An efficient approach for optimizing full field development plan using Monte-Carlo simulation coupled with Genetic Algorithm and new variable setting method for well placement applied to gas condensate field in Vietnam

Jun, Jae Young; Kang, Junsuk; Jeong, Daein; Lee, Haeseon

doi:10.1177/0144598716680307

Cited by 9 publications

(6 citation statements)

References 17 publications

(23 reference statements)

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“…Besides, the training period of SVM is long. For the large-size problems, the large amount of computation time should be reserved (Cao and Tay, 2003; Jun et al., 2017). Fortunately, two algorithms (i.e.…”

Section: Model Structurementioning

confidence: 99%

Accuracy analyses and model comparison of machine learning adopted in building energy consumption prediction

Liu

et al. 2019

Energy Exploration & Exploitation

108

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It is of great significance to achieve the prediction of building energy consumption. However, machine learning, as a promising technique for many practical applications, was rarely utilized in this field. The most important reason is that the predictive structure with best performance is difficult to be determined. To fill the gap, this paper offers one in-depth review, which focuses on the accuracy analyses and model comparisons. Specifically, the accuracy analyses were conducted based on different types of buildings (e.g. residential building, commercial building, government building or educational building), different type of temporal granularity (e.g. sub-hourly, hourly, daily or annual), as well as input/output variables and historical data collections. Further, artificial neural network (ANN) and support vector machine (SVM), as the epidemic models, were compared in terms of their complexity of prediction processes, accuracies of results, the amounts of required historical data, the numbers of inputs, etc. Then the hybrid and single machine learning methods were outlined and compared in terms of their strengths and weaknesses. In addition, several vital defects and further research directions are presented from a multivariate perspective. We hope

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Section: Model Structurementioning

confidence: 99%

Accuracy analyses and model comparison of machine learning adopted in building energy consumption prediction

Liu

et al. 2019

Energy Exploration & Exploitation

108

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“…Gas condensate reservoirs are different from the conventional reservoir systems; when the gas condensate reservoir pressure drops below the dew point pressure, the heavy components of the condensate gas begin to condense and become the liquid phase [5][6][7][8]. Therefore, liquid condensate will accumulate near the wellbore, which will seriously damage the productivity of gas wells [9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Calculation Method of Three-Phase Productivity of Horizontal Well in Water-Bearing Condensate Gas Reservoir

Zhang,

Chen,

et al. 2024

Energies

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Condensate gas reservoirs are a special kind of reservoir because of the presence of multiphase flow in their production. The accurate calculation of the multiphase flow productivity in horizontal wells is of great significance for reservoir development. In this study, the total pseudopressure and the equivalent total flow are defined to solve the multiphase nonlinear problem in the oil–gas–water three-phase control flow equation, and to establish a three-phase productivity equation considering the non-Darcy effect. It provides a solution for the three-phase productivity calculation of horizontal wells in water-bearing condensate gas reservoirs. The example verification and error analysis of three horizontal wells, except for the gas production error of Well Y3 and the average error of the production data of other gas wells, is below 4%. The fitting effect of Well Y2 is better than that of the other two horizontal wells, and the average error of the oil, gas, and water phases is below 3%. This method provides a practical and simple engineering tool for the analysis of the productivity of condensate gas reservoirs considering multiphase flow.

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“…Considering the influence of plunger motion law, valve ball motion law, oil well inflow performance, etc, Wang et al (2018) establish the dynamic parameter simulation model of pumping wells and study the methods to improve the system efficiency. For gas injection wells, Jun et al (2017) use Monte Carlo simulation method to select the optimal gas recovery scheme, then genetic algorithm is used to find the optimal development scheme. In order to overcome the difficulties brought by the low oil prices and provide meaningful and valuable information for operators.…”

Section: Introductionmentioning

confidence: 99%

“…(2018) establish the dynamic parameter simulation model of pumping wells and study the methods to improve the system efficiency. For gas injection wells, Jun et al. (2017) use Monte Carlo simulation method to select the optimal gas recovery scheme, then genetic algorithm is used to find the optimal development scheme.…”

Section: Introductionmentioning

confidence: 99%

Multi-objective optimization for oil-gas production process based on compensation model of comprehensive energy consumption using improved evolutionary algorithm

Liu

Yuan

Wang

et al. 2020

Energy Exploration & Exploitation

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This paper establishes an error compensation multi-objective optimization model of oil-gas production process for optimizing these production indices, including overall oil production, overall water production and comprehensive energy consumption per ton of oil. In order to reduce the error between the model output and the actual value of comprehensive energy consumption per ton of oil, combining the mechanism model with least squares support vector machine (LS-SVM) error model optimized by Bayesian optimization algorithm (BOA), a hybrid model is established to predict the comprehensive energy consumption, in which the mechanism model is used to describe the overall characteristics of oil-gas production process, and LS-SVM error model is established to compensate the mechanism model error. Then, in order to improve the performance of Pareto non-dominated solutions, an improved non-dominated sorting genetic algorithm-II with multi-strategy improvement (IMS-NSGA-II) is proposed to solve the error compensation multi-objective optimization model. Finally, the effectiveness and superiority of the the proposed optimization method are verified by the experiment results on some stand test problems and the optimization problem for the oil-gas production process in a block of an oil production operation area.

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An efficient approach for optimizing full field development plan using Monte-Carlo simulation coupled with Genetic Algorithm and new variable setting method for well placement applied to gas condensate field in Vietnam

Cited by 9 publications

References 17 publications

Accuracy analyses and model comparison of machine learning adopted in building energy consumption prediction

Accuracy analyses and model comparison of machine learning adopted in building energy consumption prediction

Calculation Method of Three-Phase Productivity of Horizontal Well in Water-Bearing Condensate Gas Reservoir

Multi-objective optimization for oil-gas production process based on compensation model of comprehensive energy consumption using improved evolutionary algorithm

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