Genetic Algorithm-Based Well Placement Optimization: A Review of Studies

Abstract


Introduction
Efficient well placement optimization is crucial for maximizing production rates, determining the net present value NPV, and facilitating effective field development in the oil and gas industry (Al-Fatlawi, 2018).The strategic positioning of wells within a reservoir directly impacts the overall performance and economic viability of hydrocarbon extraction.Proper well placement reduces the risk of bypassed oil and gas, helps mitigate reservoir heterogeneity challenges, and increases the overall recovery factor (Bagheri and Masihi, 2016;Alpak et al., 2016;Naderi et al., 2021;Al-Mudhafar et al., 2023).Consequently, it leads to higher production rates and a more sustainable reservoir exploitation strategy (Awadh and Al-Owaidi, 2020).Optimal well placement strategies contribute to maximizing NPV by maximizing production rates, reducing operational costs, and minimizing capital expenditures (Carosio et al., 2015;Ding et al., 2019).Strategic field development strategies that incorporate well placement optimization result in refined production profiles, reduced operational risks, and enhanced field management (Alrashdi and Sayyafzadeh, 2019;Salehian et al., 2021;Andreeva and Afanasyev, 2021).Achieving optimal well placement is a challenging task due to various factors (Islam et al., 2020b), including the complex nature of reservoir behavior (Vaseghi et al., 2021), non-linear relationships between decision variables and reservoir responses (Hutahaean et al., 2019), and the restrictions imposed by well positions (Lee et al., 2020).Researchers have turned to artificial intelligence techniques (Bhagat, et al., 2021;Tao et al., 2021), which have shown promise in providing quick and reasonable results for identifying the most favorable locations for wells in reservoir areas with desirable characteristics, such as high oil saturation and permeability.In particular, the genetic algorithm GA has emerged as a powerful tool for well placement optimization, offering efficient and effective solutions.DOI: 10.46717/igj.56.2F.16ms-2023-12-22This review paper provides a comprehensive review of the literature on well placement optimization using genetic algorithms (GAs).The paper discusses the various types of GAs that have been utilized for well placement and explore the different modifications made to enhance their performance.Additionally, the paper examines the diverse applications of GAs in well placement, including optimizing well number, and location.The manuscript concludes by highlighting the advantages of GAs in handling complex problems with multiple objectives and constraints.

Significance of Well Placement in Field Development
Proper well placement is a crucial aspect of the field development process, contributing significantly to the overall success and profitability of oil and gas reservoirs.A well-designed Field Development Plan (FDP) is necessary but not sufficient for the oil and gas project to contribute to economic development while minimizing social impact (Chang, 2015;Humphries and Haynes, 2015).The optimal placement of wells in a reservoir can significantly increase the amount of oil and gas that can be extracted from it, reduce operational costs, and improve recovery rates.Well placement optimization can also handle complex problems with multiple objectives and constraints, making it an ideal choice for optimizing well number (Mahjour et al., 2021), location (Pouladi et al., 2020), and trajectory (Saikia and Shanker, 2019).Enabling technologies can also improve reserves and well productivity (Arinkoola et al., 2016;Temizel et al., 2020).By strategically positioning wells, the reservoir's productive zones can be effectively drained, reducing bypassed reserves and maximizing the recovery factor.This, in turn, has a direct impact on the economic viability and profitability of the field development project.
Well placement optimization also takes into consideration the geological and reservoir characteristics, such as reservoir heterogeneity, fluid properties, and pressure gradients.By incorporating these factors into the placement decisions, operators can mitigate potential challenges such as water or gas coning, avoid high-permeability channels, and target areas with higher reservoir connectivity and permeability (Awadh et al., 2021).Furthermore, well placement optimization is essential for field development planning and asset management.It enables engineers and decision-makers to assess various scenarios, evaluate trade-offs between conflicting objectives, and make informed decisions regarding well spacing, types, and trajectories.By optimizing well placement, operators can achieve a more efficient exploitation of the reservoir, extend the field's economic life, and maximize the ultimate recovery.The significance of well placement in field development is evident in its impact on key performance indicators, including production rates, net present value, and field productivity.Consequently, well placement optimization using advanced techniques such as genetic algorithms have gained considerable attention as a valuable tool for improving field development strategies and optimizing reservoir performance (Rodriguez-Sanchez et al., 2012;Al-Fatlawi, 2018;Li et al., 2022;Mahmood and Al-Fatlawi, 2022;Al-Rubiay and Al-Fatlawi, 2023;Ghorayeb et al., 2023).

