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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

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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.

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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

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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
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  2. Ali Ranjbar
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  3. Reza Azin
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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.

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Correspondence to Ali Ranjbar.

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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

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