Seismic Facies Classification of an Intraslope Minibasin in The Keathley Canyon, Northern Gulf of Mexico

This work examines several volume attributes extracted from 3D seismic data with the goal of seismic facies classification and lithology prediction in intraslope minibasins. The study area is in the Keathley Canyon protraction (KC), within the middle slope of the Northern Gulf of Mexico (GOM). It lays within the tabular salt and minibasins province downdip of the main Pliocene and Pleistocene deltaic depocenters. Interaction between sedimentation and mobile salt substrate lead to the emergence of many stratigraphic patterns in the intraslope minibasins. Interest in subsalt formations left above salt formations poorly logged. Facies classification using Artificial Neural Network (ANN) was applied in those poorly logged areas. The resultant facies classes were calibrated and used to predict the lithology of the recognized facies patterns in an intraslope minibasin, away from well control. Three types of facies classes were identified: Convergent thinning, convergent baselaping and bypassing. The convergent baselaping are found to be the most sand rich among all other facies

Seismic Facies Classification of an Intraslope Minibasin in The Keathley Canyon, Northern Gulf of Mexico

This work examines several volume attributes extracted from 3D seismic data with the goal of seismic facies classification and lithology prediction in intraslope minibasins. The study area is in the Keathley Canyon protraction (KC), within the middle slope of the Northern Gulf of Mexico (GOM). It lays within the tabular salt and minibasins province downdip of the main Pliocene and Pleistocene deltaic depocenters. Interaction between sedimentation and mobile salt substrate lead to the emergence of many stratigraphic patterns in the intraslope minibasins. Interest in subsalt formations left above salt formations poorly logged. Facies classification using Artificial Neural Network (ANN) was applied in