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A Review of Metaheuristic Algorithms for Optimizing 3D Well-Path Designs

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Abstract

Due to a variety of possible good types and so many complex drilling variables and constraints, optimization of the trajectory of a complex wellbore is very challenging. There are several types of well such as directional wells, horizontal wells, redrilling wells, complex structure wells, cluster wells, and extended reach wells etcetera. Over the past few years, the number of unconventional well including deviated well, highly deviated well is steadily increasing. Directional drilling has some advantages over vertical drilling though it is more expensive. In drilling engineering optimization of wellbore plays an important role, which can be optimized based on the minimization of the length of wellbore, minimization of mud pressure, critical pressure etcetera. Till today so many approaches or methods used to optimize this wellbore trajectory. From those methods here in this study we focused on metaheuristic approaches that used to optimize wellbore trajectory. This reduction of the wellbore length helps to establish cost-effective approaches that can be utilized to resolve a group of complex trajectory optimization challenges. For efficient performance (i.e.; quickly locating global optima while taking the smallest amount of computational time) we have to identify flexible control parameters. Later this parameter can be effectively fixed to tune different algorithms. This research will develop a review of the various algorithm used to optimize deviated wellbore trajectories. In the environment of challenging commodity prices, engineers and commercial firms bound to utilize technology to find out a low-cost solution. Technology is also helpful for the automatic solution of a complex problem, which also allows for the better utilization of resources. This review paper helps to find out optimal wellbore trajectory optimization algorithms that can close the technology gap by giving a useful method. This method can generate a solution automatically. Before establishing a different algorithm, those previous authors considered a number of factors and limitations. Safety is one of them. Before drilling depleted zones should be well studied, otherwise, it may be the reason for so many problems. The heuristic approach builds up solutions through the trial and error method. Those methods take a decent amount of time for solving a complex problem. For a single known problem, different metaheuristic approaches take a different amount of time to find the optimal solution. Among different metaheuristic algorithm which was used to find minimum true measured depth (TMD), we try to make a review where we focused on their computational time, minimization of length, method, etcetera.

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Abbreviations

A:

Curvature of the curve

ABC:

Artificial bee colony

ACO:

Ant colony optimization

AI:

Artificial intelligence

HS:

Harmonic search

CCA:

Cheetah chase algorithm

BFO:

Bat flight algorithm

CSO:

Cuckoo search optimization

D1, D2, D3:

Three segments for build and drop

D KOP :

True vertical depth of kickoff point, M

D v1 :

True vertical depth after at end of first hold section

D v2 :

True vertical depth after at end of drop section, M

DLS:

Dog leg severity

EA:

Evolutionary algorithm

FSQGA:

Frequency search quantum genetic algorithm

GA:

Genetic algorithm

GMMPR:

Global minimum mud pressure

HCSO:

Hybrid cuckoo search method

HD:

Horizontal section M

HMCR:

Harmony memory considering rate

HMS:

Harmony memory size

KOP:

Kick of point

MMPR:

Minimum mud pressure required

MOGA:

Multi-objective genetic algorithm

MR:

Mutation rate

PSO:

Particle swarm optimization

QGA:

Quantum genetic algorithm

T:

Dogleg severity

TD:

Torque and drag

TMD:

True measured depth

TVD:

True vertical depth

\(\theta_{1}\) :

Azimuth angle at kickoff point, degrees

\(\theta_{2}\) :

Azimuth angle at end of first build section, degrees

\(\theta_{3}\) :

Azimuth angle at end of first hold section, degrees

\(\theta_{4}\) :

Azimuth angle at end of drop section, degrees

\(\theta_{5}\) :

Azimuth angle at end of second hold section, degrees

\(\theta_{6}\) :

Azimuth angle at end of second build section, degree

\(\varnothing_{1} \varnothing_{2} \varnothing_{3}\) :

