Optimization of drilling penetration rate through the optimal design of drilling leg and mechanical and hydraulic parameters of drilling using energy characteristic method (case study: South Pars gas field)

Subject Areas : Journal of Simulation and Analysis of Novel Technologies in Mechanical Engineering

MohammadReza Nouri ¹ , Mojtaba Rahimi ^{2 *} , Ali Mokhtarian ³

1 - Department of Mechanical, Civil, and Architectural Engineering, Khomeinishahr Branch, Islamic Azad University, Khomeinishahr/Isfahan, Iran
2 - Department of Mechanical, Civil, and Architectural Engineering, Khomeinishahr Branch, Islamic Azad University, Khomeinishahr/Isfahan, Iran
3 - Department of Mechanical, Civil, and Architectural Engineering, Khomeinishahr Branch, Islamic Azad University, Khomeinishahr/Isfahan, Iran

Received: 2024-01-08 Accepted : 2024-05-15 Published : 2025-03-01

Keywords: Rate of penetration (ROP), Optimal design, Mechanical and hydraulic parameters, Energy characteristic method, South Pars gas field.,

Abstract :

Since the first well was drilled to discover underground formations, oil industry professionals have explored many methods to increase the length and stability of the well and hence to achieve higher efficiency. The development of the drilling industry accelerated in the past decades to increase oil production and reduce related costs. The purpose of this study is to describe the existing drilling techniques and to deal with the optimization method for drilling a new well in the South Pars gas field. The South Pars gas field is a common offshore gas condensate field known as the largest gas field in the world, approximately 38% of which is located on the Iranian side. In this research, first a well plan was presented to start the optimization, and then, its final three holes (including 16, 12 ¼, and 8 ½-inch holes) were modeled in Landmark software. During modeling in Landmark, the well profile, BHA (Bottom Hole Assembly), drilling fluid properties, drilling hydraulics, effective drag, and surface torque were simulated and then optimized based on operational constraints (such as pump capacity) and mechanical constraints. Furthermore, the mechanical energy characteristic method was used to study the correlations between the actual rate of penetration (ROP) obtained based on field data and theory, and the artificial neural network was used to optimize the drilling process. Landmark results indicated that the drilling of 16, 12 ¼, and 8 ½-inch holes was limited by the selection of mud characteristics, so the optimal values of plastic viscosity (PV), yield point (YP), revolutions per minute (RPM), and mud pumping volume per minute (GPM) were calculated. For each hole, the results from the modeling and optimization of the artificial neural network showed an excellent correlation between drilling parameters and ROP for the 12 ¼ and 8 ½-inch sections (R-Train, R-Test, R-Validation, and R-All were all larger than 0.99 for these sections), while the correlation was very good for the 16-inch section (the above parameters were all around 0.92 for this section). The results of this study can be applied to a real drilling process to maximize drilling efficiency.

References:

[1] Wilson, D. C., & Bentsen, R. G. (1972, October). Optimization techniques for minimizing drilling costs. In SPE Annual Technical Conference and Exhibition? (pp. SPE-3983). SPE.
[2] Bourgoyne Jr, A. T., & Young Jr, F. S. (1974). A multiple regression approach to optimal drilling and abnormal pressure detection. Society of Petroleum Engineers Journal, 14(04), 371-384.
[3] Eren, T., & Ozbayoglu, M. E. (2010, January). Real time optimization of drilling parameters during drilling operations. In SPE Oil and Gas India Conference and Exhibition (pp. SPE-129126). SPE.
[4] Warren, T. M. (1981). Drilling model for soft-formation bits. Journal of Petroleum Technology, 33(06), 963-970.
[5] Bizanti, M. S., & Blick, E. F. (1986, March). Fluid dynamics of wellbore bottomhole cleaning. In SPE Permian Basin Oil and Gas Recovery Conference (pp. SPE-15010). SPE.
[6] Warren, T. M. (1987). Penetration-rate performance of roller-cone bits. SPE Drilling Engineering, 2(01), 9-18.
[7] Maidla, E. E., & Ohara, S. (1991). Field Verification of Drilling Models and Computerized Selection off Drill Bit, WOB, and Drillstring Rotation. SPE Drilling Engineering, 6(03), 189-195.
[8] Hareland, G., & Hoberock, L. L. (1993, February). Use of drilling parameters to predict in-situ stress bounds. In SPE/IADC Drilling Conference and Exhibition (pp. SPE-25727). SPE.
[9] Samuel, G. R., & Miska, S. (1998, September). Optimization of drilling parameters with the performance of multilobe positive displacement motor (PDM). In IADC/SPE Asia Pacific Drilling Technology Conference and Exhibition (pp. SPE-47791). SPE.
[10] Pereira, J. J. (1999). Comprehensive optimization of drilling parameters for horizontal wells. The University of Tulsa.
[11] Li, Y., & Kuru, E. (2004, March). Optimization of hole cleaning in vertical wells using foam. In SPE Western Regional Meeting (pp. SPE-86927). SPE.
[12] Ogunrinde, J. O., & Dosunmu, A. (2012, August). Hydraulics optimization for efficient hole cleaning in deviated and horizontal wells. In SPE Nigeria Annual International Conference and Exhibition (pp. SPE-162970). SPE.
[13] Wang, Y. (2014). Drilling Hydraulics Optimization Using Neural Networks. University of Louisiana at Lafayette.
[14] Alsubaih, A., Albadran, F., & Alkanaani, N. (2018, January). Mechanical specific energy and statistical techniques to maximizing the drilling rates for production section of mishrif wells in southern Iraq fields. In SPE/IADC Middle East Drilling Technology Conference and Exhibition (p. D031S002R003). SPE.
[15] Robinson, T. S., Batruny, P., Gomes, D., Hashim, M. M. H. M., Yusoff, M. H., Arriffin, M. F., & Mohamad, A. (2022, March). Successful Development and Deployment of a Global ROP Optimization Machine Learning Model. In Offshore Technology Conference Asia (p. D021S009R001). OTC.
[16] Abdelaal, K., Atere, K., LeRoy, K., Eddy, A., & Smith, R. (2022, March). Holistic Real-Time Drilling Parameters Optimization Delivers Best-in-Class Drilling Performance and Preserves Bit Condition-A Case History from an Integrated Project in the Middle East. In SPE Canadian Energy Technology Conference. OnePetro.
[17] Hashemizadeh, A., Bahonar, E., Chahardowli, M., Kheirollahi, H., & Simjoo, M. (2022). A data-driven approach to estimate the rate of penetration in drilling of hydrocarbon reservoirs.
[18] South Pars Drilling Program, Pars Oil and Gas Company.
[19] Galle, E. M., & Woods, H. B. (1963). Best Constant Weight And Potary Speed For Rotary Rock Bits.
[20] Hareland, G., & Rampersad, P. R. (1994, April). Drag-bit model including wear. In SPE Latin America and Caribbean Petroleum Engineering Conference (pp. SPE-26957). SPE.
[21] Hareland, G., Nygaard, R., Virginillo, B., & Munro, H. (2007, April). Drilling Simulation vs. Actual Performance in Western Canada. In SPE Rocky Mountain Petroleum