Fluid flow in fractured reservoirs: Estimation of fracture intensity distribution, capillary diffusion coefficient and shape factor from saturation data

Abbasi, Mahdi; Sharifi, Mohammad; Kazemi, Alireza

doi:10.1016/j.jhydrol.2019.124461

Cited by 12 publications

(6 citation statements)

References 33 publications

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“…The transport of fluids within the porous medium relies on the available pore space. However, flows through fractures or fractured media also receive great research attention since any fluid found in the subsurface could migrate through the fractures instead of travelling through the pore space [4,35,60,[70][71][72]. As a result, the groundwater characteristics vary based on induced fractures in the porous medium [73,74].…”

Section: Applicabilty Of the Findings In The Field Of Hydrologymentioning

confidence: 99%

Experimental Investigation of the Effects of Porosity, Hydraulic Conductivity, Strength, and Flow Rate on Fluid Flow in Weakly Cemented Bio-Treated Sands

Konstantinou

Biscontin

2022

Hydrology

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Fluid injection in a porous medium is the underlying mechanism for many applications in the fields of groundwater hydraulics, hydrology and hydrogeology, and geo-environmental engineering and in the oil and gas industry. Fluid flow experiments in porous media with a viscous fluid at varying injection rates were conducted in a modified Hele-Shaw setup. The granular media were three-dimensional bio-cemented sands of various grain sizes across various cementation levels, generating a matrix of various hydraulic conductivities, porosities, and strengths. The fluid injection experiments showed that a cavity-like fracture developed, which transitioned to crack-like fractures at higher cementation levels (hence, higher strength). As the flow rate increased, less infiltration was evident and higher breakdown pressure was observed, with propagation pressure reducing to zero. It was harder to induce an opening in cemented specimens with higher hydraulic conductivity and a larger pore network despite their lower strength due to excessive infiltration dominance, which inhibited the build-up of pressure required to generate a fracture. The results of this study suggest that, when designing fluid injection programs, the combined effects of hydraulic conductivity and strength need to be carefully considered.

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Section: Applicabilty Of the Findings In The Field Of Hydrologymentioning

confidence: 99%

Experimental Investigation of the Effects of Porosity, Hydraulic Conductivity, Strength, and Flow Rate on Fluid Flow in Weakly Cemented Bio-Treated Sands

Konstantinou

Biscontin

2022

Hydrology

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“…In addition, scholars have proposed many improved edge detection algorithms for the PDE model based on the framework of the PM model and ROF model in recent years. The literature [10] was the first to propose a fuzzy algorithm for image enhancement, which is the first algorithm to apply fuzzy sets to the field of image enhancement, and many researchers have joined the study because of the novelty of the algorithm. The literature [11] proposed an edge enhancement algorithm with the help of fuzzy theory to obtain the image feature values, and the method is good for enhancing the image detail information.…”

Section: Related Studiesmentioning

confidence: 99%

Fractional-Order Adaptive $P$ -Laplace Equation-Based Art Image Edge Detection

Chen

2021

Advances in Mathematical Physics

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In recent years, with the rapid development of image processing research, the study of nonstandard images has gradually become a research hotspot, for example, fabric images, remote sensing images, and gear images. Some of the remote sensing images have a complex background and low illumination compared with standard images and are easy to be mixed with noise during acquisition; some of the fabric images have rich texture information, which adds difficulty to the related processing, and are also easy to be mixed with noise during acquisition. In this paper, we propose a fractional-order adaptive P -Laplace equation image edge detection algorithm for the problem of image edge detection in which the edge and texture information of the image is lost. The algorithm can apply for the order adaptively to filter the noise according to the noise distribution of the image, and the adaptive diffusion factor is determined by both the fractional-order curvature and fractional-order gradient of the iso-illumination line and combined with the iterative approach to realize the fine-tuning of the noisy image. The experimental results demonstrate that the algorithm can remove the noise while preserving the texture and details of the image. A fractional-order partial differential equation image edge detection model with a fractional-order fidelity term is proposed for Gaussian noise. The model incorporates a fractional-order fidelity term because this fidelity term smoothes out the rougher parts of the image while preserving the texture in the original image in greater detail and eliminating the step effect produced by other models such as the Perona-Malik (PM) and Rudin-Osher-Fatemi (ROF) models. By comparing with other algorithms, the image edge detection effect is measured with the help of evaluation metrics such as peak signal-to-noise ratio and structural similarity, and the optimal value is selected iteratively so that the image with the best edge detection result is retained. A convolutional mask image edge detection model based on adaptive fractional-order calculus is proposed for the scattered noise in medical images. The adaption is mainly reflected in the model algorithm by constructing an exponential parameter relation that is closely related to the image, which can dynamically adjust the parameter values, thus making the model algorithm more practical. The model achieves the scattering noise removal in four steps.

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“…Fluid flow in fractured reservoirs is significantly influenced by the distribution of fractures. Hence, it is necessary to accurately estimate the fracture distribution for the simulation and management of fractured reservoirs [1]. Inverse modeling, also termed history matching in petroleum engineering, is an effective method for predicting the distribution of fractures.…”

Section: Introductionmentioning

confidence: 99%

Deep Graph Learning-Based Surrogate Model for Inverse Modeling of Fractured Reservoirs

Ma,

Zhao,

Zhou

et al. 2024

Mathematics

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Inverse modeling can estimate uncertain parameters in subsurface reservoirs and provide reliable numerical models for reservoir development and management. The traditional simulation-based inversion method usually requires numerous numerical simulations, which is time-consuming. Recently, deep learning-based surrogate models have been widely studied as an alternative to numerical simulation, which can significantly improve the solving efficiency of inversion. However, for reservoirs with complex fracture distribution, constructing the surrogate model of numerical simulation presents a significant challenge. In this work, we present a deep graph learning-based surrogate model for inverse modeling of fractured reservoirs. Specifically, the proposed surrogate model integrates the graph attention mechanisms to extract features of fracture network in reservoirs. The graph learning can retain the discrete characteristics and structural information of the fracture network. The extracted features are subsequently integrated with a multi-layer recurrent neural network model to predict the production dynamics of wells. A surrogate-based inverse modeling workflow is then developed by combining the surrogate model with the differential evolutionary algorithm. Numerical studies performed on a synthetic naturally fractured reservoir model with multi-scale fractures illustrate the performance of the proposed methods. The results demonstrate that the proposed surrogate model exhibits promising generalization performance of production prediction. Compared with tens of thousands of numerical simulations required by the simulation-based inverse modeling method, the proposed surrogate-based method only requires 1000 to 1500 numerical simulations, and the solution efficiency can be improved by ten times.

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Fluid flow in fractured reservoirs: Estimation of fracture intensity distribution, capillary diffusion coefficient and shape factor from saturation data

Cited by 12 publications

References 33 publications

Experimental Investigation of the Effects of Porosity, Hydraulic Conductivity, Strength, and Flow Rate on Fluid Flow in Weakly Cemented Bio-Treated Sands

Experimental Investigation of the Effects of Porosity, Hydraulic Conductivity, Strength, and Flow Rate on Fluid Flow in Weakly Cemented Bio-Treated Sands

Fractional-Order Adaptive $P$ -Laplace Equation-Based Art Image Edge Detection

Deep Graph Learning-Based Surrogate Model for Inverse Modeling of Fractured Reservoirs

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Fluid flow in fractured reservoirs: Estimation of fracture intensity distribution, capillary diffusion coefficient and shape factor from saturation data

Cited by 12 publications

References 33 publications

Experimental Investigation of the Effects of Porosity, Hydraulic Conductivity, Strength, and Flow Rate on Fluid Flow in Weakly Cemented Bio-Treated Sands

Experimental Investigation of the Effects of Porosity, Hydraulic Conductivity, Strength, and Flow Rate on Fluid Flow in Weakly Cemented Bio-Treated Sands

Fractional-Order Adaptive P -Laplace Equation-Based Art Image Edge Detection

Deep Graph Learning-Based Surrogate Model for Inverse Modeling of Fractured Reservoirs

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Product

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Fractional-Order Adaptive $P$ -Laplace Equation-Based Art Image Edge Detection