First, second, and third hold angles, degrees

References

  1. Dalzell J (2013) Conventional natural gas supply costs in Western Canada. Canadian Energy Research Institute

  2. Pratt SJG (2004) A fresh angle on oil drilling. Geotimes 49(3):18–21

    Google Scholar 

  3. Joshi S (2003) Cost/benefits of horizontal wells. In: SPE western regional/AAPG Pacific section joint meeting, 2003: Society of Petroleum Engineers

  4. Karimpour K, Zarghami R, Moosavian MA, Bahmanyar H (2016) New fuzzy model for risk assessment based on different types of consequences. Oil Gas Sci Technol-Revue d’IFP Energies nouvelles 71(1):17

    Google Scholar 

  5. Short JA (1993) Introduction to directional and horizontal drilling. Pennwell Corporation, Tulsa

    Google Scholar 

  6. Miska S, Skalle P (1981) Theoretical description of a new method of optimal program design. Soc Pet Eng J 21(4):425–434

    Google Scholar 

  7. Amara M, Martin B (1990) The offshore directional drilling advisor: an expert system for directional drilling optimization. In: SPE annual technical conference and exhibition, 1990: Society of Petroleum Engineers

  8. Rampersad P, Hareland G, Pairintra T (1993) Drilling optimization of an oil or gas field. In: SPE eastern regional meeting, 1993: Society of Petroleum Engineers

  9. Helmy MW, Khalaf F, Darwish TA (1998) Well design using a computer model. SPE Drill Complet 13(1):42–46

    Google Scholar 

  10. Shokir EEM, Emera MK, Eid SM, Wally AW (2004) A new optimization model for 3D well design. Oil Gas Sci Technol 59(3):255–266

    Google Scholar 

  11. Awal M, Khan M, Mohiuddin M, Abdulraheem A, Azeemuddin M (2001) A new approach to borehole trajectory optimisation for increased hole stability. In: SPE middle east oil show, 2001: Society of Petroleum Engineers

  12. Vasant P, DeMarco A (2015) Handbook of research on artificial intelligence techniques and algorithms. Information Science Reference

  13. Vasant P, Vasant P (2012) Meta-heuristics optimization algorithms in engineering. Business, Economics, and Finance, IGI Global, p 734

  14. Guria C, Goli KK, Pathak AK (2014) Multi-objective optimization of oil well drilling using elitist non-dominated sorting genetic algorithm. Pet Sci 11(1):97–110

    Google Scholar 

  15. Atashnezhad A, Wood DA, Fereidounpour A, Khosravanian R (2014) Designing and optimizing deviated wellbore trajectories using novel particle swarm algorithms. J Nat Gas Sci Eng 21:1184–1204

    Google Scholar 

  16. Bianchi L, Dorigo M, Gambardella LM, Gutjahr WJ (2009) A survey on metaheuristics for stochastic combinatorial optimization. Nat Comput 8(2):239–287

    MathSciNet  MATH  Google Scholar 

  17. Yang X-S (2009) Harmony search as a metaheuristic algorithm. In: Music-inspired harmony search algorithm. Springer, 2009, pp 1–14

  18. Polya G (1945) How to solve it. Princeton University Press, New Jersey

    MATH  Google Scholar 

  19. Gallagher K, Sambridge M (1994) Genetic algorithms: a powerful tool for large-scale nonlinear optimization problems. Comput Geosci 20(7–8):1229–1236

    Google Scholar 

  20. Yang X-S (2010) Nature-inspired metaheuristic algorithms. Luniver Press, Bristol

    Google Scholar 

  21. Ahmed KA, El-Tayeb S, Dahab A, Khalf F (1999) 3D well design using computer optimization model. In: Symposium of SPE Egyptian section for computer application, Egypt

  22. Mansouri V, Khosravanian R, Wood DA, Aadnoy BS (2015) 3-D well path design using a multi objective genetic algorithm. J Nat Gas Sci Eng 27:219–235

