Application of artificial neural networks in the history matching process

Orientadores: Célio Maschio, Denis José SchiozerDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Mecânica e Instituto de GeociênciasResumo: O processo de ajuste de histórico consiste em uma das etapas mais importantes envolvendo estudos de reservatórios, pois com o modelo de simulação ajustado pode-se realizar previsões de produção com maior confiabilidade e avaliar diferentes estratégias de produção de forma a obter maior recuperação final com menor custo. Porém, esse processo traz consigo diversas dificuldades, sendo uma delas a não unicidade das soluções, ou seja, vários modelos podem igualmente proporcionar resultados satisfatórios dependendo do objetivo de estudo. Além disso, o reservatório pode possuir diversas heterogeneidades e não linearidades entre atributos do reservatório e valores de produção e pressão, o que também contribui para aumentar a complexidade do problema. Através dos diversos trabalhos já publicados comprovou-se que cada caso possui diferentes características, de forma que uma metodologia aplicada com sucesso a um determinado caso pode não ser aplicável a outro e vice e versa. Dessa maneira, estudos nessa área devem ser realizados e atualizados constantemente. O grande desafio em problemas envolvendo ajuste de histórico está relacionado à redução do número de simulações necessárias para alcançar ajustes satisfatórios de acordo com o objetivo proposto. Entre as diversas técnicas que podem ser encontradas na literatura para tal propósito, uma que chama atenção é a aplicação de metamodelos gerados através de Redes Neurais Artificiais. Os metamodelos, uma vez gerados, são capazes de fornecer os resultados muito mais rápido que o simulador, pois se tratam de modelos simplificados. As RNA, por sua vez, são estruturas capazes de captar com eficiência as não linearidades entre entradas e saídas de um dado problema. Assim, os metamodelos gerados por RNA possuem características que os tornam promissores para serem utilizados como substitutos do simulador em etapas do ajuste que demandam maior esforço computacional. Deste modo, nesse trabalho foi avaliada a aplicação de metamodelos gerados por RNA no processo de ajuste de histórico, principalmente no que se refere à influência que a qualidade do conjunto de entrada exerce sobre o desempenho do metamodelo gerado e com relação à confiabilidade da utilização do metamodelo como substituto do simulador para casos práticos, com características mais próximas da realidade. Os resultados mostraram que a ferramenta, apesar dos erros envolvidos, por se tratar de um modelo simplificado, pode ser utilizada como ferramenta auxiliar ao simulador de escoamento no processo de ajuste de histórico. Não é recomendada a sua utilização como substituta do simulador no processo inteiro, porém, pode contribuir em etapas do processo que não requerem grande precisão dos resultados. Para a confiabilidade dos resultados, é necessário validar a resposta (encontrada por meio do metamodelo) usando o simulador de reservatóriosAbstract: 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 simulatorMestradoReservatórios e GestãoMestre em Ciências e Engenharia de Petróle

Evaluation of an uncertainty reduction methodology based on Iterative Sensitivity Analysis (ISA) applied to naturally fractured reservoirs

International audienceHistory matching for naturally fractured reservoirs is challenging because of the complexity of flow behavior in the fracture-matrix combination. Calibrating these models in a history-matching procedure normally requires integration with geostatistical techniques (Big Loop, where the history matching is integrated to reservoir modeling) for proper model characterization. In problems involving complex reservoir models, it is common to apply techniques such as sensitivity analysis to evaluate and identify most influential attributes to focus the efforts on what most impact the response. Conventional Sensitivity Analysis (CSA), in which a subset of attributes is fixed at a unique value, may over-reduce the search space so that it might not be properly explored. An alternative is an Iterative Sensitivity Analysis (ISA), in which CSA is applied multiple times throughout the iterations. ISA follows three main steps: (a) CSA identifies Group i of influential attributes (i = 1, 2, 3, …, n); (b) reduce uncertainty of Group i, with other attributes with fixed values; and (c) return to step (a) and repeat the process. Conducting CSA multiple times allows the identification of influential attributes hidden by the high uncertainty of the most influential attributes. In this work, we assess three methods: Method 1 – ISA, Method 2 – CSA, and Method 3 – without sensitivity analysis, i.e., varying all uncertain attributes (larger searching space). Results showed that the number of simulation runs for Method 1 dropped 24% compared to Method 3 and 12% to Method 2 to reach a similar matching quality of acceptable models. In other words, Method 1 reached a similar quality of results with fewer simulations. Therefore, ISA can perform as good as CSA demanding fewer simulations. All three methods identified the same five most influential attributes of the initial 18. Even with many uncertain attributes, only a small percentage is responsible for most of the variability of responses. Also, their identification is essential for efficient history matching. For the case presented in this work, few fracture attributes were responsible for most of the variability of the responses

