Integration of 3D Volumetric Computed Tomography Scan Image Data with Conventional Well Logs for Detection of Petrophysical Rock Classes

Summary Core measurements are used for rock classification and improved formation evaluation in both cored and noncored wells. However, the acquisition of such measurements is time-consuming, delaying rock classification efforts for weeks or months after core retrieval. On the other hand, well-log-based rock classification fails to account for rapid spatial variation of rock fabric encountered in heterogeneous and anisotropic formations due to the vertical resolution of conventional well logs. Interpretation of computed tomography (CT) scan data has been identified as an attractive and high-resolution alternative for enhancing rock texture detection, classification, and formation evaluation. Acquisition of CT scan data is accomplished shortly after core retrieval, providing high-resolution data for use in petrophysical workflows in relatively short periods of time. Typically, CT scan data are used as two-dimensional (2D) cross-sectional images, which is not suitable for quantification of three-dimensional (3D) rock fabric variation, which can increase the uncertainty in rock classification using image-based rock-fabric-related features. The methods documented in this paper aim to quantify rock-fabric-related features from whole-core 3D CT scan image stacks and slabbed whole-core photos using image analysis techniques. These quantitative features are integrated with conventional well logs and routine core analysis (RCA) data for fast and accurate detection of petrophysical rock classes. The detected rock classes are then used for improved formation evaluation. To achieve the objectives, we conducted a conventional formation evaluation. Then, we developed a workflow for preprocessing of whole-core 3D CT-scan image stacks and slabbed whole-core photos. Subsequently, we used image analysis techniques and tailor-made algorithms for the extraction of image-based rock-fabric-related features. Then, we used the image-based rock-fabric-related features for image-based rock classification. We used the detected rock classes for the development of class-based rock physics models to improve permeability estimates. Finally, we compared the detected image-based rock classes against other rock classification techniques and against image-based rock classes derived using 2D CT scan images. We applied the proposed workflow to a data set from a siliciclastic sequence with rapid spatial variations in rock fabric and pore structure. We compared the results against expert-derived lithofacies, conventional rock classification techniques, and rock classes derived using 2D CT scan images. The use of whole-core 3D CT scan image-stacks-based rock-fabric-related features accurately captured changes in the rock properties within the evaluated depth interval. Image-based rock classes derived by integration of whole-core 3D CT scan image-stacks-based and slabbed whole-core photos-based rock-fabric-related features agreed with expert-derived lithofacies. Furthermore, the use of the image-based rock classes in the formation evaluation of the evaluated depth intervals improved estimates of petrophysical properties such as permeability compared to conventional formation-based permeability estimates. A unique contribution of the proposed workflow compared to the previously documented rock classification methods is the derivation of quantitative features from whole-core 3D CT scan image stacks, which are conventionally used qualitatively. Furthermore, image-based rock-fabric-related features extracted from whole-core 3D CT scan image stacks can be used as a tool for quick assessment of recovered whole core for tasks such as locating best zones for extraction of core plugs for core analysis and flagging depth intervals showing abnormal well-log responses.

A NEW OPTIMIZATION METHOD FOR ENHANCED FORMATION EVALUATION AND ROBUST PHYSICS-BASED AUTOMATIC ROCK CLASSIFICATION USING HIGH-RESOLUTION CT-SCAN IMAGE DATA AND CONVENTIONAL WELL LOGS

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., spatial distribution of rock components) not properly captured by conventional well logging tools and interpretation methods. Acquisition of high-resolution whole-core computed tomography (CT) scanning 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 rock classification, optimization of the number of rock classes, and estimation of permeability. Incorporation of additional petrophysical properties into the proposed cost function can improved 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 rock classification and estimation of petrophysical properties, (b), to automatically employ whole-core two-dimensional (2D) CT-scan images and slabbed whole-core photos for enhanced estimates of petrophysical properties, (c) to integrate whole-core CT-scan images and slabbed whole-core photos with well logs and RCA data for automatic rock classification, (d) to derive class-based rock physics models for improved estimates of petrophysical properties. First, we conducted formation evaluation using well logs and RCA data for estimation of petrophysical properties. Then, we derived quantitative features from 2D CT-scan images and slabbed whole-core photos. We employed image-based features, RCA data and CT-scan-based bulk density for optimization of the number rock classes. 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 estimates of petrophysical properties (e.g., permeability and porosity) with core-measured properties for increasing number of image-based rock classes. The cost function is computed until convergence is achieved. Finally, we used 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 two siliciclastic depth intervals with measurable variation in rock fabric. We used well logs, RCA data, and CT-scan-based bulk-density. The advantages of using whole-core CT-scan data 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 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 class that might not be identifiable using conventional rock classification techniques.

APPLICATION OF IMAGE LOGS FOR ENHANCED RESISTIVITY-BASED WATER SATURATION ASSESSMENT IN ORGANIC-RICH MUDROCKS

Organic-rich mudrocks are complex in terms of rock fabric (i.e., the spatial distribution of rock components), which impacts electrical resistivity measurements and, therefore, estimates of hydrocarbon reserves. Conventional resistivity-saturation-porosity methods for assessment of water/hydrocarbon saturation do not reliably incorporate the spatial distribution of rock components and pores in the assessment of fluid saturation. Extensive calibration efforts are required for indirectly projecting the impact of rock fabric on resistivity models. For instance, none of the existing shaly-sand models incorporate a realistic distribution of clay network. This might be acceptable in conventional reservoirs. However, oversimplifying assumptions can cause significant uncertainty in reserves evaluation in organic-rich mudrocks. It should be noted that even the methods which incorporate the realistic distribution of rock components are difficult to calibrate. To address the aforementioned challenge, we introduce a joint interpretation of conventional resistivity and resistivity image logs to improve water saturation assessment by honoring the type of rock component, the spatial distribution of the conductive and non-conductive rock components, and the volumetric concentration of fluids and minerals in the rock. Borehole image logs are a source of high-resolution continuous rock sequence records and can provide detailed rock-fabric-related features. In this paper, we propose a method for the estimation of lamination density and mean resistivity value from image logs within each rock type. These fabric-related features are used to quantify the geometric model parameters for each conductive component of the rock. We use these geometric model parameters as inputs to a new resistivity model that considers volumetric concentration and spatial distribution of rock components for a depth-by-depth assessment of water saturation. The other inputs to the workflow are the volumetric concentration of conductive and non-conductive rock components, electrical conductivity of rock components, and porosity estimates from the joint interpretation of well logs. We successfully applied the proposed workflow to a dataset from the Wolfcamp formation in the Permian Basin in which resistivity image logs were available. We observed a measurable variation in estimated image-log-based geometric model parameters, which were in agreement with the visual content of the images. Incorporation of the estimated rock-class-based geometric model parameters in the resistivity model improved water saturation assessment. Results demonstrated a relative improvement in water saturation estimates of 44.2% and 59.1% against Waxman-Smits and Archie's models, respectively. We then used the estimated geometric model parameters for each rock type for a depth-by-depth assessment of water saturation in one additional well without image logs. This led to a faster and more reliable assessment of water saturation within a certain distance from the well with image logs, where the rock types remain comparable. This distance can be evaluated using variogram analysis. We demonstrated that using the estimated geometric model parameters could improve estimates of hydrocarbon reserves in the Permian Basin by approximately 34%. It should be noted that the proposed method for assessment of geometric model parameters is completely based on the actual spatial distribution of rock components and does not require core-based calibration efforts.

Role of pre-existing fabric in abetting fracture formation, fluid flow and vein emplacement in the metavolcanics: a domain for shallow crustal gold mineralization in the Archean greenstone belt, India

<p>Archean greenstone belts gained prominence for its gold mineralization. Gold bearing vein infillings within fracture systems are significant for its economic utility. Fracture formation is often associated with reactivation of the pre-existing host rock fabric under a compatible stress field. Upper crustal fluids are mostly channelized through these fracture systems under variable fluid pressure conditions generating a widespread network of veins. A wide range of vein infilled crosscutting fractures of variable thicknesses, are investigated from the gold-bearing massive metabasalts (supracrustals) of the Chitradurga Schist Belt (CSB) adjacent to the Chitradurga Shear Zone (CSZ), Western Dharwar Craton, southern India. Anisotropy of magnetic susceptibility (AMS) studies are adopted for determining the internal anisotropy of the apparently massive metabasalt hosts. The study involves tensile strength determination of the metabasalts, deciphering the paleostress condition using fault-slip analysis and propensity of fracture/fault reactivation under the prevailing stress field. Parameters like stress ratio (ϕ) and driving pressure ratio (R´) are evaluated for understanding the conditions of fluid induced fracture opening/reactivation. Change in the opening angle (µ) of fractures with fluid pressure (P<sub>f</sub>) variation, ϕ and R´ variations with the range of fracture orientations are also ventured upon.</p><p>            We conclude ~NW-SE oriented (mean 337°/69° NE) magnetic fabric in the metabasalts are a product of regional D1/D2 deformation on an account of NE–SW shortening. This was followed by the D3 deformation with NW–SE to E–W shortening that led to the sinistral movement along CSZ. Thus, prominent fracture orientations representing riedel shear components were formed as a consequence of this sinistral shearing. Under compatible fluid pressure conditions, all such cohesionless pre-existing pathways were reactivated. Schematic models help to understand the mechanism of vein emplacement under episodic fluid pressure fluctuations from high to low P<sub>f</sub> at shallow crustal depth (~2.4 km). With respect to the prevailing stress field, fracture orientations coinciding with the host rock fabric show higher values of slip/dilatation tendencies justifying maximum vein thickness along this orientation. Multiple methods were integrated to develop a better understanding of the fracture networking system, channelizing fluids and assisting gold mineralization in the greenstone belt.</p><p> </p>