Critical Resolution and Sample Size of Digital Rock Analysis for Unconventional Reservoirs

Liu, Tong; Xu, Jin; Wang, Moran

doi:10.3390/en11071798

Cited by 26 publications

(18 citation statements)

References 46 publications

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“…Thus, it is of utmost importance to reduce scan time to capture the dynamics. Second, with respect to digital rock physics, the accuracy of the estimated rock properties strongly depends on the image quality [ 28 , 29 , 30 ]. Third, high quality images are a prerequisite for resolution enhancement techniques [ 31 , 32 , 33 ].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Driven Noise Reduction for Reduced Flux Computed Tomography

Alsamadony

Yildirim

Glatz

et al. 2021

Sensors

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Deep neural networks have received considerable attention in clinical imaging, particularly with respect to the reduction of radiation risk. Lowering the radiation dose by reducing the photon flux inevitably results in the degradation of the scanned image quality. Thus, researchers have sought to exploit deep convolutional neural networks (DCNNs) to map low-quality, low-dose images to higher-dose, higher-quality images, thereby minimizing the associated radiation hazard. Conversely, computed tomography (CT) measurements of geomaterials are not limited by the radiation dose. In contrast to the human body, however, geomaterials may be comprised of high-density constituents causing increased attenuation of the X-rays. Consequently, higher-dose images are required to obtain an acceptable scan quality. The problem of prolonged acquisition times is particularly severe for micro-CT based scanning technologies. Depending on the sample size and exposure time settings, a single scan may require several hours to complete. This is of particular concern if phenomena with an exponential temperature dependency are to be elucidated. A process may happen too fast to be adequately captured by CT scanning. To address the aforementioned issues, we apply DCNNs to improve the quality of rock CT images and reduce exposure times by more than 60%, simultaneously. We highlight current results based on micro-CT derived datasets and apply transfer learning to improve DCNN results without increasing training time. The approach is applicable to any computed tomography technology. Furthermore, we contrast the performance of the DCNN trained by minimizing different loss functions such as mean squared error and structural similarity index.

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Section: Introductionmentioning

confidence: 99%

Deep Learning Driven Noise Reduction for Reduced Flux Computed Tomography

Alsamadony

Yildirim

Glatz

et al. 2021

Sensors

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“…First, microstructure characterization of cores from the concerned reservoir: Numerous and sufficient cores, low‐permeable or extra‐low‐permeable sandstones from Changqing oilfield in China, were scanned by SEM, micro‐CT, nano‐CT, or FIB‐SEM. Please note that the resolution of scanning should be higher than the critical resolution and the critical sample size need to satisfy the representative element volume (REV) condition rigidly during scanning …”

Section: Experimental Methodsmentioning

confidence: 99%

“…Please note that the resolution of scanning should be higher than the critical resolution and the critical sample size need to satisfy the representative element volume (REV) condition rigidly during scanning. 39 Second, statistical information abstraction: After the pore structures reconstructed from the scanning data, by the image analysis software Fiji 40 in our work, the statistical information of reservoir geometries was abstracted, including pore distribution, connectivity, porosity, and so on. Based on our previous experience on multiphase displacement in rocks, [41][42][43] we believe that the pore size plays a key role on the oil development in tight reservoirs with micro/nano pores.…”

Section: Reservoir-on-a-chip Designmentioning

confidence: 99%

Enhanced oil recovery mechanism and recovery performance of micro‐gel particle suspensions by microfluidic experiments

Lei

Liu

Xie

et al. 2019

Energy Science & Engineering

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Micro‐gel particle suspensions (MGPS) have been proposed for enhanced oil recovery (EOR) in reservoirs with harsh conditions in recent years, yet the mechanisms are still not clear because of the complex property of MGPS and the complex geometry of rocks. In this paper, the micro‐gel particle‐based flooding has been studied by our microfluidic experiments on both bi‐permeability micromodels and reservoir‐on‐a‐chip. A method for reservoir‐on‐a‐chip design has been proposed based on QSGS (quartet structure generation set) to ensure that the flow geometry on chip owns the most important statistical features of real rock microstructures. In the micromodel experiments with heterogeneous microstructures, even if the MGPS has the same macroscopic rheology as the hydrolyzed polyacrylamides (HPAM) solution for flooding, MGPS may lead to significant fluctuations of pressure field caused by the nonuniform concentration distribution of particles. In the reservoir‐on‐a‐chip experiments, clustered oil trapped in the swept pores can be recovered by MGPS because of pressure fluctuation, which hardly happens in the HPAM flooding. Compared with the water flooding, the HPAM solution flooding leads to approximately 17% incremental oil recovery, while the MGPS results in approximately 49.8% incremental oil recovery in the laboratory.

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“…Studies have been done on the effects of sample size or scanning resolution, however most of them considered only one factor or both but separately [32][33][34][35][36][37][38]. In our very recent work, the accuracy of DRA, which depends on the cut-off resolution (COR), sample size and the image processing method, has been quantified for the first time for Newtonian fluid flow in high-and low-permeability rocks [39]. To our best knowledge, the upscaling of DRA with REV and resolution analysis for non-Newtonian fluid flow in tight rocks has never been reported.…”

Section: Introductionmentioning

confidence: 99%

Does Rheology of Bingham Fluid Influence Upscaling of Flow through Tight Porous Media?

2021

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Non-Newtonian fluids may cause nonlinear seepage even for a single-phase flow. Through digital rock technologies, the upscaling of this non-Darcy flow can be studied; however, the requirements for scanning resolution and sample size need to be clarified very carefully. This work focuses on Bingham fluid flow in tight porous media by a pore-scale simulation on CT-scanned microstructures of tight sandstones. A bi-viscous model is used to depict the Bingham fluid. The results show that when the Bingham fluid flows through a rock sample, the flowrate increases at a parabolic rate when the pressure gradient is small and then increases linearly with the pressure gradient. As a result, an effective permeability and a start-up pressure gradient can be used to characterize this flow behavior. By conducting flow simulations at varying sample sizes, we obtain the representative element volume (REV) for effective permeability and start-up pressure gradient. It is found that the REV size for the effective permeability is almost the same as that for the absolute permeability of Newtonian fluid. The interesting result is that the REV size for the start-up pressure gradient is much smaller than that for the effective permeability. The results imply that the sample size, which is large enough to reach the REV size for Newtonian fluids, can be used to investigate the Bingham fluids flow through porous media as well.

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Critical Resolution and Sample Size of Digital Rock Analysis for Unconventional Reservoirs

Cited by 26 publications

References 46 publications

Deep Learning Driven Noise Reduction for Reduced Flux Computed Tomography

Deep Learning Driven Noise Reduction for Reduced Flux Computed Tomography

Enhanced oil recovery mechanism and recovery performance of micro‐gel particle suspensions by microfluidic experiments

Does Rheology of Bingham Fluid Influence Upscaling of Flow through Tight Porous Media?

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