A data-driven smart proxy model for a comprehensive reservoir simulation

Alenezi, Faisal; Mohaghegh, Shahab D.

doi:10.1109/kacstit.2016.7756063

Cited by 23 publications

(10 citation statements)

References 34 publications

(34 reference statements)

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“…This study is the continuation of a work that has already been published [9]. The previous paper discussed the smart proxy model in a reservoir with only one injection well and several production wells.…”

Section: Methodsmentioning

confidence: 75%

Developing a Smart Proxy for the SACROC Water-Flooding Numerical Reservoir Simulation Model

Alenezi

Mohaghegh

2017

Day 4 Wed, April 26, 2017

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The preferred common tool to estimate the performance of oil and gas fields under different production scenarios is numerical reservoir simulation. A comprehensive numerical reservoir model has tens of millions of grid blocks. The massive potential of the existing numerical reservoir simulation models go unrealized because they are computationally expensive and time-consuming [1]. Therefore, an effective alternative tool is required for fast and reliable decision making. To reduce the required computational time, proxy models are developed. Traditional proxy models are either statistical or reduced order models (ROM). They are developed to substitute the complex numerical simulation by producing a representation of the system at a lower computational cost. However, there are shortcomings associated with these approaches when applied to complex systems. In this study, a novel proxy model approach is presented. The smart proxy model presented in this article is based on artificial intelligence and data-mining techniques. A numerical simulation run is designed for the smart proxy objectives. The static and dynamic data from the simulation run are extracted. Selected data parameters are used to create a spatial-temporal database for the smart proxy model. The smart proxy is trained, calibrated, and validated using a series of neural networks for the targeted reservoir property. To validate the smart proxy model, it is deployed to replicate a blind numerical simulation run. The developed smart proxy model can replicate the simulation outcomes in a very short time (seconds) with an acceptable range of error.

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Section: Methodsmentioning

confidence: 75%

Developing a Smart Proxy for the SACROC Water-Flooding Numerical Reservoir Simulation Model

Alenezi

Mohaghegh

2017

Day 4 Wed, April 26, 2017

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“…SPMs are implemented in various areas such as waterflood monitoring [20,194], gas injection monitoring [21], and WAG monitoring [18] using the grid-based SPM, history matching [19,22], and production optimization in a WAG process [18] using the well-based SPM. A brief summary of the PMs (including the TPMs and SPMs) used in the literature can be found in Table 2.…”

Section: Smart Proxy Models (Spm)mentioning

confidence: 99%

“…A brief summary of the PMs (including the TPMs and SPMs) used in the literature can be found in Table 2. Haghshenas et al [20] Evaluating the effect of injection rates on oil saturation using the grid-based SPM LHS ANN -SPM Alenezi and Mohaghegh [194] Evaluating the effect of injection rates on oil saturation and pressure using the grid-based SPM…”

Section: Smart Proxy Models (Spm)mentioning

confidence: 99%

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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“…He et al (2016) coupled the use of SPM with differential evolution (DE) to perform automatic history matching. Alenezi and Mohaghegh (2016) also built a SPM that reproduced and forecasted the dynamic properties of a reservoir that has been water-flooded. Moreover, Mohaghegh (2018) discussed the utilization of SPM under the context of CO 2 -EOR as a storage mechanism.…”

Section: Introductionmentioning

confidence: 99%

Smart Proxy Modeling of a Fractured Reservoir Model for Production Optimization: Implementation of Metaheuristic Algorithm and Probabilistic Application

Ghahfarokhi

Amar

et al. 2021

Nat Resour Res

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Numerical reservoir simulation has been recognized as one of the most frequently used aids in reservoir management. Despite having high calculability performance, it presents an acute shortcoming, namely the long computational time induced by the complexities of reservoir models. This situation applies aptly in the modeling of fractured reservoirs because these reservoirs are strongly heterogeneous. Therefore, the domains of artificial intelligence and machine learning (ML) were used to alleviate this computational challenge by creating a new class of reservoir modeling, namely smart proxy modeling (SPM). SPM is a ML approach that requires a spatio-temporal database extracted from the numerical simulation to be built. In this study, we demonstrate the procedures of SPM based on a synthetic fractured reservoir model, which is a representation of dual-porosity dual-permeability model. The applied ML technique for SPM is artificial neural network. We then present the application of the smart proxies in production optimization to illustrate its practicality. Apart from applying the backpropagation algorithms, we implemented particle swarm optimization (PSO), which is one of the metaheuristic algorithms, to build the SPM. We also propose an additional procedure in SPM by integrating the probabilistic application to examine the overall performance of the smart proxies. In this work, we inferred that the PSO had a higher chance to improve the reliability of smart proxies with excellent training results and predictive performance compared with the considered backpropagation approaches.

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A data-driven smart proxy model for a comprehensive reservoir simulation

Cited by 23 publications

References 34 publications

Developing a Smart Proxy for the SACROC Water-Flooding Numerical Reservoir Simulation Model

Developing a Smart Proxy for the SACROC Water-Flooding Numerical Reservoir Simulation Model

A Review of Proxy Modeling Highlighting Applications for Reservoir Engineering

Smart Proxy Modeling of a Fractured Reservoir Model for Production Optimization: Implementation of Metaheuristic Algorithm and Probabilistic Application

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