A NEW OPTIMIZATION METHOD FOR ENHANCED FORMATION EVALUATION
Conventional formation evaluation provides fast and accurate estimations of petrophysical properties in conventional formations through conventional well logs and routine core analysis (RCA) data. However, as the complexity of the evaluated formations increases, conventional formation evaluation fails to provide accurate estimates of petrophysical properties. This inaccuracy is mainly caused by rapid variation in rock fabric (i.e., the spatial distribution of rock components) not properly captured by conventional well-logging tools and interpretation methods. Acquisition of high-resolution whole-core CT-scan images can help to identify rock-fabric-related parameters that can enhance formation evaluation. In a recent publication, we introduced a permeability-based cost function for simultaneous rock classification, optimization of the number of rock classes, and estimation of permeability. The incorporation of additional petrophysical properties into the proposed cost function can improve the reliability of the detected rock classes and ultimately improve the estimation of class-based petrophysical properties.

The objectives of this paper are (a) to introduce a robust optimization method for simultaneous assessment of rock types and petrophysical properties, (b) to automatically employ whole-core CT-scan images for enhanced estimates of petrophysical properties, (c) to integrate whole-core CT-scan images with well logs and RCA data for automatic rock classification, and (d) to derive class-based rock physics models for improved estimates of petrophysical properties.

First, we conducted conventional formation evaluation using well logs and RCA data for estimation of petrophysical properties. Then, we derived quantitative features from CT-scan images and core photos. We employed image-based features, RCA data, and CT-scan-based bulk density for optimization of the number of rock classes through an iterative workflow. The optimization of rock classes was accomplished using a physics-based cost function (i.e., a function of petrophysical properties of the rock) that compares class-based depth-by-depth estimates of petrophysical properties (e.g., permeability and porosity) with core-measured properties (or the estimates from a previous iteration) for the increasing number of image-based rock classes. This workflow will be repeated until a convergence in the cost function is achieved. Finally, we employed the optimum number of rock classes and class-based rock physics models for improved estimates of porosity and permeability.

We demonstrated the reliability of the proposed method using whole-core CT-scan images and core photos from a siliciclastic depth interval with measurable variation in rock fabric. Additionally, we employed well logs, RCA data, and CT-scan-based bulk density. The advantages of using whole-core CT-scan images are two-fold. First, it provides high-resolution quantitative features that capture rapid spatial variation in rock fabric, allowing accurate rock classification. Second, the use of CT-scan-based bulk density improved the accuracy of class-based porosity bulk density models. The optimum number of rock classes was consistent for all the evaluated cost functions. Class-based rock physics models, after honoring rock fabric, significantly improved the estimates of porosity and permeability values. A unique contribution of the introduced workflow, when compared to previously documented image-based rock classification workflows, is that it simultaneously improves estimates of both porosity and permeability, and it can capture rock classes that might not be identified using conventional rock classification techniques.
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Author(s):
Andres Gonzalez, Zoya Heidari, Olivier Lopez
Company(s):
The University of Texas at Austin, Equinor
Year:
2021