Evaluation of an uncertainty reduction methodology based on Iterative Sensitivity Analysis (ISA) applied to naturally fractured reservoirs

History matching for naturally fractured reservoirs is challenging because of the complexity of flow behavior in the fracture-matrix combination. Calibrating these models in a history-matching procedure normally requires integration with geostatistical techniques (Big Loop, where the history matching is integrated to reservoir modeling) for proper model characterization. In problems involving complex reservoir models, it is common to apply techniques such as sensitivity analysis to evaluate and identify most influential attributes to focus the efforts on what most impact the response. Conventional Sensitivity Analysis (CSA), in which a subset of attributes is fixed at a unique value, may over-reduce the search space so that it might not be properly explored. An alternative is an Iterative Sensitivity Analysis (ISA), in which CSA is applied multiple times throughout the iterations. ISA follows three main steps: (a) CSA identifies Group i of influential attributes (i = 1, 2, 3, …, n); (b) reduce uncertainty of Group i, with other attributes with fixed values; and (c) return to step (a) and repeat the process. Conducting CSA multiple times allows the identification of influential attributes hidden by the high uncertainty of the most influential attributes. In this work, we assess three methods: Method 1 – ISA, Method 2 – CSA, and Method 3 – without sensitivity analysis, i.e., varying all uncertain attributes (larger searching space). Results showed that the number of simulation runs for Method 1 dropped 24% compared to Method 3 and 12% to Method 2 to reach a similar matching quality of acceptable models. In other words, Method 1 reached a similar quality of results with fewer simulations. Therefore, ISA can perform as good as CSA demanding fewer simulations. All three methods identified the same five most influential attributes of the initial 18. Even with many uncertain attributes, only a small percentage is responsible for most of the variability of responses. Also, their identification is essential for efficient history matching. For the case presented in this work, few fracture attributes were responsible for most of the variability of the responses

A new methodology to reduce uncertainty of global attributes in naturally fractured reservoirs

Accurately characterizing fractures is complex. Several studies have proposed reducing uncertainty by incorporating fracture characterization into simulations, using a probabilistic approach, to maintain the geological consistency, of a range of models instead of a single matched model. We propose a new methodology, based on one of the steps of a general history-matching workflow, to reduce uncertainty of reservoir attributes in naturally fractured reservoirs. This methodology maintains geological consistency and can treat many reservoir attributes. To guarantee geological consistency, the geostatistical attributes (e.g., fracture aperture, length, and orientation) are used as parameters in the history matching. This allows us to control Discrete Fracture Network attributes, and systematically modify fractures. The iterative sensitivity analysis allows the inclusion of many (30 or more) uncertain attributes that might occur in a practical case. At each uncertainty reduction step, we use a sensitivity analysis to identify the most influential attributes to treat in each step. Working from the general history-matching workflow of Avansi et al. (2016

A new methodology to reduce uncertainty of global attributes in naturally fractured reservoirs

Accurately characterizing fractures is complex. Several studies have proposed reducing uncertainty by incorporating fracture characterization into simulations, using a probabilistic approach, to maintain the geological consistency, of a range of models instead of a single matched model. We propose a new methodology, based on one of the steps of a general history-matching workflow, to reduce uncertainty of reservoir attributes in naturally fractured reservoirs. This methodology maintains geological consistency and can treat many reservoir attributes. To guarantee geological consistency, the geostatistical attributes (e.g., fracture aperture, length, and orientation) are used as parameters in the history matching. This allows us to control Discrete Fracture Network attributes, and systematically modify fractures. The iterative sensitivity analysis allows the inclusion of many (30 or more) uncertain attributes that might occur in a practical case. At each uncertainty reduction step, we use a sensitivity analysis to identify the most influential attributes to treat in each step. Working from the general history-matching workflow of Avansi et al. (2016), we adapted steps for use with our methodology, integrating the history matching with geostatistical modeling of fractures and other properties in a big loop approach. We applied our methodology to a synthetic case study of a naturally fractured reservoir, based on a real semi-synthetic carbonate field, offshore Brazil, to demonstrate the applicability in practical and complex cases. From the initial 18 uncertain attributes, we worked with only 5 and reduced the overall variability of the Objective Functions. Although the focus is on naturally fractured reservoirs, the proposed methodology can be applied to any type of reservoir