Integration of discrete fracture reconstruction and dual porosity/dual permeability models for gas production analysis in a deformable fractured shale reservoir

Author(s):  
Qixing Zhang ◽  
Bing Hou ◽  
Botao Lin ◽  
Xing Liu ◽  
Yanfang Gao
Energies ◽  
2020 ◽  
Vol 13 (7) ◽  
pp. 1857
Author(s):  
Bowen Hu ◽  
Jianguo Wang ◽  
Zhanguo Ma

A fractal discrete fracture network based model was proposed for the gas production prediction from a fractured shale reservoir. Firstly, this model was established based on the fractal distribution of fracture length and a fractal permeability model of shale matrix which coupled the multiple flow mechanisms of slip flow, Knudsen diffusion, surface diffusion, and multilayer adsorption. Then, a numerical model was formulated with the governing equations of gas transport in both a shale matrix and fracture network system and the deformation equation of the fractured shale reservoir. Thirdly, this numerical model was solved within the platform of COMSOL Multiphysics (a finite element software) and verified through three fractal discrete fracture networks and the field data of gas production from two shale wells. Finally, the sensitivity analysis was conducted on fracture length fractal dimension, pore size distribution, and fracture permeability. This study found that cumulative gas production increases up to 113% when the fracture fractal length dimension increases from 1.5 to the critical value of 1.7. The gas production rate declines more rapidly for a larger fractal dimension (up to 1.7). Wider distribution of pore sizes (either bigger maximum pore size or smaller minimum pore size or both) can increase the matrix permeability and is beneficial to cumulative gas production. A linear relationship is observed between the fracture permeability and the cumulative gas production. Thus, the fracture permeability can significantly impact shale gas production.


2018 ◽  
Vol 241 ◽  
pp. 8-14 ◽  
Author(s):  
Karola Elberg ◽  
Patrick Steuer ◽  
Ute Habermann ◽  
Jürgen Lenz ◽  
Michael Nelles ◽  
...  

SPE Journal ◽  
2019 ◽  
Vol 24 (06) ◽  
pp. 2653-2670 ◽  
Author(s):  
Didier–Yu Ding

Summary Unconventional shale–gas and tight oil reservoirs are commonly naturally fractured, and developing these kinds of reservoirs requires stimulation by means of hydraulic fracturing to create conductive fluid–flow paths through open–fracture networks for practical exploitation. The presence of the multiscale–fracture network, including hydraulic fractures, stimulated and nonstimulated natural fractures, and microfractures, increases the complexity of the reservoir simulation. The matrix–block sizes are not uniform and can vary in a very wide range, from several tens of centimeters to meters. In such a reservoir, the matrix provides most of the pore volume for storage but makes only a small contribution to the global flow; the fracture supplies the flow, but with negligible contributions to reservoir porosity. The hydrocarbon is mainly produced from matrix/fracture interaction. So, it is essential to accurately model the matrix/fracture transfers with a reservoir simulator. For the fluid–flow simulation in shale–gas and tight oil reservoirs, dual–porosity models are widely used. In a commonly used dual–porosity–reservoir simulator, fractures are homogenized from a discrete–fracture network, and a shape factor based on the homogenized–matrix–block size is applied to model the matrix/fracture transfer. Even for the embedded discrete–fracture model (EDFM), the matrix/fracture interaction is also commonly modeled using the dual–porosity concept with a constant shape factor (or matrix/fracture transmissibility). However, in real cases, the discrete–fracture networks are very complex and nonuniformly distributed. It is difficult to determine an equivalent shape factor to compute matrix/fracture transfer in a multiple–block system. So, a dual–porosity approach might not be accurate for the simulation of shale-gas and tight oil reservoirs because of the presence of complex multiscale–fracture networks. In this paper, we study the multiple–interacting–continua (MINC) method for flow modeling in fractured reservoirs. MINC is commonly considered as an improvement of the dual–porosity model. However, a standard MINC approach, using transmissibilities derived from the MINC proximity function, cannot always correctly handle the matrix/fracture transfers when the matrix–block sizes are not uniformly distributed. To overcome this insufficiency, some new approaches for the MINC subdivision and the transmissibility computations are presented in this paper. Several examples are presented to show that using the new approaches significantly improves the dual–porosity model and the standard MINC method for nonuniform–block–size distributions.


