Application of multi-segment well approach: Dynamic modeling of hydraulic fractures

Du, Shichao; Yoshida, Nozomu; Liang, Baosheng; Chen, Jianping

doi:10.1016/j.jngse.2016.07.028

Cited by 28 publications

(4 citation statements)

References 16 publications

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“…In general, simple fracture geometries such as bi‐wing fractures and orthogonal fracture networks are often modeled using local grid refinement (LRG) to capture the transient flow behavior from matrix to fractures . In recent years, some advanced numerical methods such as unstructured grid, multisegment well approach, and embedded discrete fracture model (EDFM) are developed. However, the above numerical approaches are still challenging to use efficiently due to very complicated gridding issues and an expensive computational cost for unstructured grid method, and complexities in development of computational codes for EDFM preprocessor.…”

Section: Introductionmentioning

confidence: 99%

A semianalytical model for simulating real gas transport in nanopores and complex fractures of shale gas reservoirs

Wang

et al. 2017

AIChE Journal

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An efficient gridless semianalytical model was developed to simulate real gas transport in shale formation with nanopores and complex fracture geometry. This model incorporates multiple physics such as gas desorption, adsorbed gas porosity, gas slippage and diffusion, residual water saturation, non-Darcy flow, choke skin, and pressuredependent matrix permeability, and fracture conductivity. Additionally, this model is easy to handle complex fracture geometry through dividing fractures into a number of segments and nodes. We verified the model against a numerical model and an analytical model for bi-wing hydraulic fractures. After validation, the impacts of all these physics on well performance were evaluated in detail through a series of case studies. The simulation results confirm that modeling of gas production from complex fracture geometry as well as modeling important physics in shale gas reservoirs is significant. This study improves our understanding of critical physics affecting gas recovery in shale gas reservoirs.

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Section: Introductionmentioning

confidence: 99%

A semianalytical model for simulating real gas transport in nanopores and complex fractures of shale gas reservoirs

Wang

et al. 2017

AIChE Journal

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“…It is important to accurately model the influence of complex fractures on well performance in reservoir simulation. Traditional LGR approach can accurately model the fluid transport from matrix to fractures, but it is difficult to deal with complex non-planar fractures32. The EDFM is an efficient approach to handle the complex fracture geometries through discretizing the fractures into segments with matrix cell boundaries333435.…”

Section: Discussionmentioning

confidence: 99%

A Comprehensive Numerical Model for Simulating Fluid Transport in Nanopores

Zhang

Sepehrnoori

et al. 2017

Sci Rep

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Since a large amount of nanopores exist in tight oil reservoirs, fluid transport in nanopores is complex due to large capillary pressure. Recent studies only focus on the effect of nanopore confinement on single-well performance with simple planar fractures in tight oil reservoirs. Its impacts on multi-well performance with complex fracture geometries have not been reported. In this study, a numerical model was developed to investigate the effect of confined phase behavior on cumulative oil and gas production of four horizontal wells with different fracture geometries. Its pore sizes were divided into five regions based on nanopore size distribution. Then, fluid properties were evaluated under different levels of capillary pressure using Peng-Robinson equation of state. Afterwards, an efficient approach of Embedded Discrete Fracture Model (EDFM) was applied to explicitly model hydraulic and natural fractures in the reservoirs. Finally, three fracture geometries, i.e. non-planar hydraulic fractures, non-planar hydraulic fractures with one set natural fractures, and non-planar hydraulic fractures with two sets natural fractures, are evaluated. The multi-well performance with confined phase behavior is analyzed with permeabilities of 0.01 md and 0.1 md. This work improves the analysis of capillarity effect on multi-well performance with complex fracture geometries in tight oil reservoirs.

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“…Current shale gas well production and EUR prediction methods are mainly based on production decline curve analysis and its extension, − physics-based simulation prediction, − and data-driven machine learning methods. − Among them, the production decline curve analysis method is based on the fitting of production history curve to realize the prediction of production, which lacks direct consideration of physical process and is an empirical method. The physical-based simulation prediction methods require consideration of complex fracture networks and coupled flow-transport-deformation mechanisms of gas, which is a great challenge to the relevant data and modeling cost, making it difficult to establish an effective model to predict the production and EUR of shale gas wells. − Data-driven machine learning methods avoid the assumptions of physical models and can use data science and technology to gain a deeper understanding of what drives well production .…”

Section: Introductionmentioning

confidence: 99%

Toward Production Forecasting for Shale Gas Wells Using Transfer Learning

et al. 2023

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Accurate prediction of shale gas well production and estimated ultimate recovery (EUR) is always a difficult and hot spot in shale gas development. In particular, the production and EUR prediction of shale gas wells in new production blocks are faced with the lack of field gas well data and the difficulty of model development. In view of the above problems, this study proposes a new deep transfer learning strategy, which uses transfer component analysis (TCA) and deep neural network (DNN) to achieve shale gas well production and EUR prediction across formations/blocks. The feature extractor based on TCA can narrow the input feature distribution of the source and the target domains. The neural network model can be used to establish a domainadaptive transfer learning model without the prediction performance degradation caused by distribution offset. Validity and accuracy of the model were analyzed using gas well data from Weiyuan and Luzhou blocks in Sichuan Basin, China. The results appear that the reasonable application of TCA can greatly improve the prediction performance of shale gas well transfer learning model. For data sets of the same size, compared with the transfer learning model developed by classical machine learning algorithms, the proposed neural network-based transfer learning model can significantly improve the accuracy of production prediction across formations/ blocks. In addition, the proposed model can also be extended to other types of oil and gas production prediction tasks cross formations/blocks.

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Application of multi-segment well approach: Dynamic modeling of hydraulic fractures

Cited by 28 publications

References 16 publications

A semianalytical model for simulating real gas transport in nanopores and complex fractures of shale gas reservoirs

A semianalytical model for simulating real gas transport in nanopores and complex fractures of shale gas reservoirs

A Comprehensive Numerical Model for Simulating Fluid Transport in Nanopores

Toward Production Forecasting for Shale Gas Wells Using Transfer Learning

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