Factors Contributing to the Complexity of Well Placement Optimization
Optimizing the placement of wells is a difficult task due to various factors such as the complex nature of reservoir characteristics, multiple decision variables, reservoir non-linearity, and well position restrictions (Awotunde and Naranjo, 2014;Wang et al., 2016;Chen et al., 2022).It is important to understand and address these challenges to develop effective optimization strategies.The main challenges in optimizing well placement are the interplay of multiple decision variables, reservoir nonlinearity, and well position restrictions.
• Numerous Decision Variables: Well placement optimization involves determining the optimal locations of multiple wells within a reservoir.The decision variables include the coordinates x, y, z of each well, the number of wells, and their spacing.The large number of decision variables increases the complexity of the optimization problem, making it computationally demanding and time-consuming.Managing a high-dimensional search space requires advanced algorithms capable of efficiently exploring and identifying optimal solutions.Geological variables such as reservoir architecture, permeability, and porosity distribution, fluid properties, well and surface equipment specifications are some of the factors that make determining the best location for new wells a complex problem (Guyaguler, 2002;Awadh, et al., 2018;Abukhamsin, 2009).The optimization techniques in well placement developments involve decision variables such as well coordinates and objective functions such as NPV (Ding, 2008).Due to the large number of decision variables and the nonlinearity of the reservoir, well placement optimization is a challenging problem that requires advanced algorithms (Emerick et al., 2009).Many optimization schemes have been proposed to simultaneously optimize various variables such as well locations, well operation schedules, well types, and the number of lateral wells (Kim et al., 2020).
• Reservoir Non-Linearity: Reservoir behavior exhibits non-linear relationships between various factors, such as fluid flow, rock properties, and well performance.The non-linear response of the reservoir complicates the optimization process, as small changes in well placement can lead to significant variations in production rates and recovery factors.Accurately capturing and modeling the non-linearities is essential for optimizing well placement and predicting reservoir performance reliably.Reservoir complexities such as the presence of faults, fractures, barriers, channels, and changes in rock facies can affect reservoir performance (Meehan, 2012;Dheyauldeen et al., 2022).Geological variables such as reservoir architecture, permeability, porosity distribution, and fluid properties are some of the factors that make determining the best location for new wells a complex problem (Abukhamsin, 2009;Al-Mimar et al., 2018).In addition, well stimulation is a popular method used to improve productivity from carbonate reservoirs, where the near-wellbore flow transmissibility is enhanced by injecting different chemicals into the reservoir (Sahu et al., 2022).Many optimization schemes have been proposed to simultaneously optimize various variables such as well locations, well operation schedules, well types, and the number of lateral wells (Dheyauldeen et al., 2021, Maity andCiezobka, 2021).Accurately modeling the non-linearities of the reservoir is crucial for optimizing well placement and predicting reservoir performance reliably (Guyaguler, 2002).
• Well Position Restrictions: Well placement is subject to various restrictions imposed by geological, engineering, and regulatory considerations.These restrictions limit the available options for well placement.Geological factors, such as faults, boundaries, and heterogeneities, often limit the available options for well placement.Engineering constraints, including minimum spacing requirements between wells to prevent interference and operational considerations, further restrict the feasible well locations.Incorporating these restrictions into the optimization process adds complexity and requires careful consideration to ensure practical and viable solutions.Determining the best location for new wells is a complex problem that depends on reservoir and fluid properties, well and surface equipment specifications, and geological variables such as reservoir architecture, permeability, and porosity distribution (Abukhamsin, 2009;Bukhamsin et al., 2010;Guyaguler, 2002;Nasrabadi et al., 2012).The optimization techniques in well placement developments involve decision variables such as well coordinates and objective functions such as NPV (Al-Mudhafer, 2013;Alrashdi and Stephen, 2020).Many optimization schemes have been proposed to simultaneously optimize various variables such as well locations, well operation schedules, well types, and the number of lateral wells (Dheyauldeen et al., 2022;Janiga et al., 2020).Salehian et al. (2021) presents a robust, multi-level framework for field development and control optimization under fluid processing capacity constraints while considering well placement and control.
Addressing the challenges of well placement optimization requires the adoption of advanced optimization techniques, such as artificial intelligence methods, to effectively navigate the complex decision space and overcome the non-linearities inherent in reservoir behavior.Artificial intelligence techniques, including genetic algorithms, neural networks, and machine learning algorithms, have shown promise in tackling these challenges and achieving efficient and optimal well placement solutions (Alrashdi and Stephen, 2020;Emerick et al., 2009).These techniques can help optimize well placement by considering various factors such as geological uncertainty, reservoir and fluid properties, and well and surface equipment specifications.In addition, these techniques can help incorporate the various restrictions imposed by geological, engineering, and regulatory considerations into the optimization process.

