Assisted history matching using artificial neural network based global optimization method – Applications to Brugge field and a fractured Iranian reservoir

Foroud, Toomaj; Seifi, Abbas; Aminshahidi, Babak

doi:10.1016/j.petrol.2014.07.034

Cited by 42 publications

(14 citation statements)

References 33 publications

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“…Considering petrophysical properties (porosity, permeability and NTG) in each production zone, an ANN-based automated history matching has been done previously by the authors that resulted in 40 different matched models (Foroud et al, 2014). Using one of these matched realizations, a deterministic optimization of the production plan in this field has been performed before (Foroud et al, 2016).…”

Section: Generating Multiple Geological Modelsmentioning

confidence: 99%

An efficient optimization process for hydrocarbon production in presence of geological uncertainty using a clustering method: A case study on Brugge field

Foroud

Seifi

Aminshahidy

2016

Journal of Natural Gas Science and Engineering

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Section: Generating Multiple Geological Modelsmentioning

confidence: 99%

An efficient optimization process for hydrocarbon production in presence of geological uncertainty using a clustering method: A case study on Brugge field

Foroud

Seifi

Aminshahidy

2016

Journal of Natural Gas Science and Engineering

Self Cite

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“…Theoretically, simulated reservoirs models with unknown parameters are calibrated by minimizing the misfits between the simulated and history data, which can be used to forecast the reservoirs production and provide support decisions under different operating conditions and in different production stages. As the reservoirs become large and heterogeneous, the highly nonlinear reservoirs models must be elaborate with numerous grid blocks and reservoir parameters, which brings enormous computational burden and complexity for the numerical optimization [4].…”

Section: Introductionmentioning

confidence: 99%

“…To address the issue, Assisted History Matching (AHM) techniques have been proposed to replace labor-intensive and costly manual history matching [1,[4][5][6]. Roughly, these methods for assisted history matching can be divided into two categories [7]: the data assimilation approaches (such as Ensemble Kalman Filter and Ensemble Smoother) and the optimization approaches (such as gradient, evolutionary or data-driven-based algorithms).…”

Section: Introductionmentioning

confidence: 99%

Gaussian Processes Proxy Model with Latent Variable Models and Variogram-Based Sensitivity Analysis for Assisted History Matching

Zhang

Jiang

et al. 2020

Energies

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Reservoir history matching is a well-known inverse problem for production prediction where enormous uncertain reservoir parameters of a reservoir numerical model are optimized by minimizing the misfit between the simulated and history production data. Gaussian Process (GP) has shown promising performance for assisted history matching due to the efficient nonparametric and nonlinear model with few model parameters to be tuned automatically. Recently introduced Gaussian Processes proxy models and Variogram Analysis of Response Surface-based sensitivity analysis (GP-VARS) uses forward and inverse Gaussian Processes (GP) based proxy models with the VARS-based sensitivity analysis to optimize the high-dimensional reservoir parameters. However, the inverse GP solution (GPIS) in GP-VARS are unsatisfactory especially for enormous reservoir parameters where the mapping from low-dimensional misfits to high-dimensional uncertain reservoir parameters could be poorly modeled by GP. To improve the performance of GP-VARS, in this paper we propose the Gaussian Processes proxy models with Latent Variable Models and VARS-based sensitivity analysis (GPLVM-VARS) where Gaussian Processes Latent Variable Model (GPLVM)-based inverse solution (GPLVMIS) instead of GP-based GPIS is provided with the inputs and outputs of GPIS reversed. The experimental results demonstrate the effectiveness of the proposed GPLVM-VARS in terms of accuracy and complexity. The source code of the proposed GPLVM-VARS is available at https://github.com/XinweiJiang/GPLVM-VARS.

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“…Oil reservoir history matching (HM) is significant for estimating oil reservoir models parameters, making production forecasting and establishing optimal plans for oil reservoir development. [1][2][3] Typically, HM is considered as an inverse problem where the static data (such as core and well logs, etc.) and dynamic observed/production data (such as well rates, pressure, etc.)…”

Section: Introductionmentioning

confidence: 99%

“…Among many HM methods, Bayesian HM naturally makes use of the Bayesian statistical framework to characterize the uncertainty of unknown geological variables with specific priors from oil reservoir theories and represent this kind of inverse problem from a probabilistic point of view, which could be promising. 3,4 Foroud et al 1 summarized the main optimization frameworks and techniques for automatic HM in the following four categories: gradient-based techniques; the meta-heuristic algorithms; response surface modeling; and Ensemble Kalman Filter (EnKF) techniques. All these approaches have their pros and cons.…”

Section: Introductionmentioning

confidence: 99%

Efficient history matching with dimensionality reduction methods for reservoir simulations

et al. 2017

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Oil reservoir history matching is a well-known inverse problem for predicting production by optimizing enormous unknown parameters with numerical simulation. Typically it can be formulated in a Bayesian framework with geological priors. Instead of gradient-based optimization with the possibility of converging to a local minimum, evolutionary algorithms have been introduced to globally find optimal parameters. Due to the high-dimensional parameters, the optimization could become inefficient; therefore, many dimensionality reduction algorithms have been applied in history matching. However, these methods suffer from the linear assumption or the pre-image problem, which could affect the model optimization. In this paper, based on the evolutionary algorithm termed Multi-objective Evolutionary Algorithm Based on Decomposition, which is capable of simultaneously optimizing the parameters with respect to the data of several oil wells, we propose history matching with dimensionality reduction by explicitly utilizing the nonlinear dimensionality reduction model Auto-Encoder to reduce the number of unknown parameters, which can naturally handle the pre-image problem and then improve model performance in terms of precision and complexity. Experimental results based on PUNQ-S3 data verify the efficiency of the newly proposed methods.

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Assisted history matching using artificial neural network based global optimization method – Applications to Brugge field and a fractured Iranian reservoir

Cited by 42 publications

References 33 publications

An efficient optimization process for hydrocarbon production in presence of geological uncertainty using a clustering method: A case study on Brugge field

An efficient optimization process for hydrocarbon production in presence of geological uncertainty using a clustering method: A case study on Brugge field

Gaussian Processes Proxy Model with Latent Variable Models and Variogram-Based Sensitivity Analysis for Assisted History Matching

Efficient history matching with dimensionality reduction methods for reservoir simulations

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