Accelerating Physics-Based Simulations Using End-to-End Neural Network Proxies: An Application in Oil Reservoir Modeling

Navrátil, Jǐŕı; King, Alan; Rios, Jesus; Κόλλιας, Γεώργιος; Torrado, Ruben Rodriguez; Codas, Andrés

doi:10.3389/fdata.2019.00033

Cited by 25 publications

(13 citation statements)

References 15 publications

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“…There are also other interesting literatures (Nait Amar et al 2018Navrátil et al 2019;Alakeely and Horne 2020;Ng et al 2021) that discuss and present the use of ML methods in the establishment of proxies of numerical models in petroleum domain, especially for reservoir engineering. Regarding this, there is a riveting insight being provided by Nait Amar et al (2018) about the modeling of proxies, which is the difference between static and dynamic proxy models.…”

Section: Introductionmentioning

confidence: 99%

Application of nature-inspired algorithms and artificial neural network in waterflooding well control optimization

Ghahfarokhi

Amar

2021

J Petrol Explor Prod Technol

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With the aid of machine learning method, namely artificial neural networks, we established data-driven proxy models that could be utilized to maximize the net present value of a waterflooding process by adjusting the well control injection rates over a production period. These data-driven proxies were maneuvered on two different case studies, which included a synthetic 2D reservoir model and a 3D reservoir model (the Egg Model). Regarding the algorithms, we applied two different nature-inspired metaheuristic algorithms, i.e., particle swarm optimization and grey wolf optimization, to perform the optimization task. Pertaining to the development of the proxy models, we demonstrated that the training and blind validation results were excellent (with coefficient of determination, R2 being about 0.99). For both case studies and the optimization algorithms employed, the optimization results obtained using the proxy models were all within 5% error (satisfied level of accuracy) compared with reservoir simulator. These results confirm the usefulness of the methodology in developing the proxy models. Besides that, the computational cost of optimization was significantly reduced using the proxies. This further highlights the significant benefits of employing the proxy models for practical use despite being subject to a few constraints.

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Section: Introductionmentioning

confidence: 99%

Application of nature-inspired algorithms and artificial neural network in waterflooding well control optimization

Ghahfarokhi

Amar

2021

J Petrol Explor Prod Technol

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“…To reduce the computational cost of optimization, researchers often replace an accurate but computationally expensive physical model with a quickly computable model for approximation of objective function -the so-called surrogate model [6]. Surrogate modeling is actively used to solve problems from different fields: simulating oil reservoirs for maximizing the total production of oil value and forecasting the most profitable oilfields [7], optimizing of the heatgenerating components in small electronic devices for control of temperature field [8], obtaining the hydrodynamic performance indexes of ship hull form for increasing its strength [9]. Researchers consider a wide variety of models as surrogates: from classical methods (polynomial regression, kriging, support vector regression) to complex ensemble models, deep neural networks, long-short term memory networks, etc [10].…”

Section: Related Workmentioning

confidence: 99%

Surrogate-Assisted Evolutionary Generative Design Of Breakwaters Using Deep Convolutional Networks

Starodubcev¹,

Nikitin²,

Kalyuzhnaya³

2022

Preprint

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In the paper, a multi-objective evolutionary surrogate-assisted approach for the fast and effective generative design of coastal breakwaters is proposed. To approximate the computationally expensive objective functions, the deep convolutional neural network is used as a surrogate model. This model allows optimizing a configuration of breakwaters with a different number of structures and segments. In addition to the surrogate, an assistant model was developed to estimate the confidence of predictions. The proposed approach was tested on the synthetic water area, the SWAN model was used to calculate the wave heights. The experimental results confirm that the proposed approach allows obtaining more effective (less expensive with better protective properties) solutions than non-surrogate approaches for the same time.

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“…ML methods have been used to accelerate oil reservoir simulations and achieve higher accuracy as well. Navrátil et al (2019) developed a model using deep learning methods to accelerate the simulations of oil reservoirs by three orders of magnitude compared to industry-strength physics-based partial differential equations (PDE) solvers.…”

Section: Reservoir Simulation and Field Development Optimizationmentioning

confidence: 99%

A systematic review of data science and machine learning applications to the oil and gas industry

Tariq

Aljawad

Hasan

et al. 2021

J Petrol Explor Prod Technol

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This study offered a detailed review of data sciences and machine learning (ML) roles in different petroleum engineering and geosciences segments such as petroleum exploration, reservoir characterization, oil well drilling, production, and well stimulation, emphasizing the newly emerging field of unconventional reservoirs. The future of data science and ML in the oil and gas industry, highlighting what is required from ML for better prediction, is also discussed. This study also provides a comprehensive comparison of different ML techniques used in the oil and gas industry. With the arrival of powerful computers, advanced ML algorithms, and extensive data generation from different industry tools, we see a bright future in developing solutions to the complex problems in the oil and gas industry that were previously beyond the grip of analytical solutions or numerical simulation. ML tools can incorporate every detail in the log data and every information connected to the target data. Despite their limitations, they are not constrained by limiting assumptions of analytical solutions or by particular data and/or power processing requirements of numerical simulators. This detailed and comprehensive study can serve as an exclusive reference for ML applications in the industry. Based on the review conducted, it was found that ML techniques offer a great potential in solving problems in almost all areas of the oil and gas industry involving prediction, classification, and clustering. With the generation of huge data in everyday oil and gas industry activates, machine learning and big data handling techniques are becoming a necessity toward a more efficient industry.

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Accelerating Physics-Based Simulations Using End-to-End Neural Network Proxies: An Application in Oil Reservoir Modeling

Cited by 25 publications

References 15 publications

Application of nature-inspired algorithms and artificial neural network in waterflooding well control optimization

Application of nature-inspired algorithms and artificial neural network in waterflooding well control optimization

Surrogate-Assisted Evolutionary Generative Design Of Breakwaters Using Deep Convolutional Networks

A systematic review of data science and machine learning applications to the oil and gas industry

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