MACHINE LEARNING AND THE CONSTRUCTION OF A SEISMIC ATTRIBUTE-SEISMIC FACIES ANALYSIS DATA BASE

Currently, seismic facies and structural analysis requires a significant amount of time and effort by skilled interpreters. With the advances made by companies such as Amazon and Google with AI (artificial intelligence) and machine learning, many geoscientists (and perhaps more so, many geoscience managers) have identified the application of such technologies to the seismic interpretation workflow. Advancements of such technologies, such as machine learning based interpretation like self-organizing maps (SOM), principle component analysis (PCA) and independent component analysis (ICA), will both accelerate and quantify the seismic interpretation process. Seismic attributes highlight subtle features in the seismic data that help identify architectural elements that can be used to further define the environment of deposition. Likewise, seismic attributes delineate subtle faults, folds, and flexures that better define the history of tectonic deformation. However, the understanding of “which attribute best illuminates which feature” requires either considerable experience or a tedious search process over years for published analogues. The objective of this thesis is to identify the seismic facies of interest through a prototype a web-based seismic attribute-seismic facies analysis database that can be used not only as a guide for human interpreters, but also to select attributes for machine learning. I propose a rule-based decision tree application that suggests which attributes are good candidates for machine learning applications. There are many seismic facies. This thesis illustrates the objectives and a prototype web application using only two seismic facies: marine mass transport deposits and karst collapses. After initial validation, this product can then be improved and expanded upon by a larger user community to provide an interactive attribute selection platform for interpreters at large

Seismic Attributes: Taxonomic Classification for Pragmatic Machine Learning Application

Seismic interpretation involves more than simply picking faults and horizons. It involves the interpretation of geologic features — their geometry, morphology, and the context of one group of rocks to another. It involves using well log information, memories from fieldwork, and photos from outcrops. It involves the understanding of salt mechanics, wave propagation, and signal analysis. It requires context and agile minds that can readily distinguish mud volcanoes from salt diapirs or multiples from reflectors. It is a difficult practice, and individuals spend their entire careers devoted to it. Seismic attributes have always been considered by many to be an art form — a “dark art” — practiced by a chosen few. The proliferation of attributes to the workstation has not, unfortunately, proliferated the understanding of what the attributes mean or of what they are capable. Today, you will often find the seismic attribute specialists in quantitative interpretation or computational geophysics groups. The perspective of these specialists and of general interpreters can be quite different. They understand both physics and geology in different ways and at different levels. As the discipline moves toward new technologies and the promises of new algorithms like convolutional neural networks and other forms of machine learning, we must remind ourselves that the technical understanding required of scientists and professionals grows accordingly. However, like the proliferation of seismic attributes (e.g., geometric and single-trace), machine learning approaches will feel underwhelming by those who fail to understand both the algorithms and what can reasonably be achieved. This dissertation provides the reader with the foundational knowledge one requires to begin to understand seismic attributes and how they can be used with machine learning algorithms. I begin by establishing a common framework on which to communicate. I build upon that through the development of a procedure to enhance faults in seismic data using commercially available tools, and I end with the introduction of a simple, but effective, use of self-organizing maps, a simple machine learning algorithm