1990 ◽  
Author(s):  
T.W. Doe ◽  
M. Uchida ◽  
J.S. Kindred ◽  
W.S. Dershowitz

Lithosphere ◽  
2021 ◽  
Vol 2021 (Special 4) ◽  
Author(s):  
Suran Wang ◽  
Yuhu Bai ◽  
Bingxiang Xu ◽  
Yanzun Li ◽  
Ling Chen ◽  
...  

Abstract Two-phase (gas+water) flow is quite common in tight sandstone gas reservoirs during flowback and early-time production periods. However, many analytical models are restricted to single-phase flow problems and three-dimensional fracture characteristics are seldom considered. Numerical simulations are good choices for this problem, but it is time consuming in gridding and simulating. This paper presents a comprehensive hybrid model to characterize two-phase flow behaviour and predict the production performance of a fractured tight gas well with a three-dimensional discrete fracture. In this approach, the hydraulic fracture is discretized into several panels and the transient flow equation is solved by the finite difference method numerically. A three-dimensional volumetric source function and superposition principle are deployed to capture the flow behaviour in the reservoir analytically. The transient responses are obtained by coupling the flow in the reservoir and three-dimensional discrete fracture dynamically. The accuracy and practicability of the proposed model are validated by the numerical simulation result. The results indicate that the proposed model is highly efficient and precise in simulating the gas/water two-phase flow and evaluating the early-time production performance of fractured tight sandstone gas wells considering a three-dimensional discrete fracture. The results also show that the gas production rate will be overestimated without considering the two-phase flow in the hydraulic fracture. In addition, the influences of fracture permeability, fracture half-length, and matrix permeability on production performance are significant. The gas production rate will be higher with larger fracture permeability at the early production period, but the production curves will merge after fracturing fluid flows back. A larger fracture half-length and matrix permeability can enhance the gas production rate.


2021 ◽  
Author(s):  
Xupeng He ◽  
Ryan Santoso ◽  
Marwa Alsinan ◽  
Hyung Kwak ◽  
Hussein Hoteit

Abstract Detailed geological description of fractured reservoirs is typically characterized by the discrete-fracture model (DFM), in which the rock matrix and fractures are explicitly represented in the form of unstructured grids. Its high computation cost makes it infeasible for field-scale applications. Traditional flow-based and static-based methods used to upscale detailed geological DFM to reservoir simulation model suffer from, to some extent, high computation cost and low accuracy, respectively. In this paper, we present a novel deep learning-based upscaling method as an alternative to traditional methods. This work aims to build an image-to-value model based on convolutional neural network to model the nonlinear mapping between the high-resolution image of detailed DFM as input and the upscaled reservoir simulation model as output. The reservoir simulation model (herein refers to the dual-porosity model) includes the predicted fracture-fracture transmissibility linking two adjacent grid blocks and fracture-matrix transmissibility within each coarse block. The proposed upscaling workflow comprises the train-validation samples generation, convolutional neural network training-validating process, and model evaluation. We apply a two-point flux approximation (TPFA) scheme based on embedded discrete-fracture model (EDFM) to generate the datasets. We perform trial-error analysis on the coupling training-validating process to update the ratio of train-validation samples, optimize the learning rate and the network architecture. This process is applied until the trained model obtains an accuracy above 90 % for both train-validation samples. We then demonstrate its performance with the two-phase reference solutions obtained from the fine model in terms of water saturation profile and oil recovery versus PVI. Results show that the DL-based approach provides a good match with the reference solutions for both water saturation distribution and oil recovery curve. This work manifests the value of the DL-based method for the upscaling of detailed DFM to the dual-porosity model and can be extended to construct generalized dual-porosity, dual-permeability models or include more complex physics, such as capillary and gravity effects.


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