Fault interpretation enhances our understanding of complex geologic structures and stratigraphy apparent in 3D seismic images. Common steps in this interpretation include image processing to highlight faults, the construction of fault surfaces, and estimation of fault throws. Although all three of these steps have been automated to some extent by others, fault interpretation today typically requires significant manual effort, suggesting that further improvements in automatic methods are feasible and worthwhile. I first used an efficient algorithm to compute images of fault likelihoods, strikes, and dips from a 3D seismic image. From these three fault images, I then automatically extracted fault surfaces as meshes of quadrilaterals that coincide with ridges of fault likelihood. A quadrilateral mesh is a simple data structure alongside which one can easily gather samples of the 3D seismic image. I automatically estimated fault throws by minimizing differences in values of samples gathered from oppos...

Methods to compute fault images, extract fault surfaces, and estimate fault throws from 3D seismic images

Numerous methods have been proposed to automatically extract fault surfaces from 3D seismic images, and those surfaces are often represented by meshes of triangles or quadrilaterals. However, extraction of intersecting faults is still a difficult problem that is not well addressed. Moreover, mesh data structures are more complex than the arrays used to represent seismic images, and they are more complex than necessary for subsequent processing tasks, such as that of automatically estimating fault slip vectors. We have represented a fault surface using a simpler linked data structure, in which each sample of a fault corresponded to exactly one seismic image sample, and the fault samples were linked above and below in the fault dip directions, and left and right in the fault strike directions. This linked data structure was easy to exchange between computers and facilitated subsequent image processing for faults. We then developed a method to construct complete fault surfaces without holes using thi...

3D seismic image processing for faults

Fault detection is a crucial step in reservoir characterization. Despite the many tools developed in the past decades, automation of this task remains a challenge. We investigate the application of convolutional neural networks (CNNs) to seismic fault detection. CNN is a deep learning method growing in interest in the computer vision community, due to its high performances in a great variety of object detection tasks. One of the constraints of this method is the need to provide a massive number of interpreted data, a requirement particularly difficult to attend in the seismic area. To this end, we built a synthetic data set with simple fault geometries. The input of our network is the seismic amplitude only; the method does not require computing any seismic attribute. We apply a strategy of patch classification along the images, which requires a simple postprocess to extract the exact fault location. Our network shows good results on synthetic data and encouraging results when tested on regions of a real section of The Netherland offshore F3 block in the North Sea.

Seismic Fault Detection Using Convolutional Neural Networks Trained on Synthetic Poststacked Amplitude Maps

Reliable fault detection is one of the major tasks of subsurface interpretation and reservoir characterization from three-dimensional (3D) seismic surveying. This study presents an innovative workflow based on multi-attribute support vector machine (SVM) analysis of a seismic volume, which consists of four steps. First, three groups of seismic attributes are selected and computed from the volume of seismic amplitude, including edge-detection, geometric, and texture, all of which clearly highlight the seismic faults in the attribute images. Second, two sets of training samples are prepared by manually picking on the faults and the nonfaulting zones, respectively. Third, the SVM analysis is performed on the training datasets that builds an optimal classification model for volumetric processing. Finally, applying the SVM model to the whole seismic survey leads to a binary volume, in which the presence of a fault is labelled as ones. The added values of the proposed method are verified through applications to the seismic dataset over the Great South Basin in New Zealand, where the dominant features are polygonal faults of varying sizes and orientations. The results demonstrate not only good match between the detected faults and the original seismic images, but also great potential for quantitative fault interpretation, such as semi-automatic/automatic fault extraction, to aid structural framework modeling and reservoir simulation in the exploration areas of numerous faults and fractures

Seismic-fault detection based on multiattribute support vector machine analysis

Fault interpretation is one of the routine processes used for subsurface structure mapping and reservoir characterization from 3D seismic data. Various techniques have been developed for co...

Improving seismic fault detection by super-attribute-based classification

A novel approach to the automatic extraction of geological faults from three-dimensional (3-D) seismic data is described, and qualitative and quantitative comparisons of manually and automatically picked fault geometries interpreted from both high and medium quality 3-D seismic datasets are presented. An algorithm has been developed that allows semiautomated identification, extraction, and modeling of fault surfaces imaged in 3-D seismic datasets. Based on a multistage approach, the algorithm operates initially at a small spatial scale, identifying local discontinuities in the seismic horizons, and then gradually considers larger and larger segments of fault surfaces until a set of complete fault surfaces are identified. A large portion of the work involves merging of segments of fault surfaces, performed using a highest confidence first (HCF) stratagem, taking into consideration the context of the resultant fault geometry. We show that results from the automated fault picker compare favorably with a manually labeled set of faults surfaces interpreted from a high-quality dataset. Last, we present an estimate of the savings in human operator time that can be made by using the automated approach, indicating savings of multiple person-days for the multigigabyte datasets that typify the petroleum industry.

Fault surface detection in 3-D seismic data

A novel approach to the automatic extraction of geological faults from 3D seismic data is described and comparisons of manually and automatically picked fault geometries interpreted from a high-quality 3D seismic dataset are presented. Based on the premise that faults introduce discontinuities to otherwise coherent layers (horizons) in seismic data volumes, prototype software has been developed that employs a coherency measure to detect points of significant discontinuity. An iterative highest confidence first merging strategy then groups clusters of ‘faulty’ points to form many planar segments, or patches, of larger fault surfaces. Because a planar model cannot reflect the curvature and localized irregularities that characterize real fault surfaces, the 3D fault surface geometry is generated using a combined parametric and residual field model to link the fault patches. The parametric model, in our case a planar model, represents the basic structure of the fault surface whilst the residual field models the surface deviation from the planar model. This is one of the few projects of this kind that combines expertise from computer vision and structural geology. We conclude with a discussion on how empirical structural geological criteria describing the geometry of natural fault systems will be integrated with the autopicking software to optimize the geological validity of the interpreted fault geometry.

Automated interpretation of geological fault surfaces imaged in 3D seismic data

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Fault surface detection in 3-D seismic data

Automated interpretation of geological fault surfaces imaged in 3D seismic data