3-D fracture tracing for X-ray computed tomography data

X-ray computed tomography (CT) is a nondestructive imaging method that shows differences in X-ray attenuation, which is a proxy for density. Sensitivity to density differences makes CT a good choice for imaging open natural fractures because of the significant density contrast between air and rock. The image data, however, are prone to artifacts and blurring, which makes taking accurate measurements challenging. As a first step to addressing this problem, a tool was created to quantify the spatial resolution of CT data with a point-spread function (PSF). The PSF tool permits accurate measurement of fine-scale features in CT data – critical for measuring fractures, which are often thinner in one dimension than the PSF size, in turn influencing measurement of aperture. The difference between the measurements given by the PSF method and a simple threshold value pick is shown to demonstrate a non-trivial improvement in accuracy. In addition, a 3-D fracture network tracing algorithm was developed, for which the PSF is a necessary input, to characterize accurately the network’s attributes, such as fracture orientations, apertures, and roughnesses. To date, only single, isolated, relatively flat fractures have been characterized thoroughly in 3-D for CT data, and most numerical modeling has been conducted on 2D subsets. Additionally, research involving fracture networks is currently limited to simplified numerical models and simulations. This new work extends CT fracture characterization to the majority of fractured materials with 3-D networks, thus providing a source of real data for studying the difference between fracture networks and single fractures. A sample of Packsaddle schist, with a large number of thin fractures and complex, anastomosing bifurcations, was selected as a testing ground for the network tracing algorithms being developed. Preliminary analysis verifies the method’s efficacy.Geological Science

GEOMETRIC ANALYSIS TOOLS FOR MESH SEGMENTATION

Surface segmentation, a process which divides a surface into parts, is the basis for many surface manipulation applications which include model metamorphosis, model simplifica- tion, model retrieval, model alignment and texture mapping. This dissertation discusses novel methods for geometric surface analysis and segmentation and applications for these methods. Novel work within this dissertation includes a new 3D mesh segmentation algo- rithm which is referred to as the ridge-walking algorithm. The main benefit of this algo- rithm is that it can dynamically change the criteria it uses to identify surface parts which allows the algorithm to be adjusted to suit different types of surfaces and different segmen- tation goals. The dynamic segmentation behavior allows users to extract three different types of surface regions: (1) regions delineated by convex ridges, (2) regions delineated by concave valleys, and (3) regions delineated by both concave and convex curves. The ridge walking algorithm is quantitatively evaluated by comparing it with competing algo- rithms and human-generated segmentations. The evaluation is accompanied with a detailed geometrical analysis of a select subset of segmentation results to facilitate a better under- standing of the strengths and weaknesses of this algorithm. The ridge walking algorithm is applied to three domain-specific segmentation prob- lems. The first application uses this algorithm to partition bone fragment surfaces into three semantic parts: (1) the fracture surface, (2) the periosteal surface and (3) the articular surface. Segmentation of bone fragments is an important computational step necessary in developing quantitative methods for bone fracture analysis and for creating computational tools for virtual fracture reconstruction. The second application modifies the 3D ridge walking algorithm so that it can be applied to 2D images. In this case, the 2D image is modeled as a Monge patch and principal curvatures of the intensity surface are computed iv for each image pixel. These principal curvatures are then used by ridge walking algorithm to segment the image into meaningful parts. The third application uses the ridge walking algorithm to facilitate analysis of virtual 3D terrain models. Specifically, the algorithm is integrated as a part of a larger software system designed to enable users to browse, visualize and analyze 3D geometric data generated by NASA’s Mars Exploratory Rovers Spirit and Opportunity. In this context, the ridge walking algorithm is used to identify surface features such as rocks in the terrain models