    Google Scholar 

  23. Wood DA (2016) Hybrid bat flight optimization algorithm applied to complex wellbore trajectories highlights the relative contributions of metaheuristic components. J Nat Gas Sci Eng 32:211–221

    Google Scholar 

  24. Kasravi J, Safarzadeh MA, Hashemi A (2017) A population-feedback control based algorithm for well trajectory optimization using proxy model. J Rock Mech Geotech Eng 9(2):281–290

    Google Scholar 

  25. Sha L, Pan Z (2018) FSQGA based 3D complexity wellbore trajectory optimization. Oil Gas Sci Technol-Revue d’IFP Energies nouvelles 73:79

    Google Scholar 

  26. Khosravanian R, Mansouri V, Wood DA, Alipour MR (2018) A comparative study of several metaheuristic algorithms for optimizing complex 3-D well-path designs. J Pet Explor Prod Technol 8(4):1487–1503

    Google Scholar 

  27. Suryanarayana PV, McCann RC, Rudolf RL, Rupani RA (1998) Mathematical technique improves directional well-path planning. Oil Gas J 96:57–63

    Google Scholar 

  28. Xiushan L, Zaihong S, Sen F (1997) Natural parameter method accurately calculates well bore trajectory. Oil Gas J 95(4):90–92

    Google Scholar 

  29. Adams N, Charrier T (1985) Drilling engineering: a complete well planning approach. Pennwell Corp, Tulsa

    Google Scholar 

  30. Dorigo M, Stützle T (2019) Ant colony optimization: overview and recent advances. In: Handbook of metaheuristics. Springer, 2019, pp 311–351

  31. Darquennes D (2005) Implementation and applications of ant colony algorithms. Facultées Universitaires Notre-Dame de la Paix, Namur Institut d’Informatique, vol 40

  32. Hu X-M, Zhang J, Li Y (2008) Orthogonal methods based ant colony search for solving continuous optimization problems. J Comput Sci Technol 23(1):2–18

    Google Scholar 

  33. Guan Z-C, Liu Y-M, Liu Y-W, Xu Y-Q (2016) Hole cleaning optimization of horizontal wells with the multi-dimensional ant colony algorithm. J Nat Gas Sci Eng 28:347–355

    Google Scholar 

  34. Socha K, Dorigo M (2008) Ant colony optimization for continuous domains. Eur J Oper Res 185(3):1155–1173

    MathSciNet  MATH  Google Scholar 

  35. Blum C (2005) Ant colony optimization: introduction and recent trends. Phys Life Rev 2(4):353–373

    Google Scholar 

  36. Hatampour A, Razmi R, Sedaghat MH (2013) Improving performance of a neural network model by artificial ant colony optimization for predicting permeability of petroleum reservoir rocks. Middle East J Sci Res 13(9):1217–1223

    Google Scholar 

  37. Davis L (1991) Handbook of genetic algorithms. Van Nostrand Reinhold, New York

    Google Scholar 

  38. Reeves CR (1997) Genetic algorithms for the operations researcher. INFORMS J Comput 9(3):231–250

    MathSciNet  MATH  Google Scholar 

  39. Sevaux M, Dauzère-Pérès S (2003) Genetic algorithms to minimize the weighted number of late jobs on a single machine. Eur J Oper Res 151(2):296–306

    MathSciNet  MATH  Google Scholar 

  40. Pongcharoen P, Hicks C, Braiden P (2004) The development of genetic algorithms for the finite capacity scheduling of complex products, with multiple levels of product structure. Eur J Oper Res 152(1):215–225

    MathSciNet  MATH  Google Scholar 

  41. Vijande J, Piñeiro MM, Mosteiro L, Legido JL (2003) Estimation of carbonate–alcohol interaction parameters for Nitta-Chao group contribution model: application of a genetic Algorithm. Fluid Phase Equilib 212(1–2):165–174

    Google Scholar 

  42. Poli R, Langdon WB (1998) Genetic programming with one-point crossover. In: Soft computing in engineering design and manufacturing. Springer, 1998, pp 180-189