Limitations of Traditional Optimization Methods
The utilization of traditional optimization techniques for well placement is confronted with a number of constraints when applied to the intricate and considerably nonlinear characteristics of reservoir systems.These constraints have the potential to impede the accuracy and efficiency of attaining optimal well placement solutions.Among the significant limitations are: 1. Complexity of the problem: The optimization of well placement is a complex and highdimensional problem that involves multiple decision variables, including well locations (Wang et al., 2022;Islam et al., 2020a), trajectories, types, and completion designs (Salehian et al., 2020).Traditional optimization methods are often inadequate in addressing the intricacy of this problem, leading to suboptimal or impracticable solutions (Rostamian et al., 2019b;Islam et al., 2020c;Islam et al., 2020b;Ding et al., 2021).
2. Linear assumptions: the complexity of the problem is further compounded by the fact that traditional methods frequently employ linear assumptions in reservoir modeling and optimization.Nevertheless, the inherent nonlinearity of reservoir behavior and fluid flow dynamics, characterized by intricate interactions between geological formations, fluid properties, and well performance, renders such linear approaches inadequate.Linear assumptions may oversimplify the problem, leading to inaccurate representations of the reservoir and suboptimal well placement solutions.Traditional methods frequently employ linear assumptions in reservoir modeling and optimization (Bangerth et al., 2006).However, the inherent nonlinearity of reservoir behavior and fluid flow dynamics, characterized by intricate interactions between geological formations, fluid properties, and well performance, renders such linear approaches inadequate (Deb et al., 2017;Ebadi et al., 2020).Linear assumptions may oversimplify the problem, leading to inaccurate representations of the reservoir and suboptimal well placement solutions.
3. Lack of uncertainty assessment: traditional optimization methods often fail to account for uncertainties associated with reservoir properties, fluid behavior, or production data, which can significantly impact the performance and effectiveness of well placement decisions (Meisingset, 1999;Güyagüler and Horne, 2004;Zhang et al., 2007).Failing to incorporate uncertainty analysis can lead to suboptimal or unreliable placement solutions (Guyaguler, 2002;Rahim and Li, 2015).Determining the best location for new wells is a complex problem that depends on reservoir and fluid properties, well and surface-equipment specifications, and uncertainties in the fault structure and porosity (Meisingset, 1999;Zhang et al., 2007).Well placement optimization aims to determine optimal well locations so that the economic benefit from oil production can be maximized (Rahim and Li, 2015).Reservoir heterogeneity brings in severe roughness and discontinuity to the well placement optimization process, making it one of the most important and challenging issues (Ding, 2008).Therefore, it is essential to develop a framework for optimization of reservoir simulation studies that incorporates uncertainty assessment to achieve optimal well placement solutions (Zhang et al., 2007).
4. Limited consideration of multiple objectives: Traditional methods of well placement optimization often focus on a single objective, such as maximizing production rates or net present value NPV and may struggle to effectively balance and optimize multiple conflicting objectives simultaneously (Chang, 2015;Mahmood and Al-Fatlawi, 2022).However, well placement optimization involves multiple objectives, including maximizing recovery factor, minimizing costs, and considering environmental or operational constraints (Chang, 2015;Ding et al., 2021;Mahmood and Al-Fatlawi, 2022).Determining the best location for new wells is a complex problem that depends on reservoir and fluid properties, well and surface-equipment specifications, and uncertainties in the fault structure and porosity (Rahim and Li, 2015).Reservoir heterogeneity brings in severe roughness and discontinuity to the well placement optimization process, making it one of the most important and challenging issues (Ding et al., 2021).Therefore, it is essential to develop a framework for optimization of reservoir simulation studies that incorporates multiple objectives to achieve optimal well placement solutions (Chang, 2015;Mahmood and Al-Fatlawi, 2022).
5. Computational inefficiency: Traditional optimization methods can be computationally intensive and time-consuming, especially when dealing with large-scale reservoir models and complex decision spaces (Bangerth et al., 2006;Abukhamsin, 2009;Pouladi et al., 2017, Bettin et al., 2019;Lai et al., 2022b).This limits their practicality for real-time decision-making or iterative optimization processes (Bangerth et al., 2006;Bettin et al., 2019).Determining the best location for new wells is a complex problem that depends on reservoir and fluid properties, well and surface-equipment specifications, and uncertainties in the fault structure and porosity (Abukhamsin, 2009;Ding et al., 2021).Reservoir heterogeneity brings in severe roughness and discontinuity to the well placement optimization process, making it one of the most important and challenging issues (Ding et al., 2021).Therefore, it is essential to develop efficient and effective optimization algorithms that can handle large-scale reservoir models and complex decision spaces to achieve optimal well placement solutions in a timely manner (Bangerth et al., 2006;Abukhamsin, 2009;Pouladi et al., 2017;Bettin et al., 2019;Lai et al., 2022b).
To address the limitations of traditional well placement optimization methods, advanced optimization techniques such as genetic algorithms have been adopted by researchers and practitioners.This approach offers greater flexibility, scalability, and robustness in handling the complexities and uncertainties associated with well placement optimization.Genetic algorithms have emerged as a promising alternative that addresses the limitations of traditional methods and provides more efficient and effective optimization strategies for well placement in field development.

An introduction to Genetic Algorithm
The genetic algorithm, introduced by Holland in 1975, is a process that uses the biological ability method to solve complex and long problems through the development of mathematical and computational models that can be applied through optimization techniques to calculate the minimum or maximum of specific data (Carr, 2014).The petroleum and natural gas industries use genetic algorithms in a variety of ways, mostly as optimization techniques (Al-Fatlawi, 2018).David Goldberg, one of Holland's pupils, is credited with making the first application in the literature.He used the genetic algorithm to discover the best design for gas transmission lines (Goldberg, 1983).The genetic algorithm is an efficient search technique that means survival in nature is only compatible with strong generations (Tang et al., 1996).The genetic algorithm is based on natural selection and genetics, which is an optimization method based on research.Natural selection is the reflection of this algorithm where the fittest individuals are selected for reproduction to produce the optimal offspring.Thus, the offspring will inherit the same structures as the parents but are superior to them and have a better chance of survival, and this process continues to iterate until the end where a group with the fittest offspring is created (Mijwel, 2016).The genetic algorithm is commonly used to generate high-quality solutions to optimization and search problems by relying on biologically inspired operators such as mutation, crossover, and selection (Alam et al., 2020).In the context of well placement optimization, the genetic algorithm offers innovative solutions to overcome the challenges associated with complex decisionmaking and non-linear reservoir behavior.
Artificial Intelligence AI techniques, including the genetic algorithm, have shown potential in optimizing well placement by considering various factors such as geological uncertainty, reservoir and fluid properties, and well and surface equipment specifications.The genetic algorithm is a method for solving optimization problems based on natural selection, which drives biological evolution.The genetic algorithm has its own terminology, some of which are derived from genetics.The following are definitions of some terms from the genetic algorithm vocabulary: • Population: A group of individuals that represent various optimization problem solutions.
• Generation: The level of iterations in the optimization process.
• Objective function: The fitness of a solution.
• Fittest: A solution in a generation with the highest objective function.
• Chromosome: A unique solution represented by an encoded string.
• Allele: Genes, also known as chromosome building blocks or bits.
• Parents: A pair of selected solutions for reproduction.
• Children (offspring): Solutions produced as a result of reproduction.The genetic algorithm is widely used in solving optimization problems, research, and machine learning (Montes et al., 2001;Morales et al., 2011;Tukur et al., 2019;He et al., 2022).

