Smart Proxy: An Innovative Reservoir Management Tool; Case Study of a Giant Mature Oilfield in the UAE

Mohaghegh, Shahab D.; Abdulla, Fareed Al Sayed; Abdou, Medhat; Gaskari, Razi; Maysami, Mohammad

doi:10.2118/177829-ms

Cited by 9 publications

(3 citation statements)

References 4 publications

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“…The development steps will be discussed in detail in Section 3. SPMs are capable of reproducing the high-fidelity models without reducing the physics and order of the original system, and they do not decrease the resolution of the model in time or space [35]. SPMs can be developed as grid-based or well-based models depending on the objective of the study [36].…”

Section: Proxy Modeling Classificationmentioning

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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Section: Proxy Modeling Classificationmentioning

confidence: 99%

A Review of Proxy Modeling Highlighting Applications for Reservoir Engineering

2022

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“…With this breakthrough, these surrogate models were implemented on different real fields in Saudi Arabia for geological uncertainty analysis (Mohaghegh et al 2012a, c). Mohaghegh et al (2012bMohaghegh et al ( , 2015 then reformulated the concept of SPM by categorizing it as grid-based and well-based. As the nomenclatures imply, gridbased SPM is done for the analysis of numerical model at grid block level, whereas well-based SPM is for the analysis at well level.…”

Section: Introductionmentioning

confidence: 99%

“…As the nomenclatures imply, gridbased SPM is done for the analysis of numerical model at grid block level, whereas well-based SPM is for the analysis at well level. Grid-based SPM has been applied in several real-life CO 2 sequestration projects (Mohaghegh et al 2012b), whereas wellbased SPM has been implemented for optimization of production scheduling of a real field in United Arab Emirates (Mohaghegh et al 2015). Besides, the application of SPM was then extended gradually to other domains, such as history matching and enhanced oil recovery (EOR).…”

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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