Geo-Energy Research, 2021, 5(3): 252-273, doi: 10.46690/ager.2021.03.03

References

Ahmed, A., Elkatatny, S., Ali, A., et al. Rate of penetration prediction in shale formation using fuzzy logic. Paper IPTC-19548 Presented at the International Petroleum Technology Conference, Beijing, China, 26-28 March, 2019.

Akbari, B., Butt, S., Munaswamy, K., et al. Dynamic single pdc cutter rock drilling modeling and simulations focusing on rate of penetration using distinct element method. Paper ARMA-11-379 Presented at the 45th U.S. Rock Mechanics / Geomechanics Symposium, San Francisco, California, 26-29 June, 2011.

Al-AbdulJabbar, A., Elkatatny, S., Abdulhamid Mahmoud, A., et al. Prediction of the rate penetration while drilling horizontal carbonate reservoirs using a self-adaptive artificial neural network technique. Sustainability, 2020, 12(4): 1376.

Al-AbdulJabbar, A., Elkatatny, S., Mahmoud, M., et al. A robust rate of penetration model for carbonate formation. Journal of Energy Resources Technology, 2019, 141(4): 042903.

AlArfaj, I., Khoukhi, A., Eren, T. Application of advanced computational intelligence to rate of penetration prediction. Paper INSPEC 13244616 Presented at Computer Modeling and Simulation (EMS), 2012 Sixth UKSim/AMSS European Symposium on, Malta, Malta, 14-16 November, 2012.

Amar, K., Ibrahim, A. Rate of penetration prediction and optimization using advances in artificial neural networks, a comparative study. Paper INSPEC 13244616 Presented at 4th International Joint Conference on Computational Intelligence, 14-16 November, 2012.

Amer, M. M., Dahab, A. S., El-Sayed, A. -A. H. An rop predictive model in nile delta area using artificial neural networks. Paper SPE 187969 Presented at SPE Kingdom of Saudi Arabia Annual Technical Symposium and Exhibition, Dammam, Saudi Arabia, 24-27 April, 2017.

Anemangely, M., Ramezanzadeh, A., Tokhmechi, B., et al. Drilling rate prediction from petrophysical logs and mud logging data using an optimized multilayer perceptron neural network. Journal of Geophysics and Engineering, 2018, 15(4): 1146-1159.

Arabjamaloei, R., Karimi Dehkordi, B. Investigation of the most efficient approach of the prediction of the rate of penetration. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 2012, 34(7): 581-590.

Arabjamaloei, R., Shadizadeh, S. Modeling and optimizing rate of penetration using intelligent systems in an iranian southern oil field (ahwaz oil field). Petroleum Science and Technology, 2011, 29(16): 1637-1648.

Awotunde, A. A., Mutasiem, M. A. Efficient drilling time optimization with differential evolution. Paper SPE 172419 Presented at SPE Nigeria Annual International Conference and Exhibition, Lagos, Nigeria, 5-7 August, 2014.

Ayoub, M., Shien, G., Diab, D., et al. Modeling of drilling rate of penetration using adaptive neuro-fuzzy inference system. International Journal of Applied Engineering Research, 2017, 12(22): 12880-12891.

Bahari, A., Baradaran Seyed, A. Drilling cost optimization in iranian khangiran gas field. Paper SPE 108246 Presented at International Oil Conference and Exhibition in Mexico, Veracruz, Mexico, 27-30 June, 2007a.

Bahari, A., Baradaran Seyed, A. Trust-region approach to find constants of bourgoyne and young penetration rate model in khangiran iranian gas field. Paper SPE 107520 Presented at Latin American & Caribbean Petroleum Engineering Conference, Buenos Aires, Argentina, 15-18 April, 2007b.

Bahari, M. H., Bahari, A., Moradi, H. Intelligent drilling rate predictor. International Journal of Innovative Computing, Information and Control, 2011, 7(2): 1511-1520.

Bani Mustafa, A., Abbas, A. K., Alsaba, M., et al. Improving drilling performance through optimizing controllable drilling parameters. Journal of Petroleum Exploration and Production, 2021, 11(3): 1223-1232.

Barbosa, L. F. F. M., Nascimento, A., Mathias, M. H., et al. Machine learning methods applied to drilling rate of penetration prediction and optimization-a review. Journal of Petroleum Science and Engineering, 2019, 183: 106332.

Bataee, M., Irawan, S., Kamyab, M. Artificial neural network model for prediction of drilling rate of penetration and optimization of parameters. Journal of the Japan Petroleum Institute, 2014, 57(2): 65-70.