Advantages of genetic algorithms in well placement optimization
Genetic algorithms GAs are now recognized as a potent optimization technique for well placement optimization in field development.Compared to traditional methods, they provide several benefits that enable more efficient and effective solutions.The advantages of using genetic algorithms in well placement optimization include: • Nonlinearity and Complex Search Space: Genetic algorithms GAs are well-suited for handling the nonlinearity and complex search spaces encountered in well placement optimization (Montes et al., 2001;Bangerth et al., 2006;Emerick et al., 2009;Chen et al., 2022;He et al., 2022).The nonlinearity of reservoir behavior, coupled with numerous decision variables and constraints, can be effectively addressed by GAs (Montes et al., 2001;Emerick et al., 2009;AlJuboori et al., 2020).GAs explores a wide range of potential solutions, allowing for a more comprehensive search of the solution space, including unconventional well configurations (Montes et al., 2001;Emerick et al., 2009;Al-Janabi et al., 2021).Determining the optimal well location is a challenging task because the effects of geological and engineering variables on reservoir behavior are highly nonlinear (Bangerth et al., 2006;He et al., 2022).Well-placement optimization is a highly nonlinear problem, and the decision variables are integers (Chen et al., 2022).Therefore, it is essential to develop efficient and effective optimization algorithms that can handle the nonlinearity and complex search spaces encountered in well placement optimization to achieve optimal well placement solutions in a timely manner (Montes et al., 2001;Bangerth et al., 2006;Emerick et al., 2009;He et al., 2022;Chen et al., 2022).
• Multiple Objective Optimization: well placement optimization often involves multiple conflicting objectives, such as maximizing production rates, minimizing costs, and considering environmental constraints (Bangerth et al., 2006;Emerick et al., 2009, Chen et al., 2022).Genetic algorithms provide a natural framework for multi-objective optimization (Emerick et al., 2009).They can simultaneously optimize multiple objectives and generate a set of Pareto-optimal solutions, allowing decision-makers to explore trade-offs and make informed choices based on their preferences (Montes et al., 2001;Emerick et al., 2009;Pouladi et al., 2017).Determining the optimal well location is a challenging task because the effects of geological and engineering variables on reservoir behavior are highly nonlinear (Pouladi et al., 2017;He et al., 2022).Therefore, it is essential to develop efficient and effective optimization algorithms that can handle the nonlinearity and complex search spaces encountered in well placement optimization to achieve optimal well placement solutions in a timely manner (Montes et al., 2001;Bangerth et al., 2006;Emerick et al., 2009;Pouladi et al., 2017;Chen et al., 2022;He et al., 2022).
• Robustness and Flexibility: Genetic algorithms GAs are robust optimization techniques that can handle uncertainties and noisy data commonly encountered in reservoir characterization and modeling (Romero et al., 2000;Romero and Carter, 2003;Nikravesh, 2004).They are capable of finding robust solutions that are less sensitive to variations in reservoir parameters and production conditions (Emerick et al., 2009;Romero and Carter, 2003;Romero et al., 2000).GAs also offer flexibility in incorporating various types of constraints, such as well spacing, geological heterogeneity, or operational limitations (Romero et al., 2000;Emerick et al., 2009;He et al., 2022).Determining the optimal well location is a challenging task because the effects of geological and engineering variables on reservoir behavior are highly nonlinear (Romero andCarter, 2003, He et al., 2022).Therefore, it is essential to develop efficient and effective optimization algorithms that can handle the nonlinearity and complex search spaces encountered in well placement optimization to achieve optimal well placement solutions in a timely manner (Romero et al., 2000;Romero and Carter, 2003;Nikravesh, 2004;Emerick et al., 2009;He et al., 2022;Lai et al., 2022a).
• Global Search Capability: traditional optimization methods often suffer from getting trapped in local optima, leading to suboptimal solutions (Hassan and Al-Jawad, 2005;Al-Fatlawi et al., 2017).Genetic algorithms, on the other hand, exhibit global search capabilities, allowing them to explore a broader range of solutions and avoid getting stuck in local optima (Caldas and Norford, 2002).This makes GAs well-suited for exploring diverse and potentially more optimal well placement configurations (Mahmood and Al-Fatlawi, 2022).
• Computational Efficiency: Genetic algorithms (GAs) are effective in managing vast reservoir models and intricate decision spaces, and they can be parallelized to expedite the optimization process.GAs can also utilize substitute models or proxy simulations to lower computational requirements while still ensuring precision in assessing fitness functions (Abdelhafez et al., 2019).GAs have been widely used in various fields, including management decision making, data processing, and financial engineering (AlQahtani et al., 2013).They have been applied to solve a wide range of optimization problems, including unconstrained and constrained optimization problems, nonlinear programming, and combinatorial optimization problems (Hussein et al., 2014).GAs have also been extended to solve multiobjective optimization problems efficiently (Komínková Oplatková et al., 2013).Additionally, GAs have been parallelized on graphical processing units (GPUs) to further enhance their performance and reduce energy costs (Gen and Cheng, 1999;Rani et al., 2013).
• Adaptability and Evolutionary Process: Genetic algorithms imitate the natural evolutionary process, progressively refining the pool of potential solutions.Using selection, crossover, and mutation operators, GAs adapt and evolve the population towards superior solutions over multiple generations.This adaptable trait allows GAs to effectively navigate the search space, enhancing the well placement configurations to attain better performance (Santos and Monteagudo, 2010;Kramer and Kramer, 2017).
To summarize, genetic algorithms provide substantial benefits for optimizing well placement in field development.Their capacity to manage nonlinearity, uncertainties, multiple objectives, and intricate search spaces, in conjunction with their computational efficiency and global search capabilities, make them valuable resources for refining well locations, configurations, and trajectories.The implementation of genetic algorithms in well placement optimization can enhance reservoir performance, maximize recovery, and enhance the economic feasibility of oil and gas field development projects.

