Investigation of different production performances in shale gas wells using assisted history matching: Hydraulic fractures and reservoir characterization from production data

Tripoppoom, Sutthaporn; Xie, Jinsheng; Rui, Yong; Wu, Jianfa; Yu, Wei; Sepehrnoori, Kamy; Miao, Jijun; Chang, Cheng; Li, Ning

doi:10.1016/j.fuel.2020.117097

Cited by 28 publications

(18 citation statements)

References 20 publications

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“…Generally, bottomhole pressure (BHP) and water flow rate should be the response variables for shale gas reservoir. According to Bentéjac et al, the Markov Chain-Monte Carlo (MCMC) algorithm is used to systematically sample the input variables to ensure that the inversion combination is representative, and the global error value can be minimized [26]. After multiple iterations are finished, abundant simulation results are obtained.…”

Section: Edfm-ai For Modeling Parent-childmentioning

confidence: 99%

Automatic History Matching of Parent-Child Wells in Shale Gas Reservoirs with Complex Fracture Hits

et al. 2022

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It is excessively crucial for performing production match of parent-child wells automatically by using assisted history matching approach, especially considering the interwell interference scenario. We explored a new EDFM-AI (Embedded Discrete Facture Model-Artificial Intelligence) workflow by taking into account the multiple uncertainty parameters that were composed of fracture height, fracture half-length, fracture conductivity, fracture water saturation, fracture cluster efficiency, and natural fracture conductivity. The history matching algorithm implemented in this study is advanced machine learning model called XGBoost. It could overcome the issue that the proxy model is always imprecise due to the limited training sample data. Specifically, we established a field-scale reservoir model that includes parent-child shale gas wells with fracture hits in the Sichuan basin. By using this sophisticated workflow, two wells’ history matching results are excellent in terms of the inversed solution of the hydraulic fracture properties and natural fracture properties. And then, we perform the production forecasting for parent and child wells, respectively. Consequently, the results show that the best match value for estimated ultimate recovery (EUR) of parent well is 287.4 million cubic meters whereas the best match value for EUR of child well is 187.6 million cubic meters. The reliable reason is that effective fractured area of parent well is larger than that of child well. Besides, the natural fractures have a significant impact on the performance of shale gas wells with fracture hits observed, especially in the short-term production period.

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Section: Edfm-ai For Modeling Parent-childmentioning

confidence: 99%

Automatic History Matching of Parent-Child Wells in Shale Gas Reservoirs with Complex Fracture Hits

et al. 2022

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“…Even if the path of the hydraulic fracture propagation is not affected in this case, a considerable loss of the fracturing fluid occurs. (2) The natural fracture is penetrated by the hydraulic fracture in a closed state (Figure 1(b)). ( 3) After the expansion of the natural fracture, the hydraulic fracture penetrates the natural fracture at their intersection point and continues to expand along the original direction (Figure 1(c)).…”

Section: Plane Model Of Intersection Of Hydraulic and Naturalmentioning

confidence: 99%

“…The fractures are generated around the natural fracture, and their expansion is not evident. (2) The hydraulic fracture penetrates the natural fracture and expands along the original direction. The length of the generated hydraulic fracture is considerably larger than that of the natural fracture; however, few short fractures are present around the natural fracture.…”

Section: Propagation Of Hydraulicmentioning

confidence: 99%

“…In recent years, hydraulic fracturing has been widely used in the engineering practices of petroleum, natural gas, shale gas, and coal mining [1][2][3][4][5]. The propagation of hydraulic fractures plays a key role in the optimisation of hydraulic fracture design and represents a challenging problem corresponding to the theoretical study of hydraulic fractures [6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

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Numerical Simulation of the Influence of Natural Fractures on Hydraulic Fracture Propagation

