A review of proxy modeling applications in numerical reservoir simulation

Jaber, Ahmed Khalil; Al-Jawad, Sameer Noori; Alhuraishawy, Ali K.

doi:10.1007/s12517-019-4891-1

Cited by 42 publications

(14 citation statements)

References 79 publications

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“…Figure 2 presents the various ways PMs are classified in the literature. [11], Bartz and Zaefferer [12], Barton and Meckesheimer [13], and Jaber et al [14]).…”

Section: Proxy Modeling Classificationmentioning

confidence: 99%

“…Conversely, a physicsbased approach modifies the governing equations of the problem to make it computationally cheaper, which will be discussed later in this section. Black-box PMs are further divided into parametric and nonparametric, according to the nature of the unknown param- [11], Bartz and Zaefferer [12], Barton and Meckesheimer [13], and Jaber et al [14]).…”

Section: Proxy Modeling Classificationmentioning

confidence: 99%

“…In this work, we provide a new classification of PMs, including multi-fidelity models (MFMs), reduced-order models (ROMs), traditional proxy models (TPMs), and smart proxy models (SPMs). The existing classification in the literature could not cover all proxy modes [8][9][10][11][12][13][14][15], and in some studies, different proxy models are even considered as one model [16,17]. To fully comprehend the models, the advantages and disadvantages of each class are discussed.…”

Section: Introductionmentioning

confidence: 99%

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A Review of Proxy Modeling Highlighting Applications for Reservoir Engineering

2022

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Numerical models can be used for many purposes in oil and gas engineering, such as production optimization and forecasting, uncertainty analysis, history matching, and risk assessment. However, subsurface problems are complex and non-linear, and making reliable decisions in reservoir management requires substantial computational effort. Proxy models have gained much attention in recent years. They are advanced non-linear interpolation tables that can approximate complex models and alleviate computational effort. Proxy models are constructed by running high-fidelity models to gather the necessary data to create the proxy model. Once constructed, they can be a great choice for different tasks such as uncertainty analysis, optimization, forecasting, etc. The application of proxy modeling in oil and gas has had an increasing trend in recent years, and there is no consensus rule on the correct choice of proxy model. As a result, it is crucial to better understand the advantages and disadvantages of various proxy models. The existing work in the literature does not comprehensively cover all proxy model types, and there is a considerable requirement for fulfilling the existing gaps in summarizing the classification techniques with their applications. We propose a novel categorization method covering all proxy model types. This review paper provides a more comprehensive guideline on comparing and developing a proxy model compared to the existing literature. Furthermore, we point out the advantages of smart proxy models (SPM) compared to traditional proxy models (TPM) and suggest how we may further improve SPM accuracy where the literature is limited. This review paper first introduces proxy models and shows how they are classified in the literature. Then, it explains that the current classifications cannot cover all types of proxy models and proposes a novel categorization based on various development strategies. This new categorization includes four groups multi-fidelity models (MFM), reduced-order models (ROM), TPM, and SPM. MFMs are constructed based on simplifying physics assumptions (e.g., coarser discretization), and ROMs are based on dimensional reduction (i.e., neglecting irrelevant parameters). Developing these two models requires an in-depth knowledge of the problem. In contrast, TPMs and novel SPMs require less effort. In other words, they do not solve the complex underlying mathematical equations of the problem; instead, they decouple the mathematical equations into a numeric dataset and train statistical/AI-driven models on the dataset. Nevertheless, SPMs implement feature engineering techniques (i.e., generating new parameters) for its development and can capture the complexities within the reservoir, such as the constraints and characteristics of the grids. The newly introduced parameters can help find the hidden patterns within the parameters, which eventually increase the accuracy of SPMs compared to the TPMs. This review highlights the superiority of SPM over traditional statistical/AI-based proxy models. Finally, the application of various proxy models in the oil and gas industry, especially in subsurface modeling with a set of real examples, is presented. The introduced guideline in this review aids the researchers in obtaining valuable information on the current state of PM problems in the oil and gas industry.

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“…Figure 2 presents the various ways PMs are classified in the literature. [11], Bartz and Zaefferer [12], Barton and Meckesheimer [13], and Jaber et al [14]).…”

Section: Proxy Modeling Classificationmentioning

confidence: 99%

Section: Proxy Modeling Classificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Review of Proxy Modeling Highlighting Applications for Reservoir Engineering

2022

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“…Proxy or surrogate modeling in reservoir engineering has been an active research area, whose main goal is to develop an efficient substitute for the full reservoir simulation model to significantly reduce the computational cost and meanwhile retain sufficient prediction accuracy on desired outputs (Zubarev, 2009;Amini and Mohaghegh, 2019;Jaber et al, 2019). Proxy models can be constructed based on different methods.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence Aided Proxy Model for Water Front Tracking in Fractured Carbonate Reservoirs

Zhang

Hoteit

Katterbauer

et al. 2021

Day 2 Mon, November 29, 2021

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Saturation mapping in fractured carbonate reservoirs is a major challenge for oil and gas companies. The fracture channels within the reservoir are the primary water conductors that shape water front patterns and cause uneven sweep efficiency. Flow simulation for fractured reservoirs is typically time-consuming due to the inherent high nonlinearity. A data-driven approach to capture the main flow patterns is quintessential for efficient optimization of reservoir performance and uncertainty quantification. We employ an artificial intelligence (AI) aided proxy modeling framework for waterfront tracking in complex fractured carbonate reservoirs. The framework utilizes deep neural networks and reduced-order modeling to achieve an efficient representation of the reservoir dynamics to track and determine the fluid flow patterns within the fracture network. The AI-proxy model is examined on a synthetic two-dimensional (2D) fractured carbonate reservoir model. Training dataset including saturation and pressure maps at a series of time steps is generated using a dual-porosity dual-permeability (DPDP) model. Experimental results indicate a robust performance of the AI-aided proxy model, which successfully reproduce the key flow patterns within the reservoir and achieve orders of shorter running time than the full-order reservoir simulation. This suggests the great potential of utilizing the AI-aided proxy model for heavy-simulation-based reservoir applications such as history matching, production optimization, and uncertainty assessment.

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“…As a well-known inverse problem in reservoir simulation, History Matching is significant for reservoir development, management and predictions, which tries to estimate the uncertain parameters (such as porosity and permeability) of a reservoir numerical model based on observed historical production data (such as well rates and pressure) [1][2][3]. 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.…”

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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A review of proxy modeling applications in numerical reservoir simulation

Cited by 42 publications

References 79 publications

A Review of Proxy Modeling Highlighting Applications for Reservoir Engineering

A Review of Proxy Modeling Highlighting Applications for Reservoir Engineering

Artificial Intelligence Aided Proxy Model for Water Front Tracking in Fractured Carbonate Reservoirs

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

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