Numerical Simulation of Low Permeability Unconventional Gas Reservoirs

Ding, Didier Yu; Wu, Yu‐Shu; Farah, N.; Wang, C.; Bourbiaux, B.

doi:10.2118/167711-ms

Cited by 42 publications

(15 citation statements)

References 32 publications

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“…The technological advances in the unconventional naturally fractured reservoir lead to the multistage hydraulic fractured horizontal production wellbore with cased‐hole completion to more efficiently extract gas, oil, and geothermal energy from the reservoir . The main focus of this example is to illustrate the flow pattern around a multistage fractured horizontal wellbore with cased‐hole completion.…”

Section: Numerical Experimentsmentioning

confidence: 99%

Coupled and decoupled stabilized mixed finite element methods for nonstationary dual‐porosity‐Stokes fluid flow model

Mahbub

Nasu

et al. 2019

Numerical Meth Engineering

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In this paper, we propose and analyze two stabilized mixed finite element methods for the dual-porosity-Stokes model, which couples the free flow region and microfracture-matrix system through four interface conditions on an interface. The first stabilized mixed finite element method is a coupled method in the traditional format. Based on the idea of partitioned time stepping, the four interface conditions, and the mass exchange terms in the dual-porosity model, the second stabilized mixed finite element method is decoupled in two levels and allows a noniterative splitting of the coupled problem into three subproblems. Due to their superior conservation properties and convenience of the computation of flux, mixed finite element methods have been widely developed for different types of subsurface flow problems in porous media. For the mixed finite element methods developed in this article, no Lagrange multiplier is used, but an interface stabilization term with a penalty parameter is added in the temporal discretization. This stabilization term ensures the numerical stability of both the coupled and decoupled schemes. The stability and the convergence analysis are carried out for both the coupled and decoupled schemes. Three numerical experiments are provided to demonstrate the accuracy, efficiency, and applicability of the proposed methods. KEYWORDS decoupled numerical methods, dual-porosity-Stokes model, horizontal wellbore, mixed finite elements, stabilization Int J Numer Methods Eng. 2019;120:803-833. wileyonlinelibrary.com/journal/nme 804 AL MAHBUB ET AL.model, It is not surprising that a great deal of effort has been devoted to develop appropriate numerical methods to solve the (Navier-)Stokes-Darcy fluid flow system, including coupled finite element methods, 17-21 domain decomposition methods, 22-31 Lagrange multiplier methods, 3,32,33 mortar finite element methods, 34,35 least-square methods, 36-38 partitioned time-stepping methods, 39,40 two-grid and multigrid methods, 41-44 discontinuous Galerkin finite element methods, 45-50 boundary integral methods, 51,52 and many others. [53][54][55][56][57][58] Although the traditional Stokes-Darcy model has been well studied, it has limitation to describe the heterogeneity of the porous medium which contains multiple porosities. It is worth to notice that the realistic naturally fractured reservoir consists of two coexisting and interacting medium, namely, tight matrix and microfractures. Furthermore, it is more important to characterize the intrinsic properties and accurately modeled the flow interactions between each medium. 59,60 The first multiporosity model was proposed by Barenblatt et al 61 for the naturally fractured reservoir in 1960 where the microfracture and matrix systems are formulated by individual but overlapping continua. Warren and Root 62 developed a homogeneous orthotropic dual-porosity model in 1963 based on the model proposed by Barenblatt. There are many applications for the dual-porosity model such as the geothermal system, hydrogeology, pe...

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Section: Numerical Experimentsmentioning

confidence: 99%

Coupled and decoupled stabilized mixed finite element methods for nonstationary dual‐porosity‐Stokes fluid flow model

Mahbub

Nasu

et al. 2019

Numerical Meth Engineering

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“…An explicit discretized model with a single-porosity approach can accurately model regular fracture networks because fractures are identified by their spatial distributions (a detailed description of the governing equations is presented in Appendix A). As multi-scale fractures become abundant in unconventional reservoirs post stimulation, single porosity models that use an LGR (Local Grid Refinement) technique (see Cheng, 2012;Cipolla et al, 2009;Ding et al, 2014) become CPU-time consuming due to the large number of grid cells used.…”

Section: Introductionmentioning

confidence: 99%

The MINC proximity function for fractured reservoirs flow modeling with non-uniform block distribution

Farah

Ghadboun

2021

Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles

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Reservoir simulation is a powerful technique to predict the amount of produced hydrocarbon. After a solid representation of the natural fracture geometry, an accurate simulation model and a physical reservoir model that account for different flow regimes should be developed. Many models based on dual-continuum approaches presented in the literature rely on the Pseudo-Steady-State (PSS) assumption to model the inter-porosity flow. Due to the low permeability in such reservoirs, the transient period could reach several years. Thus, the PSS assumption becomes unjustified. The numerical solution adopted by the Multiple INteracting Continua (MINC) method was able to simulate the transient effects previously overlooked by dual-continuum approaches. However, its accuracy drops with increasing fracture network complexity. A special treatment of the MINC method, i.e., the MINC Proximity Function (MINC–PF) was introduced to address the latter problem. And yet, the MINC–PF suffers a limitation that arises from the existence of several grid-blocks within a studied cell. In this work, this limitation is discussed and two possible solutions (transmissibility recalculation/adjusting the Proximity Function by accounting for nearby fractures) are put forward. Both proposed methods have demonstrated their applicability and effectiveness once compared to a reference solution.

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“…For example, in developing tight gas formations, hydraulic fracturing is used to produce fractures in rock formations which stimulate the fl ow of natural gas. Reservoir modelling in such systems is an extremely complicated task, given the need to simulate fl uid fl ow in a network of induced natural fractures coupled to geo-mechanical effects and other processes such as water blocking, non-Darcy fl ow in nano-scale pores, and adsorption/desorption (Cipolla et al, 2010 andDing et al, 2014). Tight gas refers to natural gas trapped in a reservoir with a matrix permeability lower than 0.1×10 -3 μm 2 , which usually has no natural deliverability or lower natural deliverability than the industrial standard, so stimulation or special treatment wells must be used to obtain commercial gas fl ow.…”

Section: Introductionmentioning

confidence: 99%

Application of machine learning models in predicting initial gas production rate from tight gas reservoirs

Amaechi

Ikpeka

et al. 2019

MGPB

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Driven by advancements in technology, tight-gas fi eld development has become a signifi cant source of hydrocarbon to the energy industry. The amount of data generated in the process is immense as most platforms are now being digitized. Machine learning tools can be used to analyse this data in order to build patterns between several dependent and independent variables. Forecasting initial gas production rates has important implications in the planning production/processing facilities for new wells, aff ects investment decisions and is an important component of reporting to regulatory agencies. This study is based on the analysis of reservoir rock/fl uid properties and selected well parameters to build decision-based models that can predict initial gas production rates for tight gas formations. In this study, two machine learning predictive models; Artifi cial Neural Network (ANN) and Generalized Linear Model (GLM), were used to determine the expected recovery rate of planned new wells. Production data was retrieved from 224 wells and used in developing the model. The results obtained from these models were then compared to the actual recorded initial gas production rate from the wells. Results from the analysis carried out revealed a Mean Square Error (MSE) of 1.57 on a GLM model whereas the ANN model gave an MSE of 1.24. Key Performance Index for the ANN model revealed that reservoir thickness had the highest (36.5%) contribution to the initial gas production rate followed by the fl owback rate (29%). The reservoir/fl uid properties contribution to the initial gas production rate was 53% while the hydraulic fracture parameters contribution to the initial gas production rate was 47%.

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Numerical Simulation of Low Permeability Unconventional Gas Reservoirs

Cited by 42 publications

References 32 publications

Coupled and decoupled stabilized mixed finite element methods for nonstationary dual‐porosity‐Stokes fluid flow model

Coupled and decoupled stabilized mixed finite element methods for nonstationary dual‐porosity‐Stokes fluid flow model

The MINC proximity function for fractured reservoirs flow modeling with non-uniform block distribution

Application of machine learning models in predicting initial gas production rate from tight gas reservoirs

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