Bataee, M., Kamyab, M., Ashena, R. Investigation of various rop models and optimization of drilling parameters for pdc and roller-cone bits in shadegan oil field. Paper SPE 130932 Presented at International Oil and Gas Conference and Exhibition in China, Beijing, China, 8-10 June, 2010.

Bezminabadi, S. N., Ramezanzadeh, A., Jalali, S. -M. E., et al. Effect of rock properties on rop modeling using statistical and intelligent methods: A case study of an oil well in southwest of iran. Archives of Mining Sciences, 2017, 62(1): 131-144.

Bourgoyne, A. T., Young, F. A multiple regression approach to optimal drilling and abnormal pressure detection. Society of Petroleum Engineers Journal, 1974, 14(4): 371-384.

Busahmin, B., Saeid, N. H., Alusta, G., et al. Review on hole cleaning for horizontal wells. Journal of Engineering and Applied Sciences, 2017, 12(16): 4697.

Caicedo, H., Calhoun, W., Ewy, R. Unique bit performance predictor using specific energy coefficients as a function of confined compressive strength impacts drilling performance. Paper SPE WPC 18 Presented at 18th World Petroleum Congress, Johannesburg, South Africa, 25-29 September, 2005.

Chia, R., Smith, R. A new nozzle system to achieve high rop drilling. Paper SPE 15518 Presented at SPE Annual Technical Conference and Exhibition, New Orleans, Louisiana, 5-8 October, 1986.

Cho, H., Shah, S., Osisanya, S. A three-segment hydraulic model for cuttings transport in coiled tubing horizontal and deviated drilling. Journal of Canadian Petroleum Technology, 2002, 41(6): 32-39.

Conn, A. R., Gould, N. I., Toint, P. L. Trust Region Methods. Philadelphia, USA, Siam, 2000.

Diaz, M. B., Kim, K. Y., Kang, T. -H., et al. Drilling data from an enhanced geothermal project and its pre-processing for rop forecasting improvement. Geothermics, 2018, 72: 348-357.

Duklet, C. P., Bates, T. R. An empirical correlation to predict diamond bit drilling rates. Paper SPE 9324 Presented at SPE Annual Technical Conference and Exhibition, Dallas, Texas, 21-24 September, 1980.

Eberhart, R. C., Shi, Y. Comparison between genetic algorithms and particle swarm optimization. Paper Presented at International conference on evolutionary programming, San Diego, CA, USA, 25-27 March, 1998.

Eckel, J. R. Microbit studies of the effect of fluid properties and hydraulics on drilling rate. Journal of Petroleum Technology, 1967, 19(4): 541-546.

Elahifar, B., Thonhauser, G., Fruhwirth, R. K., et al. Rop modeling using neuralnetwork and drill string vibration data. Paper SPE 163330 Presented at SPE Kuwait International Petroleum Conference and Exhibition, Kuwait City, Kuwait, 10-12 December, 2012.

Elkatatny, S. New approach to optimize the rate of penetration using artificial neural network. Arabian Journal for Science and Engineering, 2017, 43(11): 6297-6304.

Elkatatny, S. Development of a new rate of penetration model using self-adaptive differential evolution-artificial neural network. Arabian Journal of Geosciences, 2019, 12(2): 19.

Elkatatny, S. Real-time prediction of rate of penetration in s-shape well profile using artificial intelligence models. Sensors, 2020, 20(12): 3506.

Elkatatny, S., Al-AbdulJabbar, A., Abdelgawad, K. A new model for predicting rate of penetration using an artificial neural network. Sensors, 2020, 20(7): 2058.

Eren, T., Ozbayoglu, M. E. Real time optimization of drilling parameters during drilling operations. Paper SPE 129126 Presented at the SPE Oil and Gas India Conference and Exhibition, Mumbai, India, 20-22 January, 2010.

Etesami, D., Shirangi, M., Zhang, W. A semiempirical model for rate of penetration with application to an offshore gas field. SPE Drilling & Completion, 2021, 36(1): 29-46.

Fear, M. How to improve rate of penetration in field operations. SPE Drilling & Completion, 1999, 14(1): 42-49.

Formighieri, S., Freitas, P. J. d. F. Estimation of bourgoyne and young model coefficients using markov chain monte carlo simulation. Paper INSPEC 15800161 Presented at 2015 Winter Simulation Conference (WSC), Huntington Beach, CA, USA, 6-9 December, 2015.

