Gas production rate optimization by genetic algorithm

In order to satisfy the excessive demand during the heating days, the excessive supply during the nonheating days should be stored and used then. Underground gas storage is the process accomplishing this task. A detailed study of the reservoir and model construction will enable correct forecasts and thus the successful operation of the underground storage facility. In the event that the demand exceeds the field's top production capacity optimization has to be made in order to maximize the gas production. Genetic algorithm is used as the optimization tool in this study. A population is initially randomly generated and manipulated by processes analog to natural operators in the effort to find the optimum. Each population consists of individuals which each represent a different set of well production rates for the gas storage field. In this study, a real gas reservoir in Turkey having the potential for being an underground gels storage unit is evaluated. A preutilization design procedure is applied to determine the field's working renditions. interactive software (GASOPT) is developed which utilizes the three-dimensional gas reservoir simulator as the generic algorithms evaluation function. A probable demand curve is put forward. Forecast and optimization is made with GASOPT

Comparison of genetic algorithm with linear programming for the optimization of an underground gas-storage field

Linear Programming (LP) methods have previously been proposed for the optimization of underground storage fields. As the name implies, these methods necessitate many assumptions for the linearization of the constraints and the objective function, reducing accuracy of the results. A new approach for field optimization is developed that eliminates these assumptions. Genetic Algorithm (GA) is used as the optimization tool. A population consisting of individuals that encode a set of well rates for the field is created and evolved into new generations through a stochastic though structured algorithm that models some natural phenomena. The three-dimensional finite-difference model of the field is used as the GA's evaluation function. Individuals that return higher field cumulative production are given higher chance to reproduce. Individuals that cause a flowing well pressure lower than the minimum allowable are penalized. It is observed that the LP method gives significantly different results than those of the GA approach because of the assumptions it bears

Otimização de alternativas para o desenvolvimento de campos de petróleo Selection of alternatives for the development of oil fields

Este artigo apresenta um sistema de suporte à decisão para a otimização de alternativas de desenvolvimento de campos de petróleo. O desenvolvimento de um campo de petróleo consiste na escolha de uma alternativa para a explotação de um reservatório petrolífero, conhecido e delimitado, que permita produzir a maior quantidade de hidrocarboneto possível, dentro dos limites físicos e econômicos existentes, isto é, maximizar o valor presente líquido (VPL). O VPL é calculado a partir da produção de óleo, a qual é obtida com o uso de um simulador de reservatório. Cada execução do simulador pode demorar desde alguns segundos até várias horas, dependendo da complexidade do reservatório modelado. Isto reduz o número de alternativas que podem ser geradas e avaliadas pelo especialista na busca da melhor solução. Deste modo, o objetivo deste trabalho é propor e avaliar um sistema inteligente de otimização que emprega: algoritmos genéticos (AGs), algoritmos culturais (ACs) e coevolução para a busca de uma alternativa de desenvolvimento ótima; e um ambiente de computação paralela para a simulação de reservatório e cálculo do VPL das alternativas. O sistema resultante permite que o especialista obtenha, em tempo hábil, a alternativa ótima (ou quase-ótima) para o desenvolvimento de um campo de petróleo conhecido. Os resultados obtidos nos estudos de casos apresentados demonstram que o sistema proposto, baseado em técnicas inteligentes, obtém boas alternativas de desenvolvimento de campos petrolíferos com uma grande redução do tempo computacional, redução esta obtida a partir do aproveitamento do poder computacional de um ambiente de computação paralela e do aproveitamento de conhecimento do especialista, por meio das sementes iniciais.<br>This paper presents an optimization system for the development of petroleum fields. Developing a petroleum field consists of choosing an alternative exploitation of an already known and delimited petroleum reservoir allowing the maximum hydrocarbon production within the physical and economical limitations i.e., maximizing the net present value (NPV). The net present value is calculated according to the oil production, which is obtained with the use of a reservoir simulator. Each reservoir simulation can take from few seconds to several hours, depending on the complexity of the reservoir being modeled. This reduces the total number of configurations that can be generated and evaluated by the user in search for the best solution. Therefore, this work proposes and evaluates a new intelligent, optimization system that employs genetic algorithms (GA), cultural algorithms (CA), and co-evolution in order to search for an optimal development alternative in a parallel computing environment for reservoir simulations and NPV calculation. The proposed system provides the user, in a reasonable time, with the optimum (or sub-optimum) configuration for the development of the petroleum field. The results obtained in the case studies demonstrate that the proposed system, based on intelligent techniques, enable good configurations for the development of petroleum fields with a great reduction in computational time. This reduction is obtained from the computational power of the parallel computing environment and from the expert knowledge, through the initial configuration of the optimizing system (initial seed)

Well Placement Optimization with the Covariance Matrix Adaptation Evolution Strategy and Meta-Models

International audienceThe amount of hydrocarbon recovered can be considerably increased by finding optimal placement of non-conventional wells. For that purpose, the use of optimization algorithms, where the objective function is evaluated using a reservoir simulator, is needed. Furthermore, for complex reservoir geologies with high heterogeneities, the optimization problem requires algorithms able to cope with the non regularity of the objective function. In this paper, we propose an optimization methodology for determining optimal well locations and trajectories based on the Covariance Matrix Adaptation - Evolution Strategy (CMA-ES) which is recognized as one of the most powerful derivative-free optimizers for continuous optimization. In addition, to improve the optimization procedure two new techniques are proposed: (1) Adaptive penalization with rejection in order to handle well placement constraints; (2) Incorporation of a meta-model, based on locally weighted regression, into CMA-ES, using an approximate stochastic ranking procedure, in order to reduce the number of reservoir simulations required to evaluate the objective function. The approach is applied to the PUNQ-S3 case and compared with a Genetic Algorithm (GA) incorporating the Genocop III technique for handling constraints. To allow a fair comparison, both algorithms are used without parameter tuning on the problem, standard settings are used for the GA and default settings for CMA-ES. It is shown that our new approach outperforms the genetic algorithm: it leads in general to both a higher net present value and a significant reduction in the number of reservoir simulations needed to reach a good well configuration. Moreover, coupling CMA-ES with a metamodel leads to further improvement, which was around 20% for the synthetic case in this study