Machine learning-assisted upscaling analysis of reservoir rock core properties based on micro-computed tomography imagery

Journal Article · Thu Sep 29 00:00:00 EDT 2022 · Journal of Petroleum Science and Engineering

DOI:https://doi.org/10.1016/j.petrol.2022.111087· OSTI ID:2422565

Yu, Xue ^[1]; Butler, Shane K. ^[1]; Kong, Lingyun ^[2]; Mibeck, Blaise A.F. ^[1]; Barajas-Olalde, Cesar ^[1]; Burton-Kelly, Matthew E. ^[1]; Azzolina, Nicholas A. ^[1]

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1. National Energy Technology Laboratory (NETL), Pittsburgh, PA, Morgantown, WV (United States); University of North Dakota, Grand Forks, ND (United States)
2. National Energy Technology Laboratory (NETL), Pittsburgh, PA, Morgantown, WV (United States); China University of Geosciences (Wuhan) (CUG), (China)

Optimum solutions for geologic modeling and reservoir simulation in industries such as oil and gas recovery and carbon capture and storage require accurate characterization of reservoir properties, which are often heterogeneous. In this study, high-quality micro-computed tomography (CT) images (1.475-μm/pixel resolution) of a sandstone core acquired from the Bell Creek oil field, USA, were used to provide nondestructive analysis of pore- and core-scale heterogeneity across measurement scales of 94–566 μm. In addition to characterizing the as-received sample, the core sample was flooded with brine to evaluate the capacity of the core sample to receive injected fluids. The micro-CT images were systematically segmented into pore spaces and grains via machine learning (ML) steps including image preprocessing, label creation using a traditional ML method based on limited manual image annotation, and finally U-Net segmentation. The segmented image stacks were reconstructed into digital cubes of various scales of voxel lengths. The 3D porosity values were calculated for all the digital cubes, and the fractal dimensions of the cubes were estimated using a box-counting method. The results showed that smaller cubes had greater heterogeneity and that the porosity values could be accurately estimated by fractal dimension and voxel lengths using ML models. For the core sample with brine flooding, the ratio of pores filled by brine to the total pore space was related to the porosity and could also be accurately estimated by porosity, fractal dimension, and voxel lengths using ML models. In conclusion, the results of this study demonstrate that the concept of fractal dimension can be a useful vector to perform upscaling analysis of sandstone rock heterogeneity from the pore to core scale and that fractal dimensions can be used to estimate porosity values and pore space-filling capacity across those scales.

• Research Organization: Leidos, Inc., Gaithersburg, MD (United States); National Energy Technology Laboratory (NETL), Pittsburgh, PA, Morgantown, WV (United States)
• Sponsoring Organization: USDOE Office of Fossil Energy and Carbon Management (FECM)
• Grant/Contract Number: 89243318CFE000003
• OSTI ID: 2422565
• Journal Information: Journal of Petroleum Science and Engineering, Journal Name: Journal of Petroleum Science and Engineering Journal Issue: C Vol. 219; ISSN 0920-4105
• Publisher: Elsevier
• Country of Publication: United States
• Language: English

Related Subjects

58 GEOSCIENCES
Energy & Fuels
Engineering
Fractal dimension
Heterogeneity
Storage capacity
U-net segmentation
Upscaling