Galle, E., Woods, H. Best constant weight and rotary speed for rotary rock bits. Paper API 63 Presented at Drilling and Production Practice, New York, USA, 1 January, 1963.

Guria, C., Goli, K. K., Pathak, A. K. Multi-objective optimization of oil well drilling using elitist non-dominated sorting genetic algorithm. Petroleum Science, 2014, 11(1): 97-110.

Hadi, F., Altaie, H., AlKamil, E. Modeling rate of penetration using artificial intelligent system and multiple regression analysis. Paper SPE 197663 Presented at the Abu Dhabi International Petroleum Exhibition & Conference, Abu Dhabi, UAE, 11-14 November, 2019.

Hareland, G., Hoberock, L. Use of drilling parameters to predict in-situ stress bounds. Paper SPE 25727 Presented at SPE/IADC Drilling Conference, Amsterdam, Netherlands, 22-25 February, 1993.

Hareland, G., Nygaard, R. Calculating unconfined rock strength from drilling data. Paper ARMA 07 Presented at 1st Canada-US Rock Mechanics Symposium, Vancouver, Canada, 27-31 May, 2007.

Hareland, G., Rampersad, P. R. Drag-bit model including wear. Paper SPE 26957 Presented at the SPE Latin America/Caribbean Petroleum Engineering Conference, Buenos Aires, Argentina, 27-29 April, 1994.

Hareland, G., Wu, A., Rashidi, B. A drilling rate model for roller cone bits and its application. Paper SPE 129592 Presented at International Oil and Gas Conference and Exhibition in China, Beijing, China, 8-10 June, 2010.

Hassan, K. H., Hussien, H. A. H. A. Optimization of drilling parameters with aid of real time data for buzargan oil field. IOP Conference Series: Materials Science and Engineering, 2019, 579: 012003.

Hegde, C., Daigle, H., Millwater, H., et al. Analysis of rate of penetration (rop) prediction in drilling using physics-based and data-driven models. Journal of Petroleum Science and Engineering, 2017, 159: 295-306.

Hegde, C., Gray, K. E. Use of machine learning and data analytics to increase drilling efficiency for nearby wells. Journal of Natural Gas Science and Engineering, 2017, 40: 327-335.

Hegde, C., Millwater, H., Pyrcz, M., et al. Rate of penetration (rop) optimization in drilling with vibration control. Journal of Natural Gas Science and Engineering, 2019, 67: 71-81.

Hegde, C., Soares, C., Gray, K. Rate of penetration (rop) modeling using hybrid models: Deterministic and machine learning. Paper Presented at Unconventional Resources Technology Conference, Houston, Texas, 23-25 July, 2018.

Hegde, C., Wallace, S., Gray, K. Using trees, bagging, and random forests to predict rate of penetration during drilling. Paper SPE 176792 Presented at the SPE Middle East Intelligent Oil and Gas Conference and Exhibition, Abu Dhabi, UAE, 15-16 September, 2015.

Holland, J. H. Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. Massachusetts, USA, The MIT Press, 1992.

Jiang, W., Samuel, R. Optimization of rate of penetration in a convoluted drilling framework using ant colony optimization. Paper SPE 178847 Presented at IADC/SPE Drilling Conference and Exhibition, Fort Worth, Texas, USA, 1-3 March, 2016.

Kerkar, P. B., Hareland, G., Fonseca, E. R., et al. Estimation of rock compressive strength using downhole weight-on-bit and drilling models. Paper IPTC 17447 Presented at IPTC 2014: International Petroleum Technology Conference, Doha, Qatar, 19-22 January, 2014.

Khosravanian, R., Choodar, B., Wood, D. A., et al. Rop fuzzy-logic model proposed for intelligent drilling in iran, malaysia. Oil & Gas Journal, 2016, 114(11): 58-61.

Kor, K., Altun, G. Is support vector regression method suitable for predicting rate of penetration? Journal of Petroleum Science and Engineering, 2020, 194: 107542.

Kutas, D., Nascimento, A., Elmgerbi, A., et al. A study of the applicability of bourgoyne & young rop model and fitting reliability through regression. Paper IPTC 18521 Presented at International Petroleum Technology Conference, Doha, Qatar, 6-9 December, 2015.

Liu, Z., Marland, C., Li, D., et al. An analytical model coupled with data analytics to estimate pdc bit wear. Paper SPE 169451 Presented at SPE Latin America and Caribbean Petroleum Engineering Conference, Maracaibo, Venezuela, 21-23 May, 2014.

