Skip to main content

Advertisement

Springer Nature Link
Log in

Optimizing the wells location using the Metaheuristic algorithms, as well as optimizing the drilling time of production and injection wells in one of the reservoirs in south west of Iran

  • RESEARCH
  • Published:
Earth Science Informatics Aims and scope Submit manuscript

Abstract

In this study, the optimization of the location of oil production and injection wells, the number of production and injection wells, and the economic evaluation in the water injection process has been carried out using three algorithms: PSO, GA, and HGAPSO. Optimizing the oil extraction process by optimizing well locations, the optimal number of production and injection wells, and economic evaluation can improve the performance and efficiency of the process. In this study, the optimization of the water injection process into oil reservoirs using the PSO, GA, and HGAPSO algorithms has been addressed. Additionally, economic evaluation, considering costs and profits from oil production using the best-optimized parameters, has been conducted, and the best Net Present Value (NPV) has been calculated. Initially, the optimal well location problem is modeled, taking into account reservoir characteristics and geological conditions. Using the PSO and GA algorithms, the best well location and number of water injection wells have been obtained, leading to improved efficiency and cost reduction. Then, the GA algorithm is used to combine and modify viewpoints and optimize the number and location of wells. This algorithm explores optimal solutions in the well location space. Economic evaluation is performed using the best-optimized parameters by the algorithms. Costs of water injection, oil production costs, and profits from increased oil production are considered. By calculating the best Net Present Value (NPV), optimal decisions regarding the water injection process and enhancing the performance of oil reservoirs are made. Simulation results indicate that the GA algorithm outperforms other algorithms and can be used as a robust and effective method for optimizing well locations and the number of production and injection wells in the oil injection process. Ultimately, this study assists the oil industry in making better decisions about optimizing water injection and improving the efficiency and profitability of the process using the introduced optimization algorithms.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21

Similar content being viewed by others

Data availability

Data will be available upon request.

Abbreviations

NPV:

Net present value ($)

D:

Discount rate (fraction)

T:

Number of years

FOE:

(OIP(initial)—OIP(now))/OIP(initial)

Qo :

Cumulative oil production (STB)

FOPR:

Field oil production rate

rop :

Oil sales price in each period($)

FOPT:

Field oil production cumulative total

Qw :

Cumulative water production (STB)

FWCT:

Field water cut

rwp :

Water production cost per period($)

Capex:

Capital expenditure ($)

Qwi :

Water injection in injection wells

rwi :

Cost of water injection in injection ($)wells

References

  • Afshari S, Aminshahidy B, Pishvaie MR (2011) Application of an improved harmony search algorithm in well placement optimization using streamline simulation. J Petrol Sci Eng 78(3–4):664–678

    Article  CAS  Google Scholar 

  • Afshari S, Pishvaie M, Aminshahidy B (2014) Well placement optimization using a particle swarm optimization algorithm, a novel approach. Pet Sci Technol 32(2):170–179

    Article  CAS  Google Scholar 

  • Afshari S, Aminshahidy B, Pishvaie MR (2015) Well placement optimization using differential evolution algorithm. Iran J Chem Chem Eng (IJCCE) 34(2):109–116

    Google Scholar 

  • Ariadji T et al (2012) Optimization of vertical well placement for oil field development based on basic reservoir rock properties using a genetic algorithm. J Eng Technol Sci 44(2):106–127

    Google Scholar 

  • Awotunde AA, Mutasiem MA (2014) Efficient drilling time optimization with differential evolution. In: SPE Nigeria Annual International Conference and Exhibition. 2014. OnePetro

  • Cheng G, An Y, Wang Z, Zhu K (2012) Oil well placement optimization using niche particle swarm optimization. In: 2012 Eighth International Conference on Computational Intelligence and Security. IEEE

  • Cho H, Shah S (2000) Optimization of well length for horizontal drilling. In: PETSOC Canadian International Petroleum Conference. 2000. PETSOC

  • Darabi H, Masihi M (2013) Well placement optimization using hybrid optimization technique combined with fuzzy inference system. Pet Sci Technol 31(5):481–491

    Article  CAS  Google Scholar 

  • Ding S, Jiang H, Li J, Tang G (2014) Optimization of well placement by combination of a modified particle swarm optimization algorithm and quality map method. Comput Geosci 18(5):747–762

    Article  Google Scholar 

  • Ding S-W et al (2019) Optimization of well placement by pso assisted by quality map and gompertz-based grey model. International Field Exploration and Development Conference. Springer

    Google Scholar 

  • Ding S et al. (2016) Optimization of well location, type and trajectory by a modified particle swarm optimization algorithm for the PUNQ-S3 model. J Ind Intell Inf, 4(1)

