Machine learning of dual porosity model closures from discrete fracture simulations

Andrianov, Nikolai; Nick, Hamidreza M.

doi:10.1016/j.advwatres.2020.103810

Cited by 14 publications

(4 citation statements)

References 44 publications

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“…One method of incorporating this information is to use flow simulation on small-scale or idealised DFNs to derive an empirical relationship between these various geometric parameters and the fracture permeability tensor (e.g. Andrianov 2021, Andrianov & Nick 2021.…”

Section: Discussionmentioning

confidence: 99%

DFN Generator v2.0: A new tool to model the growth of large-scale natural fracture networks using fundamental geomechanics

Welch

Lüthje

Oldfield

2022

Preprint

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Abstract. In this paper we present a new code to build geologically realistic models of natural fracture networks in geological formations, by simulating the processes of fracture nucleation, growth and interaction, based on geomechanical principles and the geological history of the formation. This code implements the fracture modelling algorithm described in Welch et al. (2020), developed to generate more accurate, better constrained models of large fracture networks than current stochastic techniques. It can efficiently build either implicit fracture models, explicit DFNs, or both, across large (km-scale) geological structures such as folds, major faults or salt diapirs. It will thus have applications in engineering and fluid flow modelling, including CO2 sequestration and geothermal energy, as well as in understanding the controls on the evolution of fracture networks. The code is written in C Sharp and is provided with two interfaces: a standalone interface with text file input and output, that can be compiled in standard C Sharp and can run simple models, and a plug-in interface for the Petrel geomodelling package from Schlumberger, that can run more complex models of real geological structures. The standalone version has been used to run extensive sensitivity analyses, which studied the influence of various mechanical and physical parameters (e.g. layer thickness, applied strain, Young’s Modulus, etc.) on the fracture evolution and geometry, by varying the parameters individually in simple models. The Petrel plug-in has been used to evaluate the code applicability by running simulations of actual fractured layers in outcrops and in the subsurface, and comparing the results with observed fracture patterns.

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Section: Discussionmentioning

confidence: 99%

DFN Generator v2.0: A new tool to model the growth of large-scale natural fracture networks using fundamental geomechanics

Welch

Lüthje

Oldfield

2022

Preprint

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“…Furthermore, there are various investigations performed in the literature based on a bridge between the dual-porosity approach and other techniques to enhance the accuracy of the dual-porosity results. [54][55][56][57][58][59][60][61][62][63] However, in most of the aforementioned studies, the applied shape factor is assumed to be constant in both unsteady and steady-state situations and only depends on the geometry of blocks. Accordingly, these assumptions lead to significant errors in the simulations, especially in the transient stages.…”

Section: Introductionmentioning

confidence: 99%

“…A summary of the dimensionless shape factors obtained from the previous studies is presented in Table 1. Furthermore, there are various investigations performed in the literature based on a bridge between the dual‐porosity approach and other techniques to enhance the accuracy of the dual‐porosity results 54–63 . However, in most of the aforementioned studies, the applied shape factor is assumed to be constant in both unsteady and steady‐state situations and only depends on the geometry of blocks.…”

Section: Introductionmentioning

confidence: 99%

A computational dual‐porosity approach for the coupled hydro‐mechanical analysis of fractured porous media

Khoei,

Taghvaei

2024

Num Anal Meth Geomechanics

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Dual‐porosity simulation is one of the most used and efficient approaches in modeling fractured porous media. The performance of this approach is highly dependent on the accuracy of the definition of matrix‐fracture transfer shape factor. In this paper, a two‐step computational algorithm is developed to enhance the accuracy of the dual‐porosity method in modeling large‐scale deformable porous media, particularly in the transient stage when the conventional dual‐porosity models are associated with some errors. Moreover, in the proposed finite element method (FEM)‐based algorithm, the interaction between the fluid and solid phases is taken into account using a modified expression for the conventional shape factor. To this end, an extended relationship is derived to calculate the hydro‐mechanical shape factor for deformable saturated porous media. An appropriate unit cell along with the consistent boundary conditions is introduced, and the corresponding hydro‐mechanical shape factor is numerically evaluated for the unit cell at each time step. Finally, the calculated time‐varying shape factors are employed in a dual‐porosity framework to simulate various case studies and investigate the influence of different parameters, including the boundary conditions, the fracture to matrix permeability ratio, and the fracture compressibility coefficient on the hydraulic and hydro‐mechanical time‐varying shape factors. Numerical simulations are carried out by employing the proposed dual‐porosity algorithm through two general problems and the results are compared with the fine‐grid direct numerical simulation. The results are promising and show that the present algorithm effectively and efficiently increases the precision of the dual‐porosity method in modeling large‐scale deformable problems.

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“…The study conducted the missing feature and proposed the proper characteristics in combination with in-situ fluid saturations. Andrianov and Nick (2000) [35] employed ML-based analytical method along with the discrete fracture simulations to generate a dual porosity model. Accordingly, a pixelated representation technique was employed to characterize the fracture geometry.…”

mentioning

confidence: 99%

Comparison of machine learning techniques for predicting porosity of chalk

Nourani

Alali

Samadianfard

et al. 2022

Journal of Petroleum Science and Engineering

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Machine learning of dual porosity model closures from discrete fracture simulations

Cited by 14 publications

References 44 publications

DFN Generator v2.0: A new tool to model the growth of large-scale natural fracture networks using fundamental geomechanics

DFN Generator v2.0: A new tool to model the growth of large-scale natural fracture networks using fundamental geomechanics

A computational dual‐porosity approach for the coupled hydro‐mechanical analysis of fractured porous media

Comparison of machine learning techniques for predicting porosity of chalk

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