A review on closed-loop field development and management

Mirzaei‐Paiaman, Abouzar; Santos, Susana M.G.; Schiozer, Denis José

doi:10.1016/j.petrol.2021.108457

Cited by 23 publications

(4 citation statements)

References 101 publications

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“…the values of the production data) differs from the historical one, or to guide adjusts in the model set so that their behavior approximates the historical behavior. This process, named data assimilation, aims to "reduce the geological and reservoir engineering ISBN: 978-989-8704-42-9 © 2022 uncertainties in simulation models by tuning them against the field responses" (Mirzaei-Paiaman et al, 2021). The process is also called history matching, parameter estimation, or computer-assisted history matching, among other names (Mirzaei-Paiaman et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

“…This process, named data assimilation, aims to "reduce the geological and reservoir engineering ISBN: 978-989-8704-42-9 © 2022 uncertainties in simulation models by tuning them against the field responses" (Mirzaei-Paiaman et al, 2021). The process is also called history matching, parameter estimation, or computer-assisted history matching, among other names (Mirzaei-Paiaman et al, 2021). We have a special interest in iterative data assimilation methods whose goal is to choose the best fitting models that reduce uncertainty (Maschio et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

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Using Reorderable Heatmaps as a Support for Data Assimilation in Petroleum Reservoir Management and Development Processes

2022

Proceedings of the 16th International Conference on Computer Graphics, Visualization, Computer Vision and Image Processing (CGV

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Petroleum exploration activities demand the creation of lots of models to represent the uncertainties of a given reservoir, aiming to guide decision-making regarding exploration strategies. As part of these activities, a data assimilation process may refine the model set and discard models that do not honor the history data of the reservoir, given an error tolerance level. Reservoir engineers may use visualizations to analyze the partial results of iterative data assimilation processes and make decisions such as accepting the models selected in an iteration or refining the model set. Among a set of available visualizations for this process, we did not find in the literature the use of interactive heatmaps based on reorderable matrices. In this paper, we propose to add this kind of visualization to the analysis toolset for data assimilation, aiming to help engineers to better overview data assimilation datasets and how their models honor the history data of a reservoir. The proposed visual mappings use a set of heatmaps that enable the engineers to analyze a misfit measure (NQDS) for a given iteration, regarding models, wells, and their dynamic attributes. Users may reorder and filter these heatmaps according to their needs. We implemented our proposal in a web-based prototype that was submitted to a preliminary evaluation with real users in a synthetic scenario. This evaluation revealed positive opinions about the potential of the tool, which may be used to complement other approaches, and provided some opportunities for improvement.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Using Reorderable Heatmaps as a Support for Data Assimilation in Petroleum Reservoir Management and Development Processes

2022

Proceedings of the 16th International Conference on Computer Graphics, Visualization, Computer Vision and Image Processing (CGV

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“…One reason for this problem is based on how the uncertain geological parameters in the reservoir of interest are quantified and utilized in the solution method for the reservoir optimization problem. Having a good quantification of the uncertain parameters using production history in reservoir studies has been the major contribution of closed-loop reservoir management (Aanonsen et al, 2009;Zhang et al, 2019;Mirzaei-Paiaman et al, 2021). Several solution methods for the non-linear constrained optimization problems , usually encountered in hydrocarbon reservoir fields have been proposed and used extensively for different applications (see e.g., , Jansen (2011), , , and Zhao et al (2020)).…”

Section: Introductionmentioning

confidence: 99%

Efficient Optimization and Robust Value Quantification of Enhanced Oil Recovery Strategies