Genetic operators and their role in optimization
Genetic algorithms GAs use genetic operators to mimic natural evolution and optimize solutions (Yang, 2021).These operators, including selection, crossover, and mutation, are crucial in generating new solutions and improving the population of potential solutions in GAs for well placement optimization (Al-Rubiay and Al-Fatlawi, 2023).
• Selection: the selection operator in genetic algorithms picks individuals from the population for reproduction based on their fitness values.Individuals with higher fitness, representing better solutions, have a higher likelihood of being chosen.Common selection methods in GAs include tournament selection, rank-based selection, and roulette wheel selection.The selection operator guarantees that fitter individuals have a greater chance of passing their genetic material to the next generation, preserving the population's desirable traits.
• Crossover: the crossover operator involves combining genetic material from two parent individuals to create new offspring, which mimics the biological process of genetic recombination.In well placement optimization, crossover typically involves exchanging genetic information, such as well coordinates or completion designs, between selected parent individuals.The crossover operator allows for the exploration of new combinations of genetic material, potentially generating offspring with improved fitness.Common crossover techniques include single-point crossover, two-point crossover, and uniform crossover (Jacob, 2001, Carr;2014, Katoch et al., 2021)).
• Mutation: the mutation operator introduces random changes in the genetic material of an individual, which helps to maintain diversity in the population and prevents premature convergence to suboptimal solutions (Montes et al., 2001;Carr, 2014;Hassanat et al., 2019).In well placement optimization, mutation can involve perturbing the well coordinates, adjusting completion designs, or modifying other decision variables (Fernandes, 2009;Al-Fatlawi, 2018).Mutation allows for the exploration of new regions in the search space that may not be accessible through crossover alone (Carr, 2014;Hassanat et al., 2019).The mutation rate determines the probability of introducing random changes, and it is typically kept low to balance exploration and exploitation (Hussain and Muhammad, 2020).
The evolutionary procedure in GAs is driven by the combination of genetic operators.The selection operator favors better solutions, while the crossover operator promotes promising combinations.The mutation operator allows for further exploration of the search space by introducing random changes.The effectiveness of these operators can be enhanced by adaptive strategies and advanced techniques, such as niche formation or elitism.The proper design and tuning of these genetic operators are crucial for successful well placement optimization using GAs.

Literature Review: Genetic Algorithm Studies in Well Placement Optimization
The literature review provides an overview of several studies that have applied genetic algorithms GAs for well placement optimization.Bittencourt and Horne 1997 developed a hybrid GA that integrated economic analysis, simulation, and project design, leading to an optimized well distribution and improved project profit.Other studies discussed in the literature review include the use of hybrid optimization techniques, uncertainty quantification, continuous GAs, adaptive GAs, and multi-objective optimization frameworks as listed in Table 1.Overall, these studies demonstrate the effectiveness of GAs in well placement optimization and highlight various enhancements and applications of GAs in this challenging task.

Table 1. Summary of Genetic Algorithm Studies for Well Placement Optimization
The study Summary Bittencourt and Horne (1997) The paper presents a hybrid Genetic Algorithm GA that integrates economic analysis, simulation, and project design for optimizing reservoir development.The algorithm was used to determine an optimal relocation of 33 new wells in a real oil field development project, resulting in a reduction of total new wells and an improvement in project profit by about 6% representing approximately US$70 million additional income.The results showed that the algorithm performed well in finding an optimized well distribution.