Maidla, E., Ohara, S. Field verification of drilling models and computerized selection of drill bit, wob, and drillstring rotation. SPE Drilling Engineering, 1991, 6(3): 189-195.

Mammadov, E., Osayande, N., Breuer, J., et al. Predicting and optimizing rop in competent shale by utilizing mpd technology. Paper SPE 174805 Presented at SPE Annual Technical Conference and Exhibition, Houston, Texas, USA, 28-30 September, 2015.

Manshad, A. K., Rostami, H., Toreifi, H., et al. Improvement of drilling penetration rate in oil fields using a pso-ga-mlp hybrid network, in Heavy Oil, edited by A. H. Mohammadi, Nova Science Publishers, New York, pp. 271-284, 2017.

Mantha, B., Samuel, R. Rop optimization using artificial intelligence techniques with statistical regression coupling. Paper SPE 181382 Presented at SPE Annual Technical Conference and Exhibition, Dubai, UAE, 26-28 September, 2016.

Mathis, W., Thonhauser, G., Wallnoefer, G., et al. Use of real-time rig-sensor data to improve daily drilling reporting, benchmarking, and planning—a case study. SPE Drilling & Completion, 2007, 22(3): 217-226.

Maurer, W. The “perfect-cleaning” theory of rotary drilling. Journal of Petroleum Technology, 1962, 14(11): 1270-1274.

Mensa-Wilmot, G., Langdon, S. P., Harjadi, Y. Drilling efficiency and rate of penetration: Definitions, influencing factors, relationships, and value. Paper SPE 128288 Presented at IADC/SPE Drilling Conference and Exhibition, New Orleans, Louisiana, USA, 2-4 February, 2010.

Moradi, H., Bahari, M. H., Sistani, M. B. N., et al. Drilling rate prediction using an innovative soft computing approach. Scientific Research and Essays, 2010, 5(13): 1583-1588.

Moré, J. J., Sorensen, D. C. Computing a trust region step. SIAM Journal on Scientific and Statistical Computing, 1983, 4(3): 553-572.

Mostofi, M., Shahbazi, K., Rahimzadeh, H., et al. Drilling optimization based on the rop model in one of the iranian oil fields. Paper SPE 131349 Presented at International Oil and Gas Conference and Exhibition in China, Beijing, China, 8-10 June, 2010.

Motahhari, H. R., Hareland, G., James, J. Improved drilling efficiency technique using integrated pdm and pdc bit parameters. Journal of Canadian Petroleum Technology, 2010, 49(10): 45-52.

Najjarpour, M., Jalalifar, H. Optimization of fairhurst-cook model for 2-d wing cracks using ant colony optimization (aco), particle swarm intelligence (pso), and genetic algorithm (ga). Journal of Applied Mathematics and Physics, 2018, 6(8): 1581.

Najjarpour, M., Jalalifar, H., Apourvari, S. N. The effect of formation thickness on the performance of deterministic and machine learning models for rate of penetration management in inclined and horizontal wells. Journal of Petroleum Science and Engineering, 2020, 191: 107160.

Nascimento, A., Tamas Kutas, D., Elmgerbi, A., et al. Mathematical modeling applied to drilling engineering: An application of bourgoyne and young rop model to a presalt case study. Mathematical Problems in Engineering, 2015, 2015: 631290.

Nygaard, R., Hareland, G. Application of rock strength in drilling evaluation. Paper SPE 106573 Presented at Latin American & Caribbean Petroleum Engineering Conference, Buenos Aires, Argentina, 15-18 April, 2007.

Osgouei, R. E., Özbayoğlu, M. Rate of penetration estimation model for directional and horizontal wells. Middle East Technical University, Ankara, Turkey, 2007.

Mirzaei-Paiaman, A., Al-Askari, M., Salmani, B., et al. Effect of drilling fluid properties on rate of penetration. Nafta, 2009, 60(3): 129-134.

Peterson, J. Diamond drilling model verified in field and laboratory tests. Journal of Petroleum Technology, 1976, 28(2): 213-222.

Rahimzadeh, H., Mostofi, M., Hashemi, A. A new method for determining bourgoyne and young penetration rate model constants. Petroleum Science and Technology, 2011, 29(9): 886-897.

Rahmanifard, H., Plaksina, T. Application of artificial intelligence techniques in the petroleum industry: A review. Artificial Intelligence Review, 2018, 52(4): 2295-2318.

