Investigating the capability of data-driven proxy models as solution for reservoir geological uncertainty quantification

Kolajoobi, Rasool Amiri; Haddadpour, Hosein; Niri, Mohammad Emami

doi:10.1016/j.petrol.2021.108860

Cited by 10 publications

(2 citation statements)

References 22 publications

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“…In the literature, a wide variety of techniques can be considered as TPMs. This type of proxy can approximate different areas in the subsurface or surface environment such as production optimization [100,167], uncertainty quantification [168,169], history matching [170,171], field development planning [172], risk analysis [173,174], gas lift optimization [109,175], gas storage management [176], screening purposes in fractured reservoirs [177], hydraulic fracturing [178], assessing the petrophysical and geomechanical properties of shale reservoirs [179], waterflooding optimization [180][181][182][183], well placement optimization [184][185][186], wellhead data interpretation [187], and well control optimization [188]. Additionally, TPMs have a wide range of applications in various EOR recovery techniques such as steam-assisted gravity drainage (SAGD) [189], CO 2 -gas-assisted gravity drainage (GAGD) [190], water alternating gas (WAG) [191,192], and chemical flooding [193].…”

Section: Traditional Proxy Models (Tpm)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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Section: Traditional Proxy Models (Tpm)mentioning

confidence: 99%

A Review of Proxy Modeling Highlighting Applications for Reservoir Engineering

2022

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“…Later, the use of AI in this field expanded. Up to this date, many successes have been made in cases such as predicting reservoir properties (i.e., permeability, matrix porosity, and fracture) in dual porosity reservoirs (Alajmi and Ertekin, 2007), history matching for a hydrocarbon field (Haghshenas et al, 2020(Haghshenas et al, , 2021Kolajoobi et al, 2021;Shahkarami et al, 2014), well placement optimization (e.g., Kolajoobi et al, 2023), uncertainty evaluation in reservoir performance prediction (Haddadpour and Emami Niri, 2021), CO2 storage (Van Si and Chon, 2018;Vo Thanh et al, 2020), and Hydrogen Storage (Rahimi et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Design of Artificial Neural Network for predicting the reduction in permeability of porous media as a result of polymer gel injection

Kamel Targhi,

Emami Niri,

L.J. Zitha

2023

Geoenergy Science and Engineering

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Application of machine and deep learning techniques to estimate NMR-derived permeability from conventional well logs and artificial 2D feature maps

Masroor

Niri

Rajabi-Ghozloo

et al. 2022

J Petrol Explor Prod Technol

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Nuclear magnetic resonance (NMR) logs can provide information on some critical reservoir characteristics, such as permeability, which are rarely obtainable from conventional well logs. Nevertheless, high cost and operational constraints limit the wide application of NMR logging tools. In this study, a machine learning (ML)-based procedure is developed for fast and accurate estimation of NMR-derived permeability from conventional logs. Following a comprehensive preprocessing on the collected data, the procedure is trained and tested on a well log dataset, with selected conventional logs as inputs, and NMR-derived permeability as target, shallow and deep learning (DL) methods are applied to estimate permeability from selected conventional logs through artificial production of NMR-derived information from the input data. Three supervised ML algorithms are utilized and evaluated, including random forest (RF), group method of data handling (GMDH), and one-dimensional convolutional neural network (1D-CNN). Additionally, a modified two-dimensional CNN (named as Residual 2D-CNN) is developed which is fed by artificial 2D feature maps, generated from available conventional logs. The hyper-parameters of the ML and DL models are optimized using genetic algorithm (GA) to improve their performances. By comparing the output of each model with the permeability derived from NMR log, it is illustrated that nonlinear machine and deep learning techniques are helpful in estimation of NMR permeability. The obtained accuracy of RF, GMDH, 1D-CNN and Res 2D-CNN models, respectively, is 0.90, 0.90, 0.91 and 0.97 which indicate that Res 2D-CNN model is the most efficient method among the other applied techniques. This research also highlights the importance of using generated feature maps for training Res 2D-CNN model, and the essential effect of the applied modifications (i.e., implementing residual and deeper bottleneck architectures) on improving the accuracy of the predicted output and reducing the training time.

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Investigating the capability of data-driven proxy models as solution for reservoir geological uncertainty quantification

Cited by 10 publications

References 22 publications

A Review of Proxy Modeling Highlighting Applications for Reservoir Engineering

A Review of Proxy Modeling Highlighting Applications for Reservoir Engineering

Design of Artificial Neural Network for predicting the reduction in permeability of porous media as a result of polymer gel injection

Application of machine and deep learning techniques to estimate NMR-derived permeability from conventional well logs and artificial 2D feature maps

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