Song

et al. 2020

Geofluids

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According to the theory of plane mechanics involving the interaction of hydraulic and natural fractures, the law of hydraulic fracture propagation under the influence of natural fractures is verified using theoretical analysis and RFPA2D-Flow numerical simulation approaches. The shear and tensile failure mechanisms of rock are simultaneously considered. Furthermore, the effects of the approach angle, principal stress difference, tensile strength and length of the natural fracture, and elastic modulus and Poisson’s ratio of the reservoir on the propagation law of a hydraulic fracture are investigated. The following results are obtained: (1) The numerical results agree with the experimental data, indicating that the RFPA2D-Flow software can be used to examine the hydraulic fracture propagation process under the action of natural fractures. (2) In the case of a low principal stress difference and low approach angle, the hydraulic fracture likely causes shear failure along the tip of the natural fracture. However, under a high stress difference and high approach angle, the hydraulic fracture spreads directly through the natural fracture along the original direction. (3) When natural fractures with a low tensile strength encounter hydraulic fractures, the hydraulic fractures likely deviate and expand along the natural fractures. However, in the case of natural fractures with a high tensile strength, the natural fracture surface is closed, and the hydraulic fracture directly passes through the natural fracture, propagating along the direction of the maximum principal stress. (4) Under the same principal stress difference, a longer natural fracture corresponds to the easier initiation and expansion of a hydraulic fracture from the tip of the natural fracture. However, when the size of the natural fracture is small, the hydraulic fracture tends to propagate directly through the natural fracture. (5) A smaller elastic modulus and larger Poisson’s ratio of the reservoir result in a larger fracture initiation pressure. The presented findings can provide theoretical guidance regarding the hydraulic fracturing of reservoirs with natural fractures.

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“…To address the issue, Assisted History Matching (AHM) techniques have been proposed to replace labor-intensive and costly manual history matching [1,[4][5][6]. Roughly, these methods for assisted history matching can be divided into two categories [7]: the data assimilation approaches (such as Ensemble Kalman Filter and Ensemble Smoother) and the optimization approaches (such as gradient, evolutionary or data-driven-based algorithms).…”

Section: Introductionmentioning

confidence: 99%

Gaussian Processes Proxy Model with Latent Variable Models and Variogram-Based Sensitivity Analysis for Assisted History Matching

Zhang

Jiang

et al. 2020

Energies

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Reservoir history matching is a well-known inverse problem for production prediction where enormous uncertain reservoir parameters of a reservoir numerical model are optimized by minimizing the misfit between the simulated and history production data. Gaussian Process (GP) has shown promising performance for assisted history matching due to the efficient nonparametric and nonlinear model with few model parameters to be tuned automatically. Recently introduced Gaussian Processes proxy models and Variogram Analysis of Response Surface-based sensitivity analysis (GP-VARS) uses forward and inverse Gaussian Processes (GP) based proxy models with the VARS-based sensitivity analysis to optimize the high-dimensional reservoir parameters. However, the inverse GP solution (GPIS) in GP-VARS are unsatisfactory especially for enormous reservoir parameters where the mapping from low-dimensional misfits to high-dimensional uncertain reservoir parameters could be poorly modeled by GP. To improve the performance of GP-VARS, in this paper we propose the Gaussian Processes proxy models with Latent Variable Models and VARS-based sensitivity analysis (GPLVM-VARS) where Gaussian Processes Latent Variable Model (GPLVM)-based inverse solution (GPLVMIS) instead of GP-based GPIS is provided with the inputs and outputs of GPIS reversed. The experimental results demonstrate the effectiveness of the proposed GPLVM-VARS in terms of accuracy and complexity. The source code of the proposed GPLVM-VARS is available at https://github.com/XinweiJiang/GPLVM-VARS.

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Investigation of different production performances in shale gas wells using assisted history matching: Hydraulic fractures and reservoir characterization from production data

Cited by 28 publications

References 20 publications

Automatic History Matching of Parent-Child Wells in Shale Gas Reservoirs with Complex Fracture Hits

Automatic History Matching of Parent-Child Wells in Shale Gas Reservoirs with Complex Fracture Hits

Numerical Simulation of the Influence of Natural Fractures on Hydraulic Fracture Propagation

Gaussian Processes Proxy Model with Latent Variable Models and Variogram-Based Sensitivity Analysis for Assisted History Matching

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