Oguntola¹

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With an increasing demand for hydrocarbon reservoir produces such as oil, etc., and difficulties in finding green oil fields, the use of Enhanced Oil Recovery (EOR) methods such as polymer, Smart water, and solvent flooding for further development of existing fields can not be overemphasized. For reservoir profitability and reduced environmental impact, it is crucial to consider appropriate well control settings of EOR methods for given reservoir characterization. Moreover, finding appropriate well settings requires solving a constrained optimization problem with suitable numerical solution methods. Conventionally, the solution method requires many iterations involving several computationally demanding function evaluations before convergence to the appropriate near optimum. The major subject of this thesis is to develop an efficient and accurate solution method for constrained optimization problems associated with EOR methods for their value quantifications and ranking in the face of reservoir uncertainties. The first contribution of the thesis develops a solution method based on the inexact line search method (with Ensemble Based Optimization (EnOpt) for approximate gradient computation) for robust constrained optimization problems associated with polymer, Smart water, and solvent flooding. Here, the objective function is the expectation of the Net Present Value (NPV) function over given geological realizations. For a given set of well settings, the NPV function is defined based on the EOR simulation model, which follows from an appropriate extension of the black-oil model. The developed solution method is used to find the economic benefits and also the ranking of EOR methods for different oil reservoirs developed to mimic North Sea reservoirs. Performing the entire optimization routine in a transformed domain along with truncations has been a common practice for handling simple linear constraints in reservoir optimization. Aside from the fact that this method has a negative impact on the quality of gradient computation, it is complicated to use for non-linear constraints. The second contribution of this thesis proposes a technique based on the exterior penalty method for handling general linear and non-linear constraints in reservoir optimization problems to improve gradient computation quality by the EnOpt method for efficient and improved optimization algorithm. Because of the computationally expensive NPV function due to the costly reservoir simulation of EOR methods, the solution method for the underlying EOR optimization problem becomes inefficient, especially for large reservoir problems. To speedup the overall computation of the solution method, this thesis introduces a novel full order model (FOM)-based certified adaptive machine learning optimization procedures to locally approximate the expensive NPV function. A supervised feedforward deep neural network (DNN) algorithm is employed to locally create surrogate model. In the FOM-based optimization algorithm of this study, several FOM NPV function evaluations are required by the EnOpt method to approximate the gradient function at each (outer) iteration until convergence. To limit the number FOM-based evaluations, we consider building surrogate models locally to replace the FOM based NPV function at each outer iteration and proceed with an inner optimization routine until convergence. We adapt the surrogate model using some FOM-based criterion where necessary until convergence. The demonstration of methodology for polymer optimization problem on a benchmark model results in an improved optimum and found to be more efficient compared to using the full order model optimization procedures.

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“…Schiozer et al (2015) e Schiozer et al (2019) desenvolveram uma metodologia baseada em 12 etapas para ser aplicada no processo de análise e decisão para o desenvolvimento e gerenciamento de um reservatório de petróleo. A metodologia baseia-se no conceito de gestão de reservatórios em circuito fechado, que envolve a atualização periódica de um modelo de campo incerto usando as últimas medições, seguido da otimização da produção (MIRZAEI-PAIAMAN et al, 2021).…”

Section: Introductionunclassified

Geração de regras de controle via mineração de dados para a otimização de indicador de produção para poços inteligentes

Ferreira Batista Filho

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ResumoO gerenciamento de reservatório de petróleo visa maximizar o lucro considerando restrições operacionais, tecnológicas, econômicas, regulatórias e outras. A gestão de reservatórios é intrinsecamente associada à infraestrutura de produção utilizada e está intimamente envolvida em todas as fases do ciclo de vida do reservatório; da exploração à produção e, finalmente à revitalização. Para estruturar e facilitar o estudo de reservatórios, essa infraestrutura é mapeada em um conjunto de variáveis para o problema estudado, e que podem ser classificadas como variáveis de: projeto (G1); controle (G2) e revitalização (G3). Atualmente, decisões de curto prazo no gerenciamento de válvulas de captação de óleo e gás, ou de injeção de fluido, são diversas vezes tomadas sem um procedimento formal, o que pode levar a uma produção não otimizada. Uma das maneiras de subsidiar as atividades de gerenciamento é basear-se em dados históricos de produção para tomar decisões futuras. Este trabalho propõe uma metodologia baseada em mineração de dados para a obtenção de regras de controle aplicáveis ao gerenciamento das variáveis de controle (G2). O desenvolvimento considerou dados simulados, equivalentes a dados de reservatórios reais, gerados a partir do modelo de reservatório UNISIM-II-D. Experimentos foram conduzidos tanto para validar as regras de controle obtidas pela metodologia proposta, junto ao simulador numérico, quanto para avaliar o desempenho das regras quando aplicadas ao gerenciamento das variáveis de controle. Os resultados das simulações mostraram que as regras de controle obtidas por meio da metodologia proposta apresentaram melhora no indicador econômico de produção para 4 dos 9 poços considerados. Ressalta-se que as regras do conjunto inicial, geradas pelo simulador, já eram resultado de um processo de busca e otimização. A aplicação das regras no gerenciamento das variáveis de controle, avaliadas por meio de um modelo substituto rápido, mostrou-se eficaz para 8 dos 9 poços testados. Os resultados reportados neste trabalho mostram evidências em favor da metodologia proposta e podem ser considerados como um passo inicial para a geração de regras de controle aplicáveis na prática para a tomada de decisão de curto prazo.

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A review on closed-loop field development and management

Cited by 23 publications

References 101 publications

Using Reorderable Heatmaps as a Support for Data Assimilation in Petroleum Reservoir Management and Development Processes

Using Reorderable Heatmaps as a Support for Data Assimilation in Petroleum Reservoir Management and Development Processes

Efficient Optimization and Robust Value Quantification of Enhanced Oil Recovery Strategies

Geração de regras de controle via mineração de dados para a otimização de indicador de produção para poços inteligentes

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