Seismic Attribute Analysis In Fluvial Seismic Geomorphology

It has long been recognized that significant geologic information can be derived from seismic data. Initially, these observations were limited to interpretations of2D seismic data. However, within the last fifteen years such observations have been extended to3D seismic volumes. Whenever seismic resolution and acoustic impedance contrasts of geological features allow, analyses of the character and form of features defined by3D seismic data give a direct indication of external form and internal architecture of depositional systems. This type of study is referred to as3D seismic geomorphology, which provides a qualitative means of mapping the geometry of reservoir, as well as a bridge to apply geomorphology of present-day landforms to subsurface data. Seismic geomorphology yields the greatest geologic insights. The comprehensive iteration of this discipline yields insights as to what depositional elements are present and consequently lead to a prediction of lithology distribution.At present, seismic attribute analysis is the most important seismic interpretation technology. For seismic geomorphology, seismic attribute analysis means many significant attributes and corresponding process methods to map depositional elements and predict lithology distribution. Specifically, ten key methods of seismic attribute analysis include attribute extraction algorithm, multi-attribute optimization workflows, fuzzy self-organization clustering, principal component anlaysis of attributes, non-linear network pattern recognition, seismic lithology analysis, multi-attribute color fusion, morphology auto-track, geometry measurement, and shape relationship regression. Among them, the attribute extraction algorithm, multi-attribute optimization workflow, the nonlinear network pattern recognition, multi-attribute color fusion, and software integration are the focus of this study. And the rest of methods are discussed in case studies.In extraction algorithm study, the Gray Level Co-occurrence Matrix (GLCM) attribute was highlighted for seismic texture analysis. The discussion involved the relationship among the gray levels, the size of moving window, and the distance and direction of gray pairs. For optimization workflows, based on compatibility and redundancy, independence and non-related of selected attribute fundamentals, the K-L transform highlighting independence, Rough Set theory highlighting compatibility, and Significance-Dispersion-Correlation theory highlighting relevance, were combined into eight series optimization workflows. To solve the failure of pattern recognition because of lack of training well, the proposal was to dig more geological insight to supply the lack of log property from seismic facies that were the results of fuzzy self-organizing clustering. With estimateed well property, integrated multiple linear regression and radial basis function neural network was used to the quality reservoir prediction. The principal component color blending is a vision-based enhancement display. In the blending digital image, the geological features were observable based on visual features including regional, mutations, abnormal, etc al.KL10-1oil field is a hundred million tons reserves play in Laizhouwan Subbasin, Bohai Bay, China. One of main reservoir, the Minghuazhen Formation, is very complex fluvial reservoir. Only qualitative deposition characteristics are not workable for exploration and production. Fortunately, a high quality of3D seismic volume provided a reliable data for application of seismic geomorphology in Laizhouwan Subbasin. The primary goals of this study are to trace and understand the shapes of complex meander channels toward the development of sedimentary models of channel distribution. First,11seismic geometrical attributes were extracted to provide large quantities of morphometric data on the channel plan-view images of four fluvial phases. These data were then co-analyzed to yield the desired5morphometric measures including channel width, meander-belt width, meander length, meander-arc height and sinuosity. Finally, the relationships, if any, among the5geometric measures were assessed to understand channel reservoir distribution in the study area.Bar graph of sinuosity shows the dominant grain size transported by flow that is bedload and mixed-load in four fluvial phases. The dominant scale of channel is an important consideration in reservoir modeling and flow simulation. Statistics of geometric measures, for instance P50values, provide controls on reservoir parameters. Five cross-plot analyses of morphometrics were obtained in fluvial channels, which help to understand the shape variability of the channel reservoir. Additionally, relationships among meander-belt width, meander-arc height and channel width appear to be consistently linear in both sedimentary channels and modern rivers. This provides further confidence in the validity of depositional model for predicting channel distribution and flow simulation.Constrained by seismic facies, multi-attribute pattern recognition method calculated four reservoir properties including sandstone thickness, porosity, permeability and oil saturation in Minghuazhen Formation. And then, four reservoir parameters stacked on channel morphology to analyze correlation. The thickness of sandstone is obviously controlled by a single channel morphology and size. The porosity, permeability and oil saturation is controlled by the total shape of fluvial distribution. In inner regions of the channel, channel widths are larger and four reservoir parameter values are higher. After comprehensive evaluation, inner regions of the channel are high quality reservoir for exploration and production in Minghuazhen Formation.