Güyagüler and Horne (2004)
The paper discusses the uncertainty associated with well placement optimization, which is affected by uncertainties in reservoir and fluid properties, as well as economic criteria.
The study proposes an approach that addresses these uncertainties within the utility theory framework, using numerical simulation as the evaluation tool.The methodology was evaluated using the PUNQ-S3 model, and a Hybrid Genetic Algorithm HGA was used for optimization.Additionally, a computationally cheaper alternative was investigated, where the well placement problem was formulated as the optimization of a random function using GA.The utility framework was observed to give consistent results with the intent implied through the utility functions, while the random function formulation approach gave satisfactory results with smaller computational effort.Montes et al. (2001) This paper presents a Simple Genetic Algorithm program for optimizing well placement.
The program utilizes a simple Genetic Algorithm without hybrid techniques and is implemented using Visual FORTRAN.Well positions are encoded using pointers, and evaluation of the chromosomes is performed using the Eclipse reservoir simulator.The program includes an algorithm to ensure that a chromosome is evaluated only if it is not repeated in the same generation, which improves resolution speed by skipping redundant evaluations.The paper highlights the capabilities of the Genetic Algorithm in optimizing well placement for fields with complex architectures, but suggests further study is needed to explore issues like absolute convergence, stability, and additional improvements to enhance convergence speed.

Yeten et al. (2003)
The paper titled "Optimization of Nonconventional Well Type, Location, and Trajectory" presents a detailed methodology for determining the optimal type, location, and trajectory of nonconventional wells, a task it recognizes as being extremely complex due to the sheer multitude of potential well types.This optimization process hinges on the application of a Genetic Algorithm paired with several acceleration routines like an artificial neural network, a hill climber, and a near-well upscaling technique.The authors apply this approach to a range of scenarios involving various reservoir types and fluid systems, demonstrating that it consistently enhances the objective function -whether cumulative oil produced or net present value of the project -often by as much as 30%.
The authors articulate that the optimal well type can vary in accordance with the reservoir model and objective function used and also, note that reservoir uncertainty can affect the optimal well choice.Conclusively, the paper confirms the effectiveness of a Genetic Algorithm for optimally deploying nonconventional wells and highlights that optimized wells typically outperform randomly selected ones.Badru and Kabir (2003) This paper addresses the challenge of determining optimal well locations considering uncertainties and non-linear correlations of engineering and geologic variables affecting reservoir performance.The study introduces a hybrid optimization technique combining a genetic algorithm with a polytope algorithm as a helper method.The Hybrid Genetic Algorithm HGA is applied to optimize both horizontal and vertical wells for gas injection and water injection projects, aiming to maximize net present value NPV.The study presents an effective approach for well placement optimization in field development, considering uncertainties, non-linear correlations, and the optimization of horizontal and vertical wells.The findings contribute valuable insights into the performance of different well configurations and emphasize the importance of project-specific factors in determining optimal well locations.

The study Summary
Güyagüler and Horne (2004) This paper discusses the use of genetic algorithms in well placement optimization, addressing the complex problem of determining optimal well locations considering uncertainties in reservoir data.The study develops an approach within the utility-theory framework to translate uncertainty in data to uncertainty in well-placement decisions in terms of monetary value.The methodology is evaluated using the Production forecasting with UNcertainty Quantification (Goldberg, 1983)-S3 model and verified against exhaustive simulations.The study demonstrates that utility theory provides a framework to quantify the influence of uncertainties and risk attitudes.A hybrid genetic algorithm HGA is utilized for optimization, and a computationally cheaper alternative is explored, formulating the well-placement problem as the optimization of a random function using the genetic algorithm.This approach incorporates risk attitudes and is found to be approximate yet computationally feasible.Overall, the study contributes to well placement optimization by considering uncertainties and providing practical optimization techniques.
Farshi (2008) This paper focuses on enhancing the efficiency and effectiveness of genetic algorithms GAs for well placement optimization in reservoir management.The study aims to understand the steps involved in optimum well placement using GAs and introduces enhancements to the algorithm to increase the likelihood of obtaining promising solutions.The study explores the use of continuous GAs in field development and introduces dynamic mutation and a minimum Euclidean distance between individuals in the population.The study also introduces a model that allows for curved wells during the search.The main contributions of the study include modifying the multilateral well model, generating a well placement optimization framework using continuous GAs, and imposing minimum distances between multilateral wells.Emerick et al. (2009) This paper presents a computer-aided optimization tool based on a Genetic Algorithm for simultaneous optimization of producer and injector wells, considering their number, location, and trajectory.The tool incorporates a commercial reservoir simulator as the evaluation function and handles various well placement constraints.The optimization process is applied to three full-field reservoir models based on real cases, resulting in improved net present values and oil recovery factors compared to previous engineerproposed well placement scenarios.The findings demonstrate the effectiveness of the Genetic Algorithm-based optimization tool in enhancing well placement scenarios and its potential to be applied in real field development projects.