ATTRIBUTE ASSISTED SEISMIC FACIES, FAULTS, KARST, AND ANISOTROPY ANALYSIS

Seismic attributes provide quantitative measures of key statistical, geometric, or kinematic components of the 3D seismic volume. These measures can thus be subsequently used in 3D visualization, interactive crossplotting, or computer-assisted facies analysis. In this dissertation, I evaluate the attribute expression of seismic facies including karst collapse features, mass transport complexes, turbidites, and salt using 3D visualization and 3D pattern recognition. One of the more common and more important seismic facies is salt. Salt segmentation is critical for accelerating velocity modeling, which in turn is necessary for seismic depth migration. In general, geophysicists need to pick the high velocity salt interface manually. In the first chapter of the dissertation, I present a semi-supervised multiattribute clustering method, and apply it not only to salt segmentation, but also to mass transport complex, shale, and sand segmentation in the Gulf of Mexico. I develop a 3D Kuwahara filtering algorithm, and smooth the interior attribute response and sharpen the attribute contrast between one face with neighboring facies. Then, I manually paint target facies to evaluate the ability of candidate attributes to discriminate each seismic facies from the other. Crosscorrelating their histogram, candidate attributes with low correlation coefficients provide good facies discrimination. Kuwahara filtering significantly increases this discrimination. Kuwahara filtered attributes corresponding to interpreter-defined facies are then projected against a Generative Topological Mapping (GTM) manifold, resulting in a suite of n probability density functions (PDFs). The Bhattacharyya distance between the PDF of each unlabeled voxel to each facies PDF results in a probability volume of each interpreter-defined facies. In the second chapter, I introduce a 3D fault enhancement and skeletonization workflow. For large datasets, interpreter hand-picking of faults can be very time-consuming. This process can be accelerated by generating high resolution edge detecting attributes. Coherence is an algorithm that measures both stratigraphic and structural discontinuities. Application of a directional Laplacian of a Gaussian (LoG) filter to coherence volumes provides more continuous and sharper faults. To further increase fault resolution and preserve stratigraphic discontinuities, I skeletonize the filtered coherence volumes perpendicular to the discontinuities with the goal of providing subvoxel resolution. “Fault” points doesn’t fall on the geometric grid suggesting the distribution of the value onto eight neighboring grid points. I demonstrate this fault enhancement and skeletonization workflow through application to two datasets from New Zealand and the Gulf of Mexico. With the advent of shale resource plays, wide azimuth acquisition has become quite common. Migrating seismic gathers into different azimuthal bins provides a means to estimate horizontal stress and natural fractures. Different azimuths preferentially illustrate faults perpendicular to them. However, coherence applied to the lower fold azimuthally limited seismic volumes is contaminated by noise. In the third chapter, I improve the energy ratio coherence algorithm and extend it to map more subtle discontinuities, which can only be seen in different azimuthally limited seismic volumes. The main modification compared to the original energy ratio coherence algorithm is that I add the weighted covariance matrices of each azimuthal sectors together to form a single covariance matrix, thereby improving the signal-to-noise ratio. I apply this multi-azimuth coherence algorithm to two datasets from the Fort Worth Basin. In the fourth chapter, I summarize attribute-assisted interpretation in the Barnett Shale and the Ellenburger Group. Karst, faults, and joints are known to form geologic hazards for most Barnett Shale wells in the Fort Worth Basin. In the best cases, these drilling-related geohazards form conductive features that draw off expensive hydraulic fracturing fluid from the targeted shale formation. In the worst cases, the completed wells are hydraulically connected to the underlying Ellenburger aquifer and produce large amounts of water that must be disposed. Karst collapse generates a distinct morphologic pattern on 3D seismic data. I show that multiple attributes delineate different components of the same geologic features, thereby confirming my interpretation

Subsurface Geobody Imaging Using CMY Color Blending with Seismic Attributes

Recently, ideas of color blending have brought the enlightenment for subsurface geobody imaging in petroleum engineering. In this paper, we present this approach of CMY color blending and its application in subsurface geobody characterization by using seismic attributes data. The first step is to calculate three types of seismic attributes based on the Hilbert transform algorithm, including envelop, instantaneous phase, and instantaneous frequency. Then scale the three attributes and combine them together using CMY color model in three-dimensional environment, with each attribute corresponding to one primary color channel. Adjust the scale and offset for each color component and then mix them optimally to create one color-blended volume. The blended volume in CMY mode has plenty of geological information coming from the three input attributes, resulting in high resolution and accurate image for subsurface geobodies. Applications show good performances in buried channels, caves, and faults imaging. Based on the blended slice, the geological targets can be easily but accurately interpreted and depicted