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.

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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

10.30632/spwla-2021-0030 ◽

2021 ◽

Author(s):

Andres Gonzalez ◽

◽

Zoya Heidari ◽

Olivier Lopez ◽

◽

...

Keyword(s):

Ct Scan ◽

Cost Function ◽

Bulk Density ◽

Rock Physics ◽

Well Logs ◽

Petrophysical Properties ◽

Rock Classification ◽

Formation Evaluation ◽

Rock Fabric ◽

Porosity And Permeability

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.

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ANISOTROPY QUANTIFICATION USING HIGH-RESOLUTION WHOLE-CORE CT-SCAN IMAGES

10.30632/spwla-2021-0087 ◽

2021 ◽

Author(s):

Andres Gonzalez ◽

◽

Mehdi Teymouri ◽

Zoya Heidari ◽

Olivier Lopez ◽

...

Keyword(s):

Spatial Distribution ◽

Ct Scan ◽

Elastic Properties ◽

Image Data ◽

Core Material ◽

3D Ct ◽

Pore Scale ◽

Formation Evaluation ◽

3D Ct Scan ◽

Rock Anisotropy

Spatial anisotropy and heterogeneity in petrophysical properties can significantly affect formation evaluation of hydrocarbon bearing formations. A common example is permeability anisotropy, which is a consequence of the depositional mechanisms of sediments. Additionally, the variation in spatial distribution of rock components and the effect of post-depositional processes on the physical and chemical structure of the rock constituents can strongly impact the directional dependency of petrophysical, electrical, and elastic properties. Therefore, image-based quantification of spatial distribution of rock constituents can be used for anisotropy evaluation. Assessment of anisotropy has been previously accomplished through use of pore-scale images. However, the discrete nature of this images gives a narrow picture of anisotropy in larger scales. Whole-core computed tomography (CT) scan images, despite revealing the distribution of rock components at a coarser scale, provide a continuous medium for anisotropy estimation. Assessment of anisotropy using three-dimensional (3D) CT-scan data and incorporation of that information in well-log-based formation evaluation is, however, not widely studied or practiced in the petroleum industry. The objectives of this paper are (a) to develop a method to quantify anisotropy utilizing whole-core 3D CT-scan image stacks, (b) to provide a semi-continuous measure of rock anisotropy, and (c) to show the value of the proposed method by means of estimation of directional-dependent elastic properties. First, we pre-process the raw whole-core CT-scan images to remove undesired image artifacts and to generate an image containing pixels representing only the recovered core material. Then, we segment each whole-core CT-scan image stack into distinctive phases. Then, we conduct numerical simulations of electric potential distribution in conjunction with streamline tracing techniques to quantify the electrical tortuosity of the continuous phase in each cartesian direction. We employed the tortuosity distribution values in each direction as a measure of rock anisotropy. Finally, we use a simulation model to estimate direction-dependent elastic properties. We applied the introduced method to dual energy whole-core CT-scan image stacks acquired in a siliciclastic depth interval. Estimates of rock anisotropy obtained using the proposed method agreed with the observed visual distribution of the segmented phase and the observed heterogeneity in available slabbed whole-core photos and 2D CT-scan images. Additionally, estimation of directional-dependent elastic properties demonstrated the value of the proposed method. Anisotropy results coincided with directional-dependent estimation of elastic properties. We observed measurable anisotropy in the 3D CT-scan image stacks, which is important to be quantitatively taken into account in petrophysical/ mechanical evaluation of this formation. A unique contribution of the proposed workflow is the use of core-scale image data for anisotropy estimation and the continuous nature of the anisotropy estimates when compared with workflows employing only pore-scale image data. It should also be noted that the proposed method can potentially be employed to identify the optimum locations to acquire core plugs for further assessment of rock anisotropy.

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Integrated Multiphysics Workflow for Automatic Rock Classification and Formation Evaluation Using Multiscale Image Analysis and Conventional Well Logs

Petrophysics – The SPWLA Journal of Formation Evaluation and Reservoir Description ◽

10.30632/pjv61n5-2020a7 ◽

2020 ◽

Vol 61 (5) ◽

pp. 495-518

Author(s):

Andres Gonzalez ◽

◽

Lawrence Kanyan ◽

Zoya Heidari ◽

Olivier Lopez ◽

...

Keyword(s):

Image Analysis ◽

Well Logs ◽

Rock Classification ◽

Formation Evaluation ◽

Multiscale Image Analysis

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Clinicoradiologic Evaluation of Eagle’s Syndrome and its Management

Kathmandu University Medical Journal ◽

10.3126/kumj.v11i4.12527 ◽

2015 ◽

Vol 11 (4) ◽

pp. 305-309 ◽

Cited By ~ 5

Author(s):

M Pokharel ◽

S Karki ◽

I Shrestha ◽

BL Shrestha ◽

K Khanal ◽

...

Keyword(s):

Ct Scan ◽

Styloid Process ◽

History Taking ◽

3D Ct ◽

Ct Reconstruction ◽

Eagle’S Syndrome ◽

Elongated Styloid Process ◽

Intraoral Approach ◽

3D Ct Scan ◽

Significant Difference

Background Eagle’s syndrome (Elongated styloid process) is often misdiagnosed due to its vague symptomatology. The diagnosis relies on detail history taking, palpation of styloid process in tonsillar fossa and imaging modalities.Objective To assess the length and medial angulation of elongated styloid process with the help of three dimensional computed tomography (3D CT) scan and to describe our clinical and surgical experience with patients suffering from Eagle’s syndrome.Method Prospective, analytical study conducted from August 2011 to August 2012 among 39 patients with Eagle’s syndrome. Detailed history taking, clinical examination and 3D CT scan was performed. Length and medial angulation was calculated. Patients with styloid process length longer than 2.50 cm underwent surgical excision via intraoral approach. Medial angulation of styloid process on both sides was correlated with each other using rank correlation coefficient. Wilcoxon Signed Rank test was applied to test significant difference between pre-operative and post-operative symptoms scores.Result Significant positive correlation was found between the medial angulation of styloid process on right side and left side (? =0.81, p<0.001). Significant difference was also observed between pre and post-operative symptoms scores (z=-5.16, p<0.001) .Conclusion Possibility of Eagle’s syndrome should always be considered while examining patients with vague neck pain. 3D CT reconstruction is a gold standard investigation which helps in studying the relation of styloid process with surrounding structures along with accurate measurement of its length and medial angulation.Kathmandu Univ Med J 2013; 11(4): 305-309

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