Image-Based Modeling of Porous Media Using FEM and Lagrangian Particle Tracking

The study of fundamental flow and transport processes at the pore scale is essential to understanding how the mechanisms affect larger, field-scale, processes that occur in oil and gas recovery, groundwater flow, contaminant transport, and CO2 sequestration. Pore-scale imaging and modeling is one of the techniques used to investigate these fundamental mechanisms. Although extensive development of pore-scale imaging and modeling has occurred recently, some areas still need further advances. In this work, we address two areas: (1) imaging of bulk proppants and proppant-filled fractures under varying loading stress and flow simulation in these systems and (2) nanoparticle (NP) transport modeling in porous media. These are briefly explained below. Rock fracturing, followed by proppant injection, has been used for years to improve oil and gas production rates in low permeability reservoirs and is now routinely used in low-permeability resources such as a shales and tight sands. While field data makes clear the effectiveness of this technique, there is still much room to improve on the science, including how the proppant-filled fracture system responds to changes in loading stress which affect permeability and conductivity. Here, we use high-resolution x-ray computed tomography (XCT) to image two unsaturated rock/fracture/proppant systems under a series of stress levels typical of producing reservoirs: one with shale, one with Berea sandstone. The resulting XCT images were segmented, analyzed for structural and porosity changes, and then used for image-based flow modeling of Stokes flow using both finite element (FEM) and Lattice Boltzmann methods. NPs have been widely used commercially and have the potential to be extensively used in petroleum engineering as stabilizers in enhanced oil recovery operations or as tracers or sensors to detect rock and fluid properties. %In spite of a wide range of applications, many NP transport details are still unknown. In this work, we describe a Lagrangian particle tracking algorithm to model NP transport that can be used to better understand the impact of pore-scale hydrodynamics and surface forces on NP transport. Two XCT images, a Berea sandstone and a 2.5D micromodel, were meshed and used for image-based flow modeling of FEM Stokes flow. The effects of particle size, surface forces, flow rate, particle density, surface capacity, and surface forces mapped to XCT-image based mineralogy were studied

Model Calibration, Drainage Volume Calculation and Optimization in Heterogeneous Fractured Reservoirs

We propose a rigorous approach for well drainage volume calculations in gas reservoirs based on the flux field derived from dual porosity finite-difference simulation and demonstrate its application to optimize well placement. Our approach relies on a high frequency asymptotic solution of the diffusivity equation and emulates the propagation of a 'pressure front' in the reservoir along gas streamlines. The proposed approach is a generalization of the radius of drainage concept in well test analysis (Lee 1982), which allows us not only to compute rigorously the well drainage volumes as a function of time but also to examine the potential impact of infill wells on the drainage volumes of existing producers. Using these results, we present a systematic approach to optimize well placement to maximize the Estimated Ultimate Recovery. A history matching algorithm is proposed that sequentially calibrates reservoir parameters from the global-to-local scale considering parameter uncertainty and the resolution of the data. Parameter updates are constrained to the prior geologic heterogeneity and performed parsimoniously to the smallest spatial scales at which they can be resolved by the available data. In the first step of the workflow, Genetic Algorithm is used to assess the uncertainty in global parameters that influence field-scale flow behavior, specifically reservoir energy. To identify the reservoir volume over which each regional multiplier is applied, we have developed a novel approach to heterogeneity segmentation from spectral clustering theory. The proposed clustering can capture main feature of prior model by using second eigenvector of graph affinity matrix. In the second stage of the workflow, we parameterize the high-resolution heterogeneity in the spectral domain using the Grid Connectivity based Transform to severely compress the dimension of the calibration parameter set. The GCT implicitly imposes geological continuity and promotes minimal changes to each prior model in the ensemble during the calibration process. The field scale utility of the workflow is then demonstrated with the calibration of a model characterizing a structurally complex and highly fractured reservoir

