Integrated Multiphysics Workflow for Automatic Rock Classifi
Conventional well-log-based rock classification often overlooks rock-fabric features (spatial distribution of solid and fluid-rock components), which makes it not comparable against geologic facies, especially in formations with complex rock fabric. This challenge is usually addressed by the identification of geological facies from the core description and their integration with measured petrophysical properties. However, manual identification of geological facies using core data is a tedious and time-consuming process. In this paper, we propose an automatic workflow for joint interpretation of conventional well logs, computed tomography (CT) scan/core images, and routine core analysis (RCA) data for simultaneously optimizing rock classification and formation evaluation. First, we perform conventional well-log interpretation to obtain petrophysical properties of the evaluated depth intervals. Subsequently, we automatically extract rock-fabric-related features derived from core photos and core CT scan images. Then, we use a clustering algorithm to obtain rock classes from the extracted rock-fabric features. We optimize the number of rock classes by iteratively increasing the number of rock classes from an initially assumed number until a permeability-based cost function converges below a predefined threshold. The proposed workflow will provide (i) quantitative wellbore/core image-based rock-fabric- related features, (ii) automatic integration of rock-fabric-related features with conventional well logs and RCA data, and (iii) automatic and simultaneous assessment of rock classes and petrophysical properties, honoring rock fabric. Additionally, the outcomes of the proposed workflow can help to expedite the process of geological facies classification by providing an overview of different lithologies and an overall stacking pattern.
We successfully applied the proposed workflow to a sedimentary sequence with vertically variable rock fabric and lithology. Dual-energy-acquired core CT scan images were available along with core photos, RCA data, and conventional well logs. Image-based integrated rock classes were in agreement with the lithologies encountered in the evaluated depth interval. Class-by-class permeability models improved permeability estimates by 89% (decrease in mean relative error) in comparison to formation-by-formation permeability estimates. Furthermore, the detected rock classes were consistently propagated to another well where core and CT scan images were not employed for rock classification. The detected rock classes were in agreement with lithofacies obtained from the core description. Permeability estimates were also in good agreement with available RCA data.
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Andres Gonzalez, Lawrence Kanyan, Zoya Heidari, and Olivier Lopez
The University of Texas at Austin; Equinor