  • Dong X et al (2011) Optimization of vertical well placement by using a hybrid particle swarm optimization. Wuhan Univ J Nat Sci 16(3):237–240

    Article  Google Scholar 

  • Hamida Z, Azizi F, Saad G (2017) An efficient geometry-based optimization approach for well placement in oil fields. J Petrol Sci Eng 149:383–392

    Article  CAS  Google Scholar 

  • Han X et al (2021) Well placement and control optimization of horizontal steamflooding wells using derivative-free algorithms. SPE Reservoir Eval Eng 24(01):174–193

    Article  Google Scholar 

  • Harb A, Kassem H, Ghorayeb K (2020) Black hole particle swarm optimization for well placement optimization. Comput Geosci 24(6):1979–2000

    Article  MathSciNet  Google Scholar 

  • Hongwei, C., et al., (2019) Well placement optimization with cat swarm optimization algorithm under oilfield development constraints. J Energ Res Technol. 141(1)

  • Hutahaean J (2014) Use of natural tracer data in well placement optimization. In: SPEAnnual Technical Conference and Exhibition. OnePetro

  • Islam J et al (2020) A holistic review on artificial intelligence techniques for well placement optimization problem. Adv Eng Softw 141:102767

    Article  Google Scholar 

  • Islam J et al (2021) A modified niching crow search approach to well placement optimization. Energies 14(4):857

    Article  Google Scholar 

  • Janiga D, Czarnota R, Stopa J, Wojnarowski P (2019) Self-adapt reservoir clusterization method to enhance robustness of well placement optimization. J Petrol Sci Eng 173:37–52

    Article  CAS  Google Scholar 

  • Karkevandi-Talkhooncheh A, Sharifi M, Ahmadi M (2018) Application of hybrid adaptive neuro-fuzzy inference system in well placement optimization. J Petrol Sci Eng 166:924–947

    Article  CAS  Google Scholar 

  • Lai SW et al (2021) Large-scale deployment of a closed-loop drilling optimization system: implementation and field results. SPE Drill Complet 36(01):47–62

    Article  ADS  Google Scholar 

  • Liu B et al (2023) NMR log response prediction from conventional petrophysical logs with XGBoost-PSO framework. Geoenergy Sci Eng 224:211561

    Article  CAS  ADS  Google Scholar 

  • Lyons J, Nasrabadi H (2013) Well placement optimization under time-dependent uncertainty using an ensemble Kalman filter and a genetic algorithm. J Petrol Sci Eng 109:70–79

    Article  CAS  Google Scholar 

  • Morales A, Nasrabadi H, Zhu D (2010) A modified genetic algorithm for horizontal well placement optimization in gas condensate reservoirs. Paper SPE 135182 presented at the SPE Annual Technical Conference and Exhibition, Florence, Italy

  • Moussa T, et al. (2019) Well-placement optimization in heavy oil reservoirs using a novel method of in situ steam generation. J Energ Res Technol, 141(3)

  • Nozohour-leilabady B, Fazelabdolabadi B (2016) On the application of artificial bee colony (ABC) algorithm for optimization of well placements in fractured reservoirs; efficiency comparison with the particle swarm optimization (PSO) methodology. Petroleum 2(1):79–89

    Article  Google Scholar 

  • Nwankwor E, Nagar AK, Reid D (2013) Hybrid differential evolution and particle swarm optimization for optimal well placement. Comput Geosci 17(2):249–268

    Article  Google Scholar 

  • Okoro EE, Agwu OE, Olatunji D, Orodu OD (2019) Artificial bee colony ABC a potential for optimizing well placement–a review. In: SPE Nigeria Annual International Conference and Exhibition. OnePetro

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

  • Onwunalu JE, Durlofsky LJ (2010) Application of a particle swarm optimization algorithm for determining optimum well location and type. Comput Geosci 14:183–198

    Article  Google Scholar 

  • Pouladi B, Keshavarz S, Sharifi M, Ahmadi MA (2017) A robust proxy for production well placement optimization problems. Fuel 206:467–481

    Article  CAS  Google Scholar 

  • Rahmanifard H, Plaksina T (2018) Application of fast analytical approach and AI optimization techniques to hydraulic fracture stage placement in shale gas reservoirs. J Nat Gas Sci Eng 52:367–378

    Article  Google Scholar 

  • Razghandi M, Dehghan A, Yousefzadeh R (2021) Application of particle swarm optimization and genetic algorithm for optimization of a southern Iranian oilfield. J Pet Explor Prod 11:1781–1796

    Google Scholar 

  • Redoloza F, Li L (2019) A novel method for well placement design in groundwater management: Extremal optimization. Adv Water Resour 132:103405

