Production-System Optimization of Gas Fields Using Hybrid Fuzzy/Genetic Approach

Park, Hui-june; Lim, Jong‐Se; Roh, Jeongyong; Kang, Jungmin; Min, Baehyun

doi:10.2118/100179-pa

Cited by 12 publications

(2 citation statements)

References 52 publications

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“…After almost a decade, gas production and scheduling optimization is still a very active research topic (Park et al 2006;Park et al 2010). Genetic algorithms have also been used to estimate the dew point pressure of a gas condensate reservoir with positive results (Shokir 2008).…”

Section: Application Of Genetic Algorithms In Petroleum Engineeringmentioning

confidence: 99%

A Modified Genetic Algorithm for Horizontal Well Placement Optimization in Gas Condensate Reservoirs

Morales

2010

SPE Annual Technical Conference and Exhibition

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Determining optimum well locations is a crucial step in field development. Often, broad possibilities and constrains on computational resources limit the scenarios that can be considered. When dealing with heterogeneities, the intuitive engineering judgment may not be sufficient, and use of optimization algorithms becomes necessary in finding a favorable production plan. Although there have been extensive publications on optimization algorithms for oil reservoirs, there is little research aimed at gas or gas condensate reservoirs. In this work, we studied optimization of horizontal well placement in gas condensate reservoirs. One of the current challenges in development of gas condensate fields is condensate blockage. As pressure decreases condensate accumulates around the wellbore, leading to significant reduction in gas production. Horizontal wells can effectively solve this problem. However, due to their high cost, and complex phase behavior determining optimum locations cannot be based only on intuitive judgments. Here we present a horizontal well-placement optimization method for gas condensate reservoirs based on a modified genetic algorithm. Unlike oil reservoirs, the cumulative production in gas reservoirs does not vary significantly (although the variation is not economically negligible) and there are possibly more local optimums. Therefore the possibility of finding better production scenarios in subsequent optimization steps is not much higher than the worse case scenarios, which delays finding the best production plan. In this work, we use a cumulative distribution function to magnify the difference between production scenarios. As a result of this change we were able to find the best scenario with considerably fewer simulations. We apply the modified algorithm to a section of Qatar's North Field, a gas condensate field with the world's largest gas reserves. Our results show that this method can efficiently find the optimum horizontal well locations and can lead to valuable increase in gas and condensate production.

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Section: Application Of Genetic Algorithms In Petroleum Engineeringmentioning

confidence: 99%

A Modified Genetic Algorithm for Horizontal Well Placement Optimization in Gas Condensate Reservoirs

Morales

2010

SPE Annual Technical Conference and Exhibition

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“…The optimization works above have found the operating condition compromising bitumen recovery and energy efficiency due to the form of objective functions. Conventionally, most optimization approaches that the petroleum industry has adopted are based on global-objective optimization no matter what they are either gradient-based methods [19][20][21][22] or non-gradient-based methods [23][24][25][26]. Global-objective optimization scheme converts a vector of objective functions into a single global objective function, i.e., the weighted sum of individual objective function values.…”

Section: Introductionmentioning

confidence: 99%

Quick Screening of Pareto-Optimal Operating Conditions for Expanding Solvent–Steam Assisted Gravity Drainage Using Hybrid Multi-Objective Optimization Approach

2017

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Solvent-steam mixture is a key factor in controlling the economic efficiency of the solvent-aided thermal injection process for producing bitumen in a highly viscous oil sands reservoir. This paper depicts a strategy to quickly provide trade-off operating conditions of the Expanding Solvent-Steam Assisted Gravity Drainage (ES-SAGD) process based on Pareto-optimality. Response surface models are employed to evaluate multiple ES-SAGD scenarios at low computational costs. The surrogate models play a role of objective-estimators in the multi-objective optimization that provides qualified ES-SAGD scenarios regarding bitumen recovery, steam-energy efficiency, and solvent-energy efficiency. The developed hybrid approach detects positive or negative correlations among the performance indicators of the ES-SAGD process. The derived Pareto-optimal operating conditions give flexibility in field development planning and thereby help decision makers determine the operating parameters of the ES-SAGD process based on their preferences.

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Hybrid intelligent systems in petroleum reservoir characterization and modeling: the journey so far and the challenges ahead

Anifowose

Labadin

Abdulraheem

2016

J Petrol Explor Prod Technol

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Computational intelligence (CI) techniques have positively impacted the petroleum reservoir characterization and modeling landscape. However, studies have showed that each CI technique has its strengths and weaknesses. Some of the techniques have the ability to handle datasets of high dimensionality and fast in execution, while others are limited in their ability to handle uncertainties, difficult to learn, and could not deal with datasets of high or low dimensionality. The ''no free lunch'' theorem also gives credence to this problem as it postulates that no technique or method can be applicable to all problems in all situations. A technique that worked well on a problem may not perform well in another problem domain just as a technique that was written off on one problem may be promising with another. There was the need for robust techniques that will make the best use of the strengths to overcome the weaknesses while producing the best results. The machine learning concepts of hybrid intelligent system (HIS) have been proposed to partly overcome this problem. In this review paper, the impact of HIS on the petroleum reservoir characterization process is enumerated, analyzed, and extensively discussed. It was concluded that HIS has huge potentials in the improvement of petroleum reservoir property predictions resulting in improved exploration, more efficient exploitation, increased production, and more effective management of energy resources. Lastly, a number of yet-to-be-explored hybrid possibilities were recommended.

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Production-System Optimization of Gas Fields Using Hybrid Fuzzy/Genetic Approach

Cited by 12 publications

References 52 publications

A Modified Genetic Algorithm for Horizontal Well Placement Optimization in Gas Condensate Reservoirs

A Modified Genetic Algorithm for Horizontal Well Placement Optimization in Gas Condensate Reservoirs

Quick Screening of Pareto-Optimal Operating Conditions for Expanding Solvent–Steam Assisted Gravity Drainage Using Hybrid Multi-Objective Optimization Approach

Hybrid intelligent systems in petroleum reservoir characterization and modeling: the journey so far and the challenges ahead

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