Extended Brugge benchmark case for history matching and water flooding optimization

Peters, E.; Chen, Y.; Leeuwenburgh, O.; Oliver, Dean S.

doi:10.1016/j.cageo.2012.07.018

Cited by 44 publications

(28 citation statements)

References 24 publications

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“…In the second experiment, we use data from the Brugge benchmark workshop organized in 2009 [16,17]. In the original benchmark study, the 'truth' case used to generate the data was not disclosed, and therefore, in this work, we use a new 'truth' honouring all well logs, geological descriptions and distributions of geological model parameters, porosity/permeability relations and the geological structure of the Brugge field.…”

Section: Brugge Model Examplementioning

confidence: 99%

Determination of lower and upper bounds of predicted production from history-matched models

et al. 2016

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We present a method to determine lower and upper bounds to the predicted production or any other economic objective from history-matched reservoir models. The method consists of two steps: 1) performing a traditional computer-assisted history match of a reservoir model with the objective to minimize the mismatch between predicted and observed production data through adjusting the grid block permeability values of the model. 2) performing two optimization exercises to minimize and maximize an economic objective over the remaining field life, for a fixed production strategy, by manipulating the same grid block permeabilities, however without significantly changing the mismatch obtained under step 1. This is accomplished through a hierarchical optimization procedure that limits the solution space of a secondary optimization problem to the (approximate) null space of the primary optimization problem. We applied this procedure to two different reservoir models. We performed a history match based on synthetic data, starting from a uniform prior and using a gradient-based minimization procedure. After history matching, minimization and maximization of the net present value (NPV), using a fixed control strategy, were executed as secondary optimization problems by changing the model parameters while staying close to the null space of the primary optimization problem. In other words, we optimized the secondary objective functions, while requiring that optimality of the primary objective (a good history match) was preserved. This method therefore provides a way to quantify the economic consequences of the wellknown problem that history matching is a strongly ill-posed problem. We also investigated how this method can be used as a means to assess the cost-effectiveness of acquiring different data types to reduce the uncertainty in the expected NPV.

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Section: Brugge Model Examplementioning

confidence: 99%

Determination of lower and upper bounds of predicted production from history-matched models

et al. 2016

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“…In order to keep the Brugge test case as a challenging problem for testing and comparing different techniques in closed-loop reservoir management, Peters et al (2013) provided a lot of additional data including well constraints and production history from individual well completions for another 20 years, as well as the updated data of oil saturation and reservoir pressure.…”

Section: Closed-loop Reservoir Managementmentioning

confidence: 99%

A review of closed-loop reservoir management

Hou

Zhou

Zhang³

et al. 2015

Pet. Sci.

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The closed-loop reservoir management technique enables a dynamic and real-time optimal production schedule under the existing reservoir conditions to be achieved by adjusting the injection and production strategies. This is one of the most effective ways to exploit limited oil reserves more economically and efficiently. There are two steps in closed-loop reservoir management: automatic history matching and reservoir production optimization. Both of the steps are large-scale complicated optimization problems. This paper gives a general review of the two basic techniques in closed-loop reservoir management; summarizes the applications of gradient-based algorithms, gradient-free algorithms, and artificial intelligence algorithms; analyzes the characteristics and application conditions of these optimization methods; and finally discusses the emphases and directions of future research on both automatic history matching and reservoir production optimization.

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“…The original facies modeling workflow was provided by TNO to generate realizations in preparation for the SPE Applied Technology Workshop (ATW) held in Brugge in June 2008. More description of the reservoir model can be found in [19,20].…”

Section: Geological Descriptionmentioning

confidence: 99%

“…The Brugge field has been used as a benchmark case for history matching and water flooding optimization [19,20]. The geological model of the Brugge field contains five facies types formed in different depositional environments.…”

Section: Introductionmentioning

confidence: 99%

Facies Parameterization and Estimation for Complex Reservoirs - The Brugge Field

2015

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Data assimilation using ensemble-based inversion methods has been successfully applied for parameter estimation in reservoir models. However, in certain complex reservoir models, it remains challenging to estimate the model parameters and preserve the geological realism simultaneously. In particular, when handling special reservoir model parameters such as facies types concerning fluvial channels, geological realism becomes one of the key concerns. The main objective of this work is to address this issue on a real field with a newly extended version of a recently proposed facies parameterization approach coupled with ensemble-based data assimilation techniques. The proposed workflow combines the new facies parameterization and the IAGM filter into the data assimilation framework for channelized reservoirs. In order to handle discrete facies parameters, we combine probability maps and truncated Gaussian fields to obtain a continuous parameterization of the facies fields. For the data assimilation we use the Iterative Adaptive Gaussian Mixture (IAGM) Filter, which is an efficient history matching approach that incorporates a resampling routine which allows us to regenerate facies fields using information from the updated probability maps. This workflow is evaluated, for the first time, on a complex field case -the Brugge field. This reservoir model consist of layers with complex channelized structures and layers characterized by reservoir properties generated with variograms. With limited prior knowledge on uncertain facies models, this workflow is shown to be able to preserve the channel continuity while reducing the reservoir model uncertainty with IAGM. When applied to complex real field studies, it provides a geologically consistent and realistic reservoir model which leads to improved capability of predicting subsurface flow behaviors.

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Extended Brugge benchmark case for history matching and water flooding optimization

Cited by 44 publications

References 24 publications

Determination of lower and upper bounds of predicted production from history-matched models

Determination of lower and upper bounds of predicted production from history-matched models

A review of closed-loop reservoir management

Facies Parameterization and Estimation for Complex Reservoirs - The Brugge Field

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