Numerical simulation of two-phase flow in fractured porous media using streamline simulation and IMPES methods and comparing results with a commercial software

Ahmadpour, Mahmoud; Siavashi, Majid; Doranehgard, Mohammad Hossein

doi:10.1007/s11771-016-3324-5

Cited by 34 publications

(8 citation statements)

References 14 publications

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“…At the same time, in these models, we will simulate reservoirs of different sizes by changing the length of the grid, and simulate different water injection conditions by changing the water injection volume of the injection well. Based on the oil-water two-phase percolation model, through the IMPES method [34], [35], this simulation obtained a total of 5,000 sample models and their corresponding dynamic production information.…”

Section: Procedures a Dataset Collectionmentioning

confidence: 99%

The Connectivity Evaluation Among Wells in Reservoir Utilizing Machine Learning Methods

Wang

Wei

et al. 2020

IEEE Access

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Machine learning is becoming prevalent increasingly for reservoir characteristics analysis in the petroleum industry. This investigation proposes an alternative way for evaluating interwell connectivity in oil fields utilizing machine learning. In this study, three-dimensional convolutional neural network (CNN) was utilized to establish a deep learning model, which can invert interwell connectivity combining with dynamic production data. Different from traditional methods that try to construct mathematical formulas to calculate the connectivity among wells basing on physical laws, deep learning model can capture autonomously the changing characteristics of dynamic production data by training continuously and provide a potential to characterize the interwell connectivity accurately without physical model. At the same time, the back propagation (BP) neural network has also been built to analyze the prediction performance, which are compared with CNN. The results demonstrate that CNN has better performance in predicting the connectivity with the overall AARD below 15.35%. Moreover, the connectivity predicted by CNN is closest to the real connectivity factor compared with some traditional methods. The evaluation method on interwell connectivity proposed by this paper provides effective guidance for the secondary development of both conventional and unconventional reservoirs. INDEX TERMS BP neural network, convolutional neural network, dynamic production data, interwell connectivity, machine learning.

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Section: Procedures a Dataset Collectionmentioning

confidence: 99%

The Connectivity Evaluation Among Wells in Reservoir Utilizing Machine Learning Methods

Wang

Wei

et al. 2020

IEEE Access

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“…The results obtained show that presence of a small fraction of oil-wet rock grains drastically affects oil recovery by capillary imbibition Understanding rock wettability, among other petrophysical properties, is very important in the prediction of fluid transport within the rock matrix in fractured reservoirs (Arns et al, 2004;Blunt et al, 2013;Liu et al, 2017). Pore network structures of fractured reservoir matrix rocks, idealized from 3D X-ray tomography images of core plugs, are useful in efficient calculation of storage capacity and multiphase fluid transport in the formation (Jiang et al 2007;Ryazanov et al, 2009;Bauer et al, 2012;Al-Dhalhi et al, 2013;Ahmadpour et al, 2016;Liu et al, 2017;Ding et al, 2017). The extension of pore-network models to include explicit fractures (Jiang et al, 2012;Ding et al, 2017) provides an important approach in understanding the rock matrix-to-fractures fluid transfer process and in the modeling of the effects of wettability on waterflood oil recovery.…”

Section: Fractured Reservoirsmentioning

confidence: 99%

Review of studies on pore-network modeling of wettability effects on waterflood oil recovery

Wopara¹,

Iyuke²

2018

J. Petroleum Gas Eng.

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Waterflooding has become by far the most widely applied method for enhanced oil recovery, accounting for more than half of oil production worldwide. Changes in the wettability of a porous medium and the chemical composition of the wetting fluid result to changes in oil recovery. In recent time, pore-network modeling has been used increasingly to study the effect of wettability on waterflood oil recovery and multiphase flow properties in different types of petroleum reservoirs. Starting from single pore models of fluid arrangements, computations of relative permeability, interfacial area, mass dissolution rate and many other physical properties have been made. Using realistic representations of the pore spaces of various rock systems, it is now possible to predict many petro-physical properties that govern fluid flow and transport in petroleum reservoir rock systems. Such properties include porosity, permeability, waterflood relative permeability, capillary pressure, phase saturations, and fluid transfer coefficients. This review looks briefly into some of the successes made so far on pore network modeling, with an emphasis on models of wettability trends and oil recovery by waterflooding.

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“…Therefore, water flow process in fractured rock masses should be studied firstly and foremost. Till now, five models have been found in literatures to simulate the water flow in fractured rock masses, that is, continuous model [3][4][5], dual-continuum model [6][7][8], equivalent continuum model [9][10][11], discrete fracture network (DFN) [12][13][14], and hybrid model [15][16][17]. In continuous model, rock masses are treated as continuum porous media with porosity and permeability and continuum-mechanics formulations can be used to describe flow and transport in media.…”

Section: Literature Review About Nuclide Transport Simulationmentioning

confidence: 99%

Implementation of a Time-Domain Random-Walk Method into a Discrete Element Method to Simulate Nuclide Transport in Fractured Rock Masses

Liu

Chan

et al. 2017

Geofluids

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It is essential to study nuclide transport with underground water in fractured rock masses in order to evaluate potential radionuclide leakage in nuclear waste disposal. A time-domain random-walk (TDRW) method was firstly implemented into a discrete element method (DEM), that is, UDEC, in this paper to address the pressing challenges of modelling the nuclide transport in fractured rock masses such as massive fractures and coupled hydromechanical effect. The implementation was then validated against analytical solutions for nuclide transport in a single fracture and a simple fracture network. After that, the proposed implementation was applied to model the nuclide transport in a complex fracture network investigated in the DECOVALEX 2011 project to analyze the effect of matrix diffusion and stress on the nuclide transport in the fractured rock masses. It was concluded that the implementation of the TDRW method into UDEC provided a valuable tool to study the nuclide transport in the fractured rock masses. Moreover, it was found that the total travel time of the nuclide particles in the fractured rock masses with the matrix diffusion and external stress modelled was much longer than that without the matrix diffusion and external stress modelled.

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Numerical simulation of two-phase flow in fractured porous media using streamline simulation and IMPES methods and comparing results with a commercial software

Cited by 34 publications

References 14 publications

The Connectivity Evaluation Among Wells in Reservoir Utilizing Machine Learning Methods

The Connectivity Evaluation Among Wells in Reservoir Utilizing Machine Learning Methods

Review of studies on pore-network modeling of wettability effects on waterflood oil recovery

Implementation of a Time-Domain Random-Walk Method into a Discrete Element Method to Simulate Nuclide Transport in Fractured Rock Masses

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