Hybrid physics and data-driven modeling for unconventional field development and its application to US onshore basin

Park, Jaeyoung; Datta‐Gupta, Akhil; Singh, Ajay; Sankaran, Sathish

doi:10.1016/j.petrol.2021.109008

Cited by 17 publications

(7 citation statements)

References 19 publications

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“…For unconventional resources, Xue et al (2023) proposed a deep learning model driven jointly by the decline curve analysis model and production data for the production performance prediction of tight gas wells. Park et al (2021) developed a hybrid model by combining physics and data-driven approach for optimum unconventional field development. The existing methods typically require labeled data, particularly the precise solution of PDEs.…”

Section: Data and Physics Jointly Driven Methodsmentioning

confidence: 99%

Artificial intelligence methods for oil and gas reservoir development: Current progresses and perspectives

Xue,

Li,

Dou

2023

Adv. Geo-Energy Res.

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Artificial neural networks have been widely applied in reservoir engineering. As a powerful tool, it changes the way to find solutions in reservoir simulation profoundly. Deep learning networks exhibit robust learning capabilities, enabling them not only to detect patterns in data, but also uncover underlying physical principles, incorporate prior knowledge of physics, and solve complex partial differential equations. This work presents the latest research advancements in the field of petroleum reservoir engineering, covering three key research directions based on artificial neural networks: data-driven methods, physics driven artificial neural network partial differential equation solver, and data and physics jointly driven methods. In addition, a wide range of neural network architectures are reviewed, including fully connected neural networks, convolutional neural networks, recurrent neural networks, and so on. The basic principles of these methods and their limitations in practical applications are also outlined. The future trends of artificial intelligence methods for oil and gas reservoir development are further discussed. The large language models are the most advanced neural networks so far, it is expected to be applied in reservoir simulation to predict the development performance.

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Section: Data and Physics Jointly Driven Methodsmentioning

confidence: 99%

Artificial intelligence methods for oil and gas reservoir development: Current progresses and perspectives

Xue,

Li,

Dou

2023

Adv. Geo-Energy Res.

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“…And these models do not take into account the physical law of seepage in tight gas reservoirs. In order to overcome the disadvantage of a pure data-driven model's adaptability, PARK [31] proposed a seepage model based on the physical model and mixed data-driven model in 2020. The results show that, even if the amount of data is reduced, the addition of the physical model can still maintain the high accuracy of the hybrid model.…”

Section: Introductionmentioning

confidence: 99%

“…The research of the above two scholars [31,32] shows that the combined use of a physical model and data-driven model can increase the prediction accuracy of the model and improve the generalization ability and anti-interference ability of the pure data-driven model. This paper combines the traditional decline curve model with the neural network of the data-driven model, and embeds the decline curve model into the optimization and adjustment of the data-driven model through driving and stimulating in the process of neural network learning.…”

Section: Introductionmentioning

confidence: 99%

Production Prediction Model of Tight Gas Well Based on Neural Network Driven by Decline Curve and Data

Chen,

Qu,

Liu

et al. 2024

Processes

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The accurate prediction of gas well production is one of the key factors affecting the economical and efficient development of tight gas wells. The traditional oil and gas well production prediction method assumes strict conditions and has a low prediction accuracy in actual field applications. At present, intelligent algorithms based on big data have been applied in oil and gas well production prediction, but there are still some limitations. Only learning from data leads to the poor generalization ability and anti-interference ability of prediction models. To solve this problem, a production prediction method of tight gas wells based on the decline curve and data-driven neural network is established in this paper. Based on the actual production data of fractured horizontal wells in three tight gas reservoirs in the Ordos Basin, the prediction effect of the Arps decline curve model, the SPED decline curve model, the MFF decline curve model, and the combination of the decline curve and data-driven neural network model is compared and analyzed. The results of the case analysis show that the MFF model and the combined data-driven model have the highest accuracy, the average absolute percentage error is 14.11%, and the root-mean-square error is 1.491, which provides a new method for the production prediction of tight gas wells in the Ordos Basin.

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“…Jagtap et al [38] proposed a conservative physics-informed neural network (cPINN) for solving nonlinear PDEs by decomposing the computational domain into different sub-domains and enforcing the flux continuity in the strong form along the sub-domain interfaces. Park et al [39] constructed a hybrid model for optimum unconventional field development by combining unconventional reservoir uncalibrated priors and data generated by a numerical simulator. To solve the two-phase immersible flow problem governed by the Buckley-Leverett equation [40,41], they used physics-informed neural networks and obtained a physical solution by adding a diffusion term or an observational amount to the PDEs.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Porous Media Fluid Flow with Spatial Heterogeneity Using Criss-Cross Physics-Informed Convolutional Neural Networks

Han,

Xue,

Jia

et al. 2024

CMES

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Recent advances in deep neural networks have shed new light on physics, engineering, and scientific computing. Reconciling the data-centered viewpoint with physical simulation is one of the research hotspots. The physicsinformed neural network (PINN) is currently the most general framework, which is more popular due to the convenience of constructing NNs and excellent generalization ability. The automatic differentiation (AD)-based PINN model is suitable for the homogeneous scientific problem; however, it is unclear how AD can enforce flux continuity across boundaries between cells of different properties where spatial heterogeneity is represented by grid cells with different physical properties. In this work, we propose a criss-cross physics-informed convolutional neural network (CC-PINN) learning architecture, aiming to learn the solution of parametric PDEs with spatial heterogeneity of physical properties. To achieve the seamless enforcement of flux continuity and integration of physical meaning into CNN, a predefined 2D convolutional layer is proposed to accurately express transmissibility between adjacent cells. The efficacy of the proposed method was evaluated through predictions of several petroleum reservoir problems with spatial heterogeneity and compared against state-of-the-art (PINN) through numerical analysis as a benchmark, which demonstrated the superiority of the proposed method over the PINN.

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Hybrid physics and data-driven modeling for unconventional field development and its application to US onshore basin

Cited by 17 publications

References 19 publications

Artificial intelligence methods for oil and gas reservoir development: Current progresses and perspectives

Artificial intelligence methods for oil and gas reservoir development: Current progresses and perspectives

Production Prediction Model of Tight Gas Well Based on Neural Network Driven by Decline Curve and Data

Prediction of Porous Media Fluid Flow with Spatial Heterogeneity Using Criss-Cross Physics-Informed Convolutional Neural Networks

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