  43. Tate DM, Smith AE (1995) A genetic approach to the quadratic assignment problem. Comput Oper Res 22(1):73–83

    MATH  Google Scholar 

  44. Gen M, Cheng R, Lin L (2008) Network models and optimization: Multiobjective genetic algorithm approach. Springer, Berlin

    MATH  Google Scholar 

  45. Lin W-Y, Lee W-Y, Hong T-P (2003) Adapting crossover and mutation rates in genetic algorithms. J Inf Sci Eng 19(5):889–903

    Google Scholar 

  46. Xie L, Moran DP, Yan L, Mercado J (2012) Sophisticated software analysis system and use of torque/drag modeling for complex well operations increases operational efficiency. In: IADC/SPE drilling conference and exhibition, 2012: Society of Petroleum Engineers

  47. Johancsik C, Friesen D, Dawson R (1984) Torque and drag in directional wells-prediction and measurement. J Petrol Technol 36(06):987–992

    Google Scholar 

  48. McCormick JE, Evans CD, Le J, Chiu T (2011) The practice and evolution of torque and drag reduction: theory and field results. In: International petroleum technology conference, 2011

  49. Yasari E, Pishvaie MR, Khorasheh F, Salahshoor K, Kharrat R (2013) Application of multi-criterion robust optimization in water-flooding of oil reservoir. J Petrol Sci Eng 109:1–11

    Google Scholar 

  50. Haupt RL, Haupt SE (2004) Practical genetic algorithms. Wiley, New York

    MATH  Google Scholar 

  51. Sivanandam S, Deepa S (2007) Introduction to genetic algorithms. Springer, Berlin

    MATH  Google Scholar 

  52. Li A (2010) The operator of genetic algorithms to improve its properties. Mod Appl Sci 4(3):2010

    Google Scholar 

  53. Karaboga D (2005) An idea based on honey bee swarm for numerical optimization. Technical report-tr06, Erciyes University, Engineering Faculty, Computer…, 2005

  54. Karaboga D, Basturk B (2007) A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J Global Optim 39(3):459–471

    MathSciNet  MATH  Google Scholar 

  55. Karaboga D, Ozturk C (2011) A novel clustering approach: artificial Bee Colony (ABC) algorithm. Appl Soft Comput 11(1):652–657

    Google Scholar 

  56. Karaboga D, Gorkemli B (2014) A quick artificial bee colony (qABC) algorithm and its performance on optimization problems. Appl Soft Comput 23:227–238

    Google Scholar 

  57. Goldberg DE (1989) Genetic algorithms in search. Optim Mach Learn

  58. Chakraborty P, Roy GG, Das S, Jain D, Abraham A (2009) An improved harmony search algorithm with differential mutation operator. Fundam Inform 95(4):401–426

    MathSciNet  MATH  Google Scholar 

  59. Daham BFA, Mohammed N, Mohammed KS (2014) Parameter controlled harmony search algorithm for solving the four-color mapping problem. Int J Comput Inf Technol 3–6

  60. Weyland D (2010) A rigorous analysis of the harmony search algorithm: how the research community can be misled by a “novel” methodology. Int J Appl Metaheuristic Comput 1(2):50–60

    Google Scholar 

  61. Padberg M (2012) Harmony search algorithms for binary optimization problems. In: Operations research proceedings 2011. Springer, 2012, pp 343–348

  62. Lee KS, Geem ZW (2005) A new meta-heuristic algorithm for continuous engineering optimization: harmony search theory and practice. Comput Methods Appl Mech Eng 194(36–38):3902–3933

    MATH  Google Scholar 

  63. Kazakevičius R, Ruseckas J (2014) Lévy flights in inhomogeneous environments and 1/f noise. Physica A 411:95–103

    MathSciNet  MATH  Google Scholar 

  64. Kennedy J, Eberhart R (1995) Particle swarm optimization. In: IEEE international of first conference on neural networks, Perth, Australia. IEEE Press