Rampersad, P., Hareland, G., Boonyapaluk, P. Drilling optimization using drilling data and available technology. Paper SPE 27034 Presented at SPE Latin America/-Caribbean Petroleum Engineering Conference, Buenos Aires, Argentina, 27-29 April, 1994.

Rastegar, M., Hareland, G., Nygaard, R., et al. Optimization of multiple bit runs based on rop models and cost equation: A new methodology applied for one of the persian gulf carbonate fields. Paper SPE 114665 Presented at IADC/SPE Asia Pacific Drilling Technology Conference and Exhibition, Jakarta, Indonesia, 25-27 August, 2008.

Seifabad, M. C., Ehteshami, P. Estimating the drilling rate in ahvaz oil field. Journal of Petroleum Exploration and Production Technology, 2013, 3(3): 169-173.

Shi, X., Liu, G., Gong, X., et al. An efficient approach for real-time prediction of rate of penetration in offshore drilling. Mathematical Problems in Engineering, 2016, 2016: 3575380.

Shirkavand, F., Hareland, G., Aadnoy, B. Rock mechanical modelling for a underbalanced drilling rate of penetration prediction. Paper ARMA 09 Presented at 43rd US Rock Mechanics Symposium & 4th US-Canada Rock Mechanics Symposium, Asheville, North Carolina, 28 June-1 July, 2009.

Soares, C., Daigle, H., Gray, K., et al. Evaluation of pdc bit rop models and the effect of rock strength on model coefficients. Journal of Natural Gas Science and Engineering, 2016, 34: 1225-1236.

Soares, C., Gray, K. Real-time predictive capabilities of analytical and machine learning rate of penetration (rop) models. Journal of Petroleum Science and Engineering, 2019, 172: 934-959.

Sorensen, D. Trust-region methods for unconstrained minimization. Paper Presented at NATO Advanced Research Institute conference on nonlinear optimization, Cambridge, UK, 13 July, 1981.

Sui, D., Nyboe, R., Azizi, V. Real-time optimization of rate of penetration during drilling operation. Paper INSPEC 13679818 Presented at 2013 10th IEEE International Conference on Control and Automation (ICCA), Hangzhou, China, 12-14 June, 2013.

Tewari, S., Dwivedi, U. D. A novel neural network framework for the prediction of drilling rate of penetration. Paper Presented at APCEC 17 International Conference on Advances in Petroleum , Chemical & Energy Challenges, RGIPT, Amethi, Jais, Uttar Pradesh, India, March, 2017.

Walker, B., Black, A., Klauber, W., et al. Roller-bit penetration rate response as a function of rock properties and well depth. Paper SPE 15620 Presented at SPE Annual Technical Conference and Exhibition, New Orleans, Louisiana, 5-8 October, 1986.

Warren, T. Drilling model for soft-formation bits. Journal of Petroleum Technology, 1981, 33(6): 963-970.

Warren, T. Penetration rate performance of roller cone bits. SPE Drilling Engineering, 1987, 2(1): 9-18.

Whitley, D. A genetic algorithm tutorial. Statistics and Computing, 1994, 4(2): 65-85.

Wiktorski, E., Kuznetcov, A., Sui, D. Rop optimization and modeling in directional drilling process. Paper SPE 185909 Presented at the SPE Bergen One Day Seminar, Bergen, Norway, 5 April, 2017.

Winters, W., Warren, T., Onyia, E. Roller bit model with rock ductility and cone offset. Paper SPE 16696 Presented at SPE Annual Technical Conference and Exhibition, Dallas, Texas, 27-30 September, 1987.

Wojtanowicz, A. K., Kuru, E. Dynamic drilling strategy for pdc bits. Paper SPE 16118 Presented at SPE/IADC Drilling Conference, New Orleans, Louisiana, 15-18 March, 1987.

Xiao, L., Zhang, T. A proximal stochastic gradient method with progressive variance reduction. SIAM Journal on Optimization, 2014, 24(4): 2057-2075.

Yavari, H., Sabah, M., Khosravanian, R., et al. Application of an adaptive neuro-fuzzy inference system and mathematical rate of penetration models to predicting drilling rate. Iranian Journal of Oil & Gas Science and Technology, 2018, 7(3): 73-100.

Yi, P., Kumar, A., Samuel, R. Realtime rate of penetration optimization using the shuffled frog leaping algorithm. Journal of Energy Resources Technology, 2015, 137(3): 032902.