Aplicação de redes neurais artificiais no processo de ajuste de histórico / Application of artificial neural networks in the history matching process

AUTOR(ES)
FONTE

IBICT - Instituto Brasileiro de Informação em Ciência e Tecnologia

DATA DE PUBLICAÇÃO

24/05/2012

RESUMO

The history matching process is one of the most important stages involving studies of reservoirs, because with the adjusted reservoir model, the production forecasts can be done with higher reliability and different production strategies can be evaluated to obtain greater final recovery associated with less costs. However, this process have several problems associated, one being the multiple solution, meaning that different models provide satisfactory results, depending on the objective of the study. Furthermore, the reservoir in study can have different heterogeneities and nonlinearities between reservoir attributes and values of production and pressure, which also contributes to increase the complexity. Various published work showed that each case has different characteristics, so that a methodology that was applied successfully in one case, may not be efficient in another and vice-versa. Thus, studies in this area should be developed and updated constantly. The great challenge in problems involving history matching is related to reducing the number of simulations required to achieve satisfactory adjustments in accordance with the proposed objective. Among several procedures for this purpose, the application of proxy models generated through artificial neural networks (ANN) can be cited. The proxy models, once generated, are able to calculate the results much faster than the simulator due to the fact that they are simplified models. The ANN are structures capable of efficiently capture nonlinearities between inputs and outputs of a given problem. Thus, these proxy models have characteristics that make them promising for use as substitute of simulator in stages that require greater computational effort. Thereby, in this work the application of proxy models generated through ANN in the history matching process was evaluated, primarily regarding to the influence of the input quality in the proxy performance and the reliability of the use of proxy models as substitutes of the simulator in a realistic reservoir model. The results showed that the tool, despite the errors involved, because it is simplified model, can be used as auxiliary tool to the flow simulator in the process of history matching. It is not recommended to use as a substitute in the whole process, however, can contribute in the process stages that do not require great precision. For reliable results, it is necessary to validate the response (found through the proxy) using the reservoir simulator.

ASSUNTO(S)

inteligência artificial redes neurais algoritmos genéticos engenharia de reservatório amostragem artificial intelligence artificial neural networks genetic algorithms oil reservoir engineering sampling

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