  65. Kennedy J (1997) The particle swarm: social adaptation of knowledge. In: Proceedings of 1997 IEEE international conference on evolutionary computation (ICEC’97), 1997. IEEE, pp 303–308

  66. Shi Y, Eberhart R (1998) A modified particle swarm optimizer. In: 1998 IEEE international conference on evolutionary computation proceedings. IEEE world congress on computational intelligence (Cat. No. 98TH8360), 1998. IEEE, pp 69–73

  67. Ganesan T, Vasant P, Elamvazuthy I (2012) A hybrid PSO approach for solving non-convex optimization problems. Arch Control Sci 22(1):87–105

    MATH  Google Scholar 

  68. Eberhart RC, Hu X (1999) Human tremor analysis using particle swarm optimization. In: Proceedings of the 1999 congress on evolutionary computation-CEC99 (Cat. No. 99TH8406), 1999, vol 3. IEEE, pp 1927–1930

  69. Xu R, Wunsch D II, Frank R (2007) Inference of genetic regulatory networks with recurrent neural network models using particle swarm optimization. IEEE/ACM Trans Comput Biol Bioinform 4(4):681–692

    Google Scholar 

  70. Xiao X, Dow ER, Eberhart R, Miled ZB, Oppelt RJ (2003) Gene clustering using self-organizing maps and particle swarm optimization. In: Proceedings international parallel and distributed processing symposium, 2003. IEEE

  71. Han P, Huang Y, Jia Z-Z, Wang D-F, Li Y-L (2005) Mixed H/spl I. bar/2/H/spl I. bar//spl infin/optimal PID control for superheated steam temperature system based on PSO optimization. In: 2005 international conference on machine learning and cybernetics, 2005, vol 2. IEEE, pp 960–964

  72. Sharma P, Khurana N (2013) Study of optimal path finding techniques. Int J Adv Technol 4(2):124–130

    Google Scholar 

  73. Onwunalu J (2010) Optimization of field development using particle swarm optimization and new well pattern descriptions. Stanford University

  74. Pedersen MEH (2010) Tuning & simplifying heuristical optimization. University of Southampton

  75. Mercer RE, Sampson J (1978) Adaptive search using a reproductive meta-plan. Kybernetes 7(3):215–228

    Google Scholar 

  76. Keane AJ (1995) Genetic algorithm optimization of multi-peak problems: studies in convergence and robustness. Artif Intell Eng 9(2):75–83

    Google Scholar 

  77. Meissner M, Schmuker M, Schneider G (2006) Optimized Particle Swarm Optimization (OPSO) and its application to artificial neural network training. BMC Bioinform 7(1):125

    Google Scholar 

  78. Pedersen MEH, Chipperfield AJ (2010) Simplifying particle swarm optimization. Appl Soft Comput 10(2):618–628

    Google Scholar 

  79. Huang L, Ding S, Yu S, Wang J, Lu K (2016) Chaos-enhanced Cuckoo search optimization algorithms for global optimization. Appl Math Model 40(5–6):3860–3875

    MathSciNet  MATH  Google Scholar 

  80. Chandrasekar C (2013) “An optimized approach of modified bat algorithm to record deduplication. Int J Comput Appl 62(1):10–15

    Google Scholar 

  81. Nakamura RY, Pereira LA, Costa KA, Rodrigues D, Papa JP, Yang X-S (2012) BBA: a binary bat algorithm for feature selection. In: 2012 25th SIBGRAPI conference on graphics, patterns and images, 2012. IEEE, pp 291–297

  82. Ramesh B, Mohan VCJ, Reddy VV (2013) Application of bat algorithm for combined economic load and emission dispatch. Int J Electr Eng Telecommun 2(1):1–9

    Google Scholar 

  83. Kabir MWU, Sakib N, Chowdhury SMR, Alam MS (2014) A novel adaptive bat algorithm to control explorations and exploitations for continuous optimization problems. Int J Comput Appl 94(13):15–20

