Abstract: Method for automated and quantitative assessment of multiple direct hydrocarbon indicators (“DHI's”) extracted from seismic data. DHI's are defined in a quantitative way (33), making possible a method of geophysical prospecting based on quantification of DHI anomalies. Instead of working in a particular spatial region of seismic data pre-defined as a hydrocarbon opportunity, the present invention works on entire data volumes derived from the measured seismic data (31), and identifies opportunities based on quantified DHI responses. In some embodiments, a series of algorithms utilizes the geophysical responses that cause DHI's to arise in seismic data to search entire data sets and identify hydrocarbon leads based on the presence of individual and/or combinations of DHI's (34).

Abstract: Method for analyzing seismic data representing a subsurface region for presence of a hydrocarbon system or a particular play. Seismic attributes are computed, the attributes being selected to relate to the classical elements of a hydrocarbon system, namely reservoir, seal, trap, source, maturation, and migration. Preferably, the attributes are computed along structural fabrics (1) of the subsurface region, and are smoothed over at least tens or hundreds of data voxels. The resulting geologic attributes (2) are used to analyze the data for elements of the hydrocarbon system and/or recognition of specific plays, and for ranking and annotating partitioned regions (3) of the data volume based on size, quality, and confidence in the prospectivity prediction (5). A catalog (8) of hydrocarbon trap configurations may be created and used to identify potential presence of hydrocarbon traps and/or aid in scoring (4) and ranking partitioned regions as hydrocarbon prospects.

Abstract: Method for automated and quantitative assessment of multiple direct hydrocarbon indicators (“DHI's”) extracted from seismic data. DHI's are defined in a quantitative way (33), making possible a method of geophysical prospecting based on quantification of DHI anomalies. Instead of working in a particular spatial region of seismic data pre-defined as a hydrocarbon opportunity, the present invention works on entire data volumes derived from the measured seismic data (31), and identifies opportunities based on quantified DHI responses. In some embodiments, a series of algorithms utilizes the geophysical responses that cause DHI's to arise in seismic data to search entire data sets and identify hydrocarbon leads based on the presence of individual and/or combinations of DHI's (34).

Abstract: Method for analysis of hydrocarbon potential of subterranean regions by generating surfaces or geobodies and analyzing them for hydrocarbon indications. Reflection-based surfaces may be automatically created in a topologically consistent manner where individual surfaces do not overlap themselves and sets of multiple surfaces are consistent with stratigraphic superposition principles. Initial surfaces are picked from the seismic data (41), then broken into smaller parts (“patches”) that are predominantly topologically consistent (42), whereupon neighboring patches are merged in a topologically consistent way (43) to form a set of surfaces that are extensive and consistent (“skeleton”). Surfaces or geobodies thus extracted may be automatically analyzed and rated (214) based on a selected measure (213) such as AVO classification or one or more other direct hydrocarbon indicators (“DHI”).

Abstract: Method for analyzing seismic data representing a subsurface region for presence of a hydrocarbon system or a particular play. Seismic attributes are computed, the attributes being selected to relate to the classical elements of a hydrocarbon system, namely reservoir, seal, trap, source, maturation, and migration. Preferably, the attributes are computed along structural fabrics (1) of the subsurface region, and are smoothed over at least tens or hundreds of data voxels. The resulting geologic attributes (2) are used to analyze the data for elements of the hydrocarbon system and/or recognition of specific plays, and for ranking and annotating partitioned regions (3) of the data volume based on size, quality, and confidence in the prospectivity prediction (5). A catalogue (8) of hydrocarbon trap configurations may be created and used to identify potential presence of hydrocarbon traps and/or aid in scoring (4) and ranking partitioned regions as hydrocarbon prospects.

Abstract: Method for identifying geologic features from geophysical or attribute data using windowed principal component (22), or independent component, or diffusion mapping (61) analysis. Subtle features are made identifiable in partial or residual data volumes. The residual data volumes (24) are created by (36) eliminating data not captured by the most prominent principal components (14). The partial data volumes are created by (35) projecting the data (21) on to selected principal components (22, 61). Geologic features may also be identified from pattern analysis (77) or anomaly volumes (62, 79) generated with a variable-scale data similarity matrix (73). The method is suitable for identifying physical features indicative of hydrocarbon potential.

Abstract: Method for identifying geologic features from geophysical or attribute data using windowed principal component (or independent component) analysis. Subtle features are made identifiable in partial or residual data volumes. The residual data volumes (24) are created by (36) eliminating data not captured by the most prominent principal components (14). The partial data volumes are created by (35) projecting the data on to selected principal components. The method is suitable for identifying physical features indicative of hydrocarbon potential.

Abstract: Method for identifying geologic features from geophysical or attribute data using windowed principal component (22), or independent component, or diffusion mapping (61) analysis. Subtle features are made identifiable in partial or residual data volumes. The residual data volumes (24) are created by (36) eliminating data not captured by the most prominent principal components (14). The partial data volumes are created by (35) projecting the data (21) on to selected principal components (22, 61). Geologic features may also be identified from pattern analysis (77) or anomaly volumes (62, 79) generated with a variable-scale data similarity matrix (73). The method is suitable for identifying physical features indicative of hydrocarbon potential.

Abstract: Method for analysis of hydrocarbon potential of subterranean regions by generating surfaces or geobodies and analyzing them for hydrocarbon indications. Reflection-based surfaces may be automatically created in a topologically consistent manner where individual surfaces do not overlap themselves and sets of multiple surfaces are consistent with stratigraphic superposition principles. Initial surfaces are picked from the seismic data (41), then broken into smaller parts (“patches”) that are predominantly topologically consistent (42), whereupon neighboring patches are merged in a topologically consistent way (43) to form a set of surfaces that are extensive and consistent (“skeleton”). Surfaces or geobodies thus extracted may be automatically analyzed and rated (214) based on a selected measure (213) such as one or more direct hydrocarbon indications (“DHI”), e.g. AVO classification.

Abstract: A method for assessing the suitability of seismic data for quantitative amplitude analysis, where the concern is excessive residual normal moveout (RNMO). The invention uses a near offset stack and a far offset stack, the time difference between the two, a mute pattern, a reflection shape assumption for the RNMO, and a waveform and frequency for the far stack traces to generate a formula that estimates far stack amplitude error caused by RNMO. The formula can be used to compensate the far stack amplitude where the error is not so great as to require reprocessing of the data. The method can also be applied to interpreted amplitude maps.