    Article  Google Scholar 

  • Rostamian A (2017) Well placement optimization enhancement by implementation of similarity-based mating scheme in non-dominated sorting genetic algorithm-II. In: 79th EAGE Conference and Exhibition 2017. European Association of Geoscientists & Engineers

  • Rostamian A, Jamshidi S, Zirbes E (2019b) The development of a novel multi-objective optimization framework for non-vertical well placement based on a modified non-dominated sorting genetic algorithm-II. Comput Geosci 23:1065–1085

    Article  MathSciNet  Google Scholar 

  • Rostamian A, Jamshidi S, Kamari M (2019a) Non-dominated ranked based genetic algorithm multi-objective well placement optimization. In: 81st EAGE Conference and Exhibition 2019. European Association of Geoscientists & Engineers

  • Salmachi A, Sayyafzadeh M, Haghighi M (2013) Infill well placement optimization in coal bed methane reservoirs using genetic algorithm. Fuel 111:248–258

    Article  CAS  Google Scholar 

  • Sayyafzadeh M (2015) A self-adaptive surrogate-assisted evolutionary algorithm for well placement optimization problems. In: SPE/IATMI Asia Pacific Oil & Gas Conference and Exhibition. OnePetro

  • Semnani A et al (2021) Joint optimization of constrained well placement and control parameters using teaching-learning based optimization and an inter-distance algorithm. J Petrol Sci Eng 203:108652

    Article  CAS  Google Scholar 

  • Siddiqui MA, Khan RA, Jamal MS (2015) Multi-objective well placement optimization considering energy sustainability along with economical gains. In: SPE North Africa Technical Conference and Exhibition. 2015. OnePetro

  • Tang H, Durlofsky LJ (2022) Use of low-fidelity models with machine-learning error correction for well placement optimization. Comput Geosci, p 1–18

  • Temizel C et al. (2014) Optimization of well placement in real-time production optimization of intelligent fields with use of local and global methods. In: SPE Annual Technical Conference and Exhibition. OnePetro

  • Tukur, A.D., et al. (2019) Well placement optimization using simulated annealing and genetic algorithm. In: SPE Nigeria Annual International Conference and Exhibition. One Petro

  • Yazdanpanah A, Rezaei A, Mahdiyar H, Kalantariasl A (2019) Development of an efficient hybrid GA-PSO approach applicable for well placement optimization. Adv Geo-Energ Res 3(4):365–374

    Article  Google Scholar 

  • Yousefzadeh R, Sharifi M, Rafiei Y (2021) An efficient method for injection well location optimization using fast marching method. J Petrol Sci Eng 204:108620

    Article  CAS  Google Scholar 

  • Zhang J et al (2020) A two-stage step-wise framework for fast optimization of well placement in coalbed methane reservoirs. Int J Coal Geol 225:103479

    Article  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Petroleum, Gas and Petrolchemical Engineering, Petroleum Engineering Department, Persian Gulf University, Bushehr, Iran

    Hamed Nikravesh, Ali Ranjbar & Reza Azin

Authors
  1. Hamed Nikravesh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ali Ranjbar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Reza Azin
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

-  Hamed Nikravesh: Data Gathering, Results Preparation, Code Prepration, Draft Preparation, Analysis of Results.

- Ali Ranjbar: Idea, Analysis of Results, Conceptualization, Methodology.

- Reza Azin: Analysis of Results, Conceptualization, Methodology.

Corresponding author

Correspondence to Ali Ranjbar.

Ethics declarations

Ethical approval

- This material is the authors' own original work, which has not been previously published elsewhere.

- The paper is not currently being considered for publication elsewhere.

- The paper reflects the authors' own research and analysis in a truthful and complete manner.

- The paper properly credits the meaningful contributions of co-authors and co-researchers.

- The results are appropriately placed in the context of prior and existing research.

- All sources used are properly disclosed (correct citation). Literally copying of text must be indicated as such by using quotation marks and giving proper references.

- All authors have been personally and actively involved in substantial work leading to the paper, and will take public responsibility for its content.

Consent to publish

All authors approve the manuscript and give their consent for submission and publication as open access.

Competing interests

The authors declare no competing interests.

Additional information

Communicated by: H. Babaie

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Nikravesh, H., Ranjbar, A. & Azin, R. Optimizing the wells location using the Metaheuristic algorithms, as well as optimizing the drilling time of production and injection wells in one of the reservoirs in south west of Iran. Earth Sci Inform 17, 1393–1410 (2024). https://doi.org/10.1007/s12145-023-01204-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12145-023-01204-3

Keywords