    Google Scholar 

  84. Topal AO, Altun O, Yildiz YE (2015) Micro bat algorithm for high dimensional optimization problems. Int J Comput Appl 122(12):2015

    Google Scholar 

  85. Induja S, Eswaramurthy V (2016) Bat algorithm: an overview and its applications. Int J Adv Res Comput Commun Eng 5(1):448–451

    Google Scholar 

  86. Yang X-S, Deb S (2009). Cuckoo search via Lévy flights. In: 2009 World congress on nature & biologically inspired computing (NaBIC), 2009. IEEE, pp 210–214

  87. Yang X-S, Deb S (2014) Cuckoo search: recent advances and applications. Neural Comput Appl 24(1):169–174

    Google Scholar 

  88. Yang X, Deb S (2010) Engineering optimisation by cuckoo search. Int J Math Model Numer Optim 1(4):330–343

    MATH  Google Scholar 

  89. Rajabioun R (2011) Cuckoo optimization algorithm. Appl Soft Comput 11(8):5508–5518

    Google Scholar 

  90. Chandrasekaran K, Simon SP (2012) Multi-objective scheduling problem: hybrid approach using fuzzy assisted cuckoo search algorithm. Swarm Evol Comput 5:1–16

    Google Scholar 

  91. Kanagaraj G, Ponnambalam S, Jawahar N (2013) A hybrid cuckoo search and genetic algorithm for reliability–redundancy allocation problems. Comput Ind Eng 66(4):1115–1124

    Google Scholar 

  92. Zineddine M (2015) Vulnerabilities and mitigation techniques toning in the cloud: a cost and vulnerabilities coverage optimization approach using Cuckoo search algorithm with Lévy flights. Comput Secur 48:1–18

    Google Scholar 

  93. Roy S, Chaudhuri SS (2013) Cuckoo search algorithm using Lévy flight: a review. Int J Mod Educ Comput Sci 5(12):10

    Google Scholar 

  94. Walton S, Hassan O, Morgan K, Brown M (2011) Modified cuckoo search: a new gradient free optimisation algorithm. Chaos Solitons Fractals 44(9):710–718

    Google Scholar 

  95. Singh GP, Singh A (2014) Comparative study of krill herd, firefly and cuckoo search algorithms for unimodal and multimodal optimization. Int J Intell Syst Appl Eng 2(3):26–37

    Google Scholar 

  96. Han K-H, Kim J-H (2000) Genetic quantum algorithm and its application to combinatorial optimization problem. In: Proceedings of the 2000 congress on evolutionary computation. CEC00 (Cat. No. 00TH8512), 2000, vol 2. IEEE, pp 1354–1360

  97. Sha L, He Y (2012) A novel Bloch quantum genetic algorithm and its application of drilling parameters optimization. In: The international conference on engineering technology and economic management, 2012, pp 6–9

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Acknowledgements

This research will be conducted under the Graduate Assistantship (GA) scheme of Universiti Teknologi Petronas and Department of Fundamental and Applied Sciences, University Teknologi Petronas.

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  1. Department of Fundamental and Applied Sciences, Universiti Teknologi Petronas, 31750, Tronoh, Perak, Malaysia

    Kallol Biswas & Pandian M. Vasant

  2. Department of Petroleum Geosciences, Universiti Teknologi Petronas, 31750, Tronoh, Perak, Malaysia

    Jose Antonio Gamez Vintaned

  3. Computer & Information Sciences Department, Universiti Teknologi Petronas, 31750, Tronoh, Perak, Malaysia

    Junzo Watada

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  1. Kallol Biswas
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Biswas, K., Vasant, P.M., Vintaned, J.A.G. et al. A Review of Metaheuristic Algorithms for Optimizing 3D Well-Path Designs. Arch Computat Methods Eng 28, 1775–1793 (2021). https://doi.org/10.1007/s11831-020-09441-1

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