Al-Mudhafar et al. (2010)
The paper discusses the critical task of selecting the optimal locations for oil wells in field development planning, focusing on a sector of the South Rumaila oil field.It used a reservoir simulator called "SimBest II", and deployed two optimization methods: a manual method and an automatic Genetic Algorithm GA.GA, an artificial intelligence mechanism similar to Darwin's Natural Selection, was coupled with the simulator to continuously revaluate optimized wells in each iteration.It produced outcomes comparable to the manual method but required less computing time.Both methods used net present value NPV for economic evaluation as the objective function, which demonstrated better performance than using cumulative oil production in determining optimal future reservoir scenarios.The study determined that the optimal scenario involved a water injection rate of 15000 STB/day per well.Although the optimal number of additional wells was found to be nineteen for this case, practicality issues in Iraq mean the number might need to be reduced to three, with a minor NPV reduction.Consequently, if the surface injection facilities can't handle the injection of 15000 STB/day, three wells are recommended.The main takeaway from this study is that using automatic optimization methods, particularly GA, is advantageous over manual optimization methods in saving time and achieving comparable results.Morales et al. (2011) This paper presents a modified genetic algorithm GA specifically designed for wellplacement optimization under geological uncertainty.The algorithm takes possible The study Summary geological models and user-defined risk levels as inputs.The modifications include evaluating the fitness value of each well location using all provided geological models, selecting the optimum well location considering all models upon convergence, and allowing users to define their desired level of risk.The risk-constrained algorithm is applied to optimize horizontal well placement in Qatar's North Field gas condensate reservoir, considering multiple permeability fields and different user-defined risk factors.The modified genetic algorithm proves to be a robust tool, providing varying well locations under different risk scenarios.

Al-Mudhafer (2013)
The paper discusses optimal field development planning in a mature oil field with a focus on the South Rumaila oil field in Iraq.Using an Adaptive Genetic Algorithm AGA in combination with a black oil reservoir model, the study seeks to determine infill well locations.The AGA, coupled with a reservoir simulator, works serially to improve infill well locations at every algorithm iteration.The AGA applies selection, crossover, mutation, and replacement to produce new potential solutions, feeding the optimum locations (or the best chromosomes) back into the simulator for further iterations.The primary purpose is to reach a solution with the highest Net Present Value NPV, which is the chosen objective function.The procedure led to the efficient optimization of three infill wells with the highest NPV among all solutions.Despite the possibility of higher cumulative oil production with more wells, the optimal number of infill wells remained at three, considering NPV's ability to incorporate the economic efficiency of field development as opposed to mere cumulative oil production.The preferable well locations were established at the reservoir's crest, distanced from the east and west flanks to avoid regions previously invaded by the water advance from an active aquifer.Park et al. (2017) This paper proposes a novel method for infill well placement optimization that combines dynamic flow diagnostic characteristics with genetic algorithm optimization methods.
The proposed workflow integrates various stages, including building a simulation model, conducting reservoir simulation, and performing the optimization process.The effectiveness of the proposed workflow is demonstrated through a synthetic example and its application to a full-field model.The proposed method and workflow offer an improved approach to well placement optimization, addressing the limitations of static reservoir property maps and providing a more efficient and effective decision-making process.

Rostamian et al. (2019a)
This paper introduces multi-objective well placement optimization and implements the Non-Dominated Rank Based Genetic Algorithm (NRGA) for the first time to determine the optimal well arrangement in the reservoir.A comparison between the optimized model and the original model demonstrates a significant improvement in both recovery factor and net present value.The generated Pareto front provides decision makers with a range of solutions representing different development scenarios for the model.The study showcases the benefits of employing the Non-Dominated Ranked Based Genetic Algorithm for multi-objective well placement optimization, contributing to improved decision-making processes and enabling companies to achieve more efficient and effective oilfield development plans.Rostamian et al. (2019b) This paper presents a novel multi-objective optimization framework for well placement in oilfield development.The framework utilizes a Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) with a similarity-based mating scheme as the basis for the multi-objective well placement optimization.The objective functions considered in this framework are the net present value (NPV) and the recovery factor.The similarity-based mating scheme is compared with conventional mating selection methods, and the analysis of convergence speed demonstrates that the similarity-based selection method significantly reduces the number of iterations.The framework offers decision makers a broader range of options and facilitates the selection of an optimal scenario based on the company's requirements.
The study Summary Alrashdi and Sayyafzadeh (2019) The paper compares the performance of the (μ + λ) Evolution Strategy (ES) Algorithm with other optimization algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and (μ, λ) Covariance Matrix Adaptation Evolution Strategy (CMA-ES) in five different optimization cases.The paper focuses on field development optimization in reservoir management and highlights the importance of finding the optimum setting for the best exploitation strategy and financial returns.

He et al. (2022)
The paper proposes a technique that integrates genetic algorithms GA with productivity potential maps PPMs to optimize well placement.The study addresses the challenge of determining the optimal well location, considering the nonlinear and multimodal effects of geological and engineering variables on reservoir performance.The computational requirements for automatic optimization are extensive, requiring numerous reservoir simulations.To reduce optimization time and improve the optimization effect, the GA is integrated with PPMs generated using three different methods: analysis, numerical simulation, and fuzzy system.Numerical tests conducted in the PUNQ-S3 oilfield demonstrate that generating PPMs through the analytical method yields the best results.Compared to the original well scheme and GA well scheme, the GA + PPMs approach significantly increases cumulative oil production COP by 20.95% and 8.09%, respectively.

Baghban et al. (2022)
The paper proposes the use of a parallel genetic algorithm PGA to accelerate the optimization process for well placement problems.The PGA works with multiple populations of chromosomes and allows exchange between populations to improve optimization performance.The objective is to maximize the present value of the project by optimizing the location of wells, considering factors such as oil price and the cost of separating water and gas.Chromosome fitness is evaluated by CPUs in parallel on a single or multiple networked computers.A case study involving vertical wells in an oil reservoir is used to validate the method, resulting in a significant reduction in optimization time and simulator performances in parallel processing mode.The PGA has led to a four-fold reduction in execution time compared to the genetic algorithm.

