Petrophysical and Fluid Flow Properties of a Tight Carbonate Source Rock Using Digital Rock Physics

Dernaika, Moustafa; Al-Jallad, Osama; Koronfol, Safouh; Suhrer, Michael; Teh, Woan Jing; Walls, Joel; Matar, Saad; Murthy, Natarajan; Zekraoui, Mohammed

doi:10.2118/172959-ms

Cited by 5 publications

(1 citation statement)

References 13 publications

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“…Interestingly, in Figure 2g, where the radiotracer spans the entire sample, significant regions of the core have no radiotracer present. These flow observations are aligned with observations in other heterogeneous limestone samples presented in the literature (Dernaika et al., 2015; Saenger et al., 2016).…”

Section: Resultssupporting

confidence: 91%

Predictive Soft Computing Methods for Building Digital Rock Models Verified by Positron Emission Tomography Experiments

Ebadi

Armstrong

Mostaghimi

et al. 2022

Water Resources Research

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The influence of core‐scale heterogeneity on continuum‐scale flow and laboratory measurements are not well understood. To address this issue, we propose a fully automated workflow based on soft computing to characterize the heterogeneous flow properties of cores for predictive continuum‐scale models. While the proposed AI‐based workflow inherently has no trained knowledge of rock petrophysical properties, our results demonstrate that image features and morphological properties provide sufficient measures for petrophysical classification. Micro X‐ray computed tomography (μxCT) image features were extracted from full core plug images by using a Convolutional Neural Network and Minkowski functional measurements. The features were then classified into specific classes using Principal Component Analysis followed by K‐means clustering. Next, the petrophysical properties of each class were evaluated using pore‐scale simulations to substantiate that unique classes were identified. The μxCT image was then up‐scaled to a continuum‐scale grid based on the defined classes. Last, simulation results were evaluated against real‐time flooding data monitored by Positron Emission Tomography. Both homogeneous sandstone and heterogeneous carbonate were tested. Simulation and experimental saturation profiles compared well, demonstrating that the workflow provided high‐fidelity characterization. Overall, we provided a novel workflow to build digital rock models in a fully automated way to better understand the impacts of heterogeneity on flow.

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Section: Resultssupporting

confidence: 91%

Predictive Soft Computing Methods for Building Digital Rock Models Verified by Positron Emission Tomography Experiments

Ebadi

Armstrong

Mostaghimi

et al. 2022

Water Resources Research

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Multi‐GPU solution to the lattice Boltzmann method: An application in multiscale digital rock simulation for shale formation

Chen

Yang

Amritkar

et al. 2018

Concurrency and Computation

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Characterization of rock properties is vital in producing oil and gas from shale reservoirs in an economically viable fashion. The nano-pore structure and ultralow permeability in shale reservoirs present challenges to the traditional experimental characterization methods. Digital rock physics for the estimation of rock properties, especially for shale reservoirs, has become a powerful tool that greatly complements to lab experiments by combining advance imaging techniques with numerical simulations. The lattice Boltzmann method (LBM) is a well-applied numerical method to simulate the fluid flow in pore structures at multiple length scales. Usually, the LBM simulation is resource intense because of its computation complexity and is facing great numerical challenges in extremely large-cale computation. In this paper, we propose a multi-GPU parallel implementation of 3D LBM on a hybrid high-performance computing cluster to perform large-scale simulations in reconstructed digital rocks. The program provides multiscale solution, pore scale and representative elementary volume (REV) scale based on the resolution of digital rock images. Optimization strategies are applied on partitioning simulation domain, improving data communication efficiency and maximizing CUDA occupancy. When running on a cluster of 32 GPUs, the proposed parallel implementation achieves a speedup of 1074x comparing to the in-house sequential program.

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Mineralogical and morphological characterization of the Cretaceous Galembo Member mudstones of the La Luna Formation in the Middle Magdalena Valley Basin (Colombia)

Mejia-Durán

Ríos‒Reyes

Pérez-Trejos

et al. 2020

Journal of Natural Gas Science and Engineering

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Petrophysical and Fluid Flow Properties of a Tight Carbonate Source Rock Using Digital Rock Physics

Cited by 5 publications

References 13 publications

Predictive Soft Computing Methods for Building Digital Rock Models Verified by Positron Emission Tomography Experiments

Predictive Soft Computing Methods for Building Digital Rock Models Verified by Positron Emission Tomography Experiments

Multi‐GPU solution to the lattice Boltzmann method: An application in multiscale digital rock simulation for shale formation

Mineralogical and morphological characterization of the Cretaceous Galembo Member mudstones of the La Luna Formation in the Middle Magdalena Valley Basin (Colombia)

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