Automated Rock Fracture Detection Algorithm with Convolutional Neural Networks

학위논문(석사)--서울대학교 대학원 :공과대학 에너지시스템공학부,2019. 8. 송재준.암반에 존재하는 균열과 절리는 강도, 탄성계수, 투수계수 등에 큰 영향을 미치기 때문에 이들을 잘 검출하는 것은 중요한 문제이다. 특히 사진측량법은 간단하고 경제성이 있으므로 이를 기반으로 하는 연구들이 이루어졌다. 그 중, 절리는 선형성을 띠기 때문에 비교적 인식이 평이하나, 균열은 비정형성을 보이기 때문에 인식이 상대적으로 어려워 관련 연구가 부족한 실정이다. 또한, 기존의 연구들은 균열 인식을 방해하는 다양한 노이즈가 없는 이미지를 주로 사용하였다. 그림자, 균열 사이의 충전물, 식생 등의 노이즈는 전통적인 알고리즘의 균열 인식 정확도를 낮추는 요인이나, 사진을 촬영한 현장의 환경에 따라 사진에 포함될 수 있다. 본 연구에서는 딥러닝 알고리즘의 일종인 합성곱 신경망을 사용하여 다양한 노이즈가 존재하는 이미지로부터 암석 균열을 자동으로 인식하는 알고리즘을 개발하였다. 딥러닝 기반 합성곱 신경망 알고리즘은 사람이 직접 시행착오를 통해 적절한 피처를 결정했던 전통적인 이미지 처리 방식과 달리 신경망이 스스로 이미지에서 적합한 피처를 추출하여 이용하기 때문에 인식 성능이 향상된다. 또한, 기존 연구들은 모델 개발에 사용한 균열 이미지와 같은 이미지로 테스트를 수행하였기 때문에 그로부터 개발된 모델은 그 특정 이미지에 대해서만 좋은 성능을 낼 수 있었다. 그러나, 본 연구에서는 테스트 과정에 완전히 새로운 균열 이미지를 사용함으로써 새로운 이미지에도 좋은 결과를 내는 것을 보였다. 종합적으로, 본 연구에서 개발된 알고리즘은 사진으로부터 신속하고 일관적으로 균열 인식을 할 수 있으며 다양한 비정형 균열 이미지들에 대해서도 높은 검출 성능을 보인다.Detection of rock joint and fracture is important because they have a huge influence on rock mass strength. Photogrammetry technique, especially, has been used for decades due to its simplicity and economic feasibility. Although joints are easy to detected since it has linearity, fractures has irregularity which leads to difficulties in detection and lack of relevant studies. Additionally, previous researches used photographs without various types of noise such as shadow, infill material and vegetation. These kinds of noise reduce the accuracy of conventional algorithms. However, it can be included in the photographs under certain circumstances. In this study, a new algorithm based on convolutional neural networks, which can detect rock fracture from rock images with many kinds of noise, is presented. Furthermore, previous models were evaluated with the same image used in model construction stage. The model performance, therefore, is guaranteed only for that specific data. On the contrary, new rock images are used when testing the model, which shows the data-independent performance of proposed model. As a result, the developed model in this study can detect rock fracture from photographs quickly and consistently, and demonstrate high performance for irregular fractures.목 차 1. 서론 1 2. 인공신경망 이론 5 2.1 완전연결 신경망 5 2.2 합성곱 신경망 8 3. 실험 방법 13 3.1 데이터 수집 14 3.2 데이터 가공 16 3.2.1 데이터 레이블링 16 3.2.2 데이터 분리 18 3.2.3 데이터 증강 20 3.2.4 데이터 전처리 23 3.3 합성곱 신경망 모델 구조 27 3.4 학습 상세 과정 30 3.5 후처리 과정 30 4. 결과 및 고찰 33 4.1 그림자, 검은 표면 등이 있는 사진 34 4.2 줄무늬 구조가 존재하는 사진 40 4.3 충전물이 존재하는 사진 43 4.4 긁힌 자국이 존재하는 사진 47 4.5 식생이 존재하는 사진 49 4.6 모델 성능 고찰 52 5. 결론 56 참고문헌 58Maste

Lineament Extraction using Gravity Data in the Citarum Watershed

Lineament is one of the most important features showing subsurface elements or structural weakness such as faults. This study aims to identify subsurface lineament patterns using automatic lineament in Citarum watershed with gravity data. Satellite gravity data were used to generate a sub-surface lineament. Satellite gravity data corrected using Bouguer and terrain correction to obtain a complete Bouguer anomaly value. Butterworth filters were used to separate regional and residual anomaly from the complete Bouguer anomaly value. Residual anomaly gravity data used to analyze sub-surface lineament. Lineament generated using Line module in PCI Geomatica to obtain sub-surface lineament from gravity residual value. The orientations of lineaments and fault lines were created by using rose diagrams. The main trends observed in the lineament map could be recognized in these diagrams, showing a strongly major trend in NW-SE, and the subdominant directions were in N-S. Area with a high density of lineament located at the Southern part of the study area. High-density lineament might be correlated with fractured volcanic rock upstream of the Citarum watershed, meanwhile, low-density lineament is associated with low-density sediment. The high-density fracture might be associated with intensive tectonics and volcanism

Integrated Object-Based Image Analysis for semi-automated geological lineament detection in Southwest England

This is the final version. Available on open access from Elsevier via the DOI in this record.Regional lineament detection for mapping of geological structure can provide crucial information for mineral exploration. Manual methods of lineament detection are time consuming, subjective and unreliable. The use of semi-automated methods reduces the subjectivity through applying a standardised method of searching. Object-Based Image Analysis (OBIA) has become a mainstream technique for classification of landcover, however, the use of OBIA methods for lineament detection is still relatively under-utilised. The Southwest England region is covered by high-resolution airborne geophysics and LiDAR data that provide an excellent opportunity to demonstrate the power of OBIA methods for lineament detection. Herein, two complementary but stand-alone OBIA methods for lineament detection are presented which both enable semi-automatic regional lineament mapping. Furthermore, these methods have been developed to integrate multiple datasets to create a composite lineament network. The top-down method uses threshold segmentation and sub-levels to create objects, whereas the bottom-up method segments the whole image before merging objects and refining these through a border assessment. Overall lineament lengths are longest when using the top-down method which also provides detailed metadata on the source dataset of the lineament. The bottom-up method is more objective and computationally efficient and only requires user knowledge to classify lineaments into major and minor groups. Both OBIA methods create a similar network of lineaments indicating that semi-automatic techniques are robust and consistent. The integration of multiple datasets from different types of spatial data to create a comprehensive, composite lineament network is an important development and demonstrates the suitability of OBIA methods for enhancing lineament detection.British Geological Survey (BGS)Natural Environment Research Council (NERC

The use of contextual techniques and textural analysis of satellite imagery in geological studies of arid regions

This Thesis examines the problem of extracting spatial information (context and texture) of use to the geologist, from satellite imagery. Part of the Arabian Shield was chosen to be the study area. Two new contextual techniques; (a) Ripping Membrane and (b) Rolling Ball were developed and examined in this study. Both new contextual based techniques proved to be excellent tools for visual detection and analysis of lineaments, and were clearly better than the 'traditional' spatial filtration technique. This study revealed structural lineaments, mostly mapped for the first time, which are clearly related to regional tectonic history of the area. Contextual techniques were used to perform image segmentation. Two different image segmentation methods were developed and examined in this study. These methods were the automatic watershed segmentation and ripping membrane/Laserscan system method (as this method was being used for the first time). The second method produced high accuracy results for four selected test sites. A new automatic lineament extraction method using the above contextual techniques was developed. The aim of the method was to produce an automatic lineament map and the azimuth direction of these lineaments in each rock type, as defined by the segmented regions. 75-85% of the visually traced lineaments were extracted by the automatic method. The automatic method appears to give a dominant trend slightly different (10° — 15°) from the visually determined trend. It was demonstrated that not all the different types of rock could be discriminated using the spectral image enhancement techniques (band ratio, principal components and decorrelation stretch). Therefore, the spatial grey level dependency matrix (SGLDM) was used to produce a texture feature image, which would enable distinctions to be made and overcome the limitations of spectral enhancement techniques. The SGLDM did not produce any useful texture features which can discriminate between every rock type in the selected test sites. It did, however, show some acceptable texture discrimination between some rock types. The remote sensing data examined in this thesis were the Landsat (multispectral scanner, Thematic Mapper), SPOT, and Shuttle Imaging Radar (SIR-B)