Key Findings and Outcomes from Genetic Algorithm Studies in Well Placement Optimization
Based on the studies presented in Section 4, Genetic algorithms GAs have been extensively researched as a means of optimizing well placement in reservoir development.The literature review revealed several key findings and outcomes from these studies.Firstly, hybrid GAs that integrated economic analysis, simulation, and project design demonstrated promising results in optimizing well distribution, reducing the number of wells, and improving project profitability.Uncertainties in reservoir data were effectively addressed through utility theory frameworks, providing consistent results and practical optimization techniques.Simple GAs without hybrid techniques proved to be effective in optimizing complex field architectures, while suggestions were made to further study and improve convergence speed.The optimization of nonconventional wells using GAs, along with acceleration routines, consistently improved the objective function, demonstrating the effectiveness of GAs in deploying such wells.
Moreover, the studies emphasized the significance of considering uncertainties, non-linear correlations, and project-specific factors when determining optimal well locations.The use of continuous GAs, dynamic mutation, and minimum distances between individuals contributed to enhancing the efficiency and effectiveness of optimization.Multi-objective optimization frameworks, such as Non-Dominated Rank Based Genetic Algorithms NRGA and Non-Dominated Sorting Genetic Algorithm-II NSGA-II, provided decision-makers with a range of solutions for different development scenarios.Additionally, the integration of GAs with productivity potential maps PPMs and parallel genetic algorithms PGAs resulted in significant reductions in optimization time and improvements in cumulative oil production.These findings collectively demonstrate the value of GAs in well placement optimization, offering practical insights and techniques for improving net present value, recovery factor, and other objective functions in oilfield development projects.In summary, GAs have proven to be an effective tool in optimizing well placement in reservoir development, offering various techniques and frameworks for decision-makers to improve project profitability and success.

Advances and Future Directions in Well Placement Optimization using Genetic Algorithms
The field of well placement optimization using genetic algorithms GAs has seen significant advancements in recent years.These advancements have improved the efficiency and effectiveness of optimization processes and opened up new possibilities for further exploration.
One notable advancement is the integration of GAs with other optimization methods (Yeten et al., 2003), such as neural networks (Wang et al., 2022), hill climbers (Reed and MarksII, 1999), and upscaling techniques.Hybrid GAs that combines genetic algorithms with other optimization methods have shown promising results in enhancing the objective function.These hybrid approaches leverage the strengths of different algorithms to overcome the limitations of standalone GAs and achieve more robust and efficient optimization outcomes.Further research can focus on exploring new hybridization techniques and evaluating their effectiveness in different reservoir scenarios.
Another area of advancement is the incorporation of uncertainty quantification and risk analysis in well placement optimization using GAs.Several studies have proposed frameworks that consider uncertainties in reservoir and fluid properties, as well as economic criteria, within the utility theory framework.These frameworks enable decision-makers to quantify and manage risks associated with well placement decisions, leading to more informed and robust optimization solutions.Future research can delve deeper into uncertainty quantification techniques, explore novel risk analysis methodologies, and develop adaptive algorithms that can dynamically handle uncertainties during the optimization process.
Moreover, the emergence of multi-objective optimization has opened up new possibilities for well placement optimization using GAs.Non-dominated rank-based genetic algorithms and non-dominated sorting genetic algorithms have been successfully employed to generate a range of optimal solutions representing different trade-offs between conflicting objectives.These multi-objective optimization frameworks provide decision-makers with a comprehensive set of well placement options, allowing them to explore different development scenarios and make informed decisions based on their specific requirements.Further research can focus on developing new objective functions, considering additional constraints, and integrating multi-objective optimization with real-time data for dynamic decisionmaking.
Advancements in computational technologies have enabled parallelization of genetic algorithms, leading to significant reductions in optimization time (Leoshchenko et al., 2019).Parallel genetic algorithms PGAs utilize multiple populations of chromosomes and allow for exchange between populations, thereby improving optimization performance (Xavier et al., 2013).This parallelization technique can be further explored and optimized to leverage high-performance computing resources and accelerate the optimization process even more.
As we look towards the future, there are several promising directions for well placement optimization using genetic algorithms.The integration of machine learning and artificial intelligence techniques, such as deep learning (Kianinejad et al., 2022) and reinforcement learning (Dawar, 2021), can bring new insights and capabilities to the optimization process.These techniques can enhance the learning capabilities of GAs, enable adaptive optimization strategies, and handle complex reservoir and operational data.Moreover, the application of GAs can be extended beyond traditional well placement optimization to address emerging challenges, such as the optimization of intelligent wells, unconventional reservoirs, and integrated surface and subsurface systems.
In conclusion, the field of well placement optimization using genetic algorithms has witnessed significant advancements and holds great potential for further development.Continued research and innovation in these areas will contribute to the advancement of well placement optimization practices and pave the way for sustainable and optimized oilfield development in the future.

Conclusions
Genetic algorithms offer several advantages in handling nonlinearity and complex search spaces encountered in well placement optimization, allowing for a more comprehensive search and exploration of unconventional well configurations.The global search capabilities of genetic algorithms make them suitable for discovering diverse and potentially more optimal well placement configurations by avoiding local and exploring a broader range of solutions.The challenges of well placement optimization can be addressed by adopting advanced optimization techniques to navigate the complex decision space and overcome the non-linearities inherent in reservoir behavior.