Use of Natural Tracer Data in Well Placement Optimization

Hutahaean, Junko

doi:10.2118/173462-stu

Cited by 5 publications

(5 citation statements)

References 14 publications

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“…Junko Hotahyan and colleagues in 2014 utilized three algorithms, HGA (Hybrid Genetic Algorithm), GA, and PSO, for well placement optimization. In this study, the PSO algorithm exhibited good performance in optimizing well locations compared to other algorithms [14]. Saeed Afshari and Mahmoudreza Pishvaei, along with their colleagues in 2014, optimized well placements using PSO, GA, and simulated annealing (SA).…”

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confidence: 90%

Optimization of production and injection wells location in an oil field with faulted oil-rich reservoir zones in southern Iran using GA, PSO, and HGAPSO algorithms

Nikravesh,

Azin,

Ranjbar

2023

Preprint

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The problem is to optimize the location of wells and injection sites in such a way as to maximize oil production and also protect oil reservoirs from possible damages. In this study, oil well location optimization and economic evaluation in the water injection process have been done. In this regard, three particle swarm optimization algorithms (PSO), Genetic Optimization Algorithm (GA) and Hybrid Optimization Algorithm (HGAPSO) and Net Present Value (NPV) calculation have been performed. In this simulation, 100 repetitions and 50 particles were used. The results of the simulations show that the PSO algorithm has a better performance than the GA and HGAPSO algorithms and can be used as a strong and effective method to optimize the location of wells and the number of production and water injection wells in the oil injection process. We want to optimize the production well, the PSO algorithm has been 124.12% more profitable than the production in the well location optimization mode in the conventional way. The GA algorithm has been 124.12% more profitable than the production in the conventional well drilling mode The HGAPSO algorithm has 123.45% more profit than the production in the case of optimizing the location of the well by the conventional method. In the case of optimizing the location of 2 injection wells, the simulation results of each of the PSO, GA and HGAPSO algorithms are respectively 118.6%. and 118.58% and 118.51% shows more profit than the production in the case of optimizing the well location in the conventional way. In the studied reservoir, PSO algorithm in the case where the location of 4 production wells is optimized, the production has increased by 18.7% compared to the production in the case of optimizing the location of wells in the conventional way. When the location of two injection wells is optimized with the PSO algorithm, production has increased by 18.8% compared to the time of drilling an injection well using the conventional method.

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confidence: 90%

Optimization of production and injection wells location in an oil field with faulted oil-rich reservoir zones in southern Iran using GA, PSO, and HGAPSO algorithms

Nikravesh,

Azin,

Ranjbar

2023

Preprint

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“…As evident from Figs. 19 and 20, when we divide the well drilling time into two parts, we will have lower Field Oil Production Rate (FOPR-GAW2) but the highest Recovery Factor (RF-GAWI2). The locations of the producing and injection wells can be observed in Figs.…”

Section: Reservoir Fluid Propertiesmentioning

confidence: 99%

“…The well locations can be seen in Figs. 19. If we schedule well drilling without using a genetic algorithm, we will experience lower production (Natural Production -FOPR) and lower Recovery…”

Section: Reservoir Fluid Propertiesmentioning

confidence: 99%

“…In 2014,Hutahaean utilized three algorithms, HGA (Hybrid Genetic Algorithm), GA, and PSO for well placement optimization. Their study revealed that the PSO algorithm performed well in well placement optimization compared to other algorithms in this context [19]. In 2014,Afshari, S., and et al the PSO, GA, and SA algorithms for well placement optimization.…”

Section: Introductionmentioning

confidence: 99%

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

Nikravesh,

Ranjbar,

Azin

2023

Preprint

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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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“…The same conclusion was reported by Awotunde (2014a). Hutahaean (2014) and Hutahaean et al (2014) investigated the effect of sulphate scale deposition on well placement optimization in an oil reservoir assisted by waterflooding using particle swarm optimization (PSO). They showed that this approach allows to easily spot location with low-risk scale deposition and high oil recovery.…”

Section: Introductionmentioning

confidence: 99%

A field development strategy for the joint optimization of flow allocations, well placements and well trajectories

Dada

Mellal

Makhloufi

et al. 2020

Energy Exploration & Exploitation

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One of the major goals that field planning engineers and decision makers have to achieve in terms of reservoir management and hydrocarbon recovery optimization is the maximization of return on financial investments. This task yet very challenging due to high number of decision variables and some uncertainties, pushes the engineers and technical advisors to seek for robust optimization methods in order to optimally place wells in the most profitable locations with a focus on increasing the net-present value over a project life-cycle. The quest to deliver a good quality advice is also dependent on how some uncertainties – geologic, economic and flow patterns – have been handled and formulated all along the optimization process. With the enhancement of computer power and the advent of remarkable optimization techniques, the oil and gas industry has at hand a wide range of tools to get an overview on value maximization from petroleum assets. Amongst these tools, genetic algorithms which belong to stochastic optimization methods have become well known in the industry as one the best alternatives to apply when trying to solve well placement and production allocation problems, though computationally demanding. The aim of this work is to present a novel approach in the area of hydrocarbon production optimization where control settings and well placement are to be determined based on a single objective function, in addition to the optimization of wells’ trajectories. Starting from a reservoir dynamic model of a synthetic offshore oil field assisted by water injection, the work consisted in building a data-driven model that was generated using artificial neural networks. Then, we used Matlab’s genetic algorithm toolbox to perform all the needed optimizations; from which, we were able to establish a drilling schedule for the set of wells to be realized, and we made it possible to simultaneously get the well location and configuration (vertical or horizontal), well type (producer or injector), well length, well orientation – in the horizontal plane –, as well as well controls (flow rates) and near wellbore pressure with respect to a set of linear and nonlinear-constraints. These constraints were formulated so as to reproduce real field development considerations, and with the aid of a genetic algorithm procedure written upon Matlab, we were able to satisfy those constraints such as, maximum production and injection rates, optimal wellbore pressures, maximum allowable liquid processing capacity, optimal well locations, wells’ drilling and completion maximum duration, in addition to other considerations. We have investigated some scenarios with the intention of proving the benefits of development strategy that we have chosen to study. It was found the chosen scenario could improve NPV by 3 folds in comparison to a base case scenario. The positioning of the wells was successful as all producers were placed in zones having initial water saturation less than 0.4., and all injectors were placed high water saturation zones. Moreover, we established a procedure regarding well trajectory design and optimization by taking into account, minimum dogleg severity and maximum duration for a well to be drilled and completed with respect to a time threshold. The findings as well as the workflow that will be presented hereafter could be considered as a guideline for subsequent tasks pertaining to the process of decision making, especially when it has to do with the development of green oil and gas fields and will certainly help in the placement of wells in less risky and cost-effective locations.

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Use of Natural Tracer Data in Well Placement Optimization

Cited by 5 publications

References 14 publications

Optimization of production and injection wells location in an oil field with faulted oil-rich reservoir zones in southern Iran using GA, PSO, and HGAPSO algorithms

Optimization of production and injection wells location in an oil field with faulted oil-rich reservoir zones in southern Iran using GA, PSO, and HGAPSO algorithms

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

A field development strategy for the joint optimization of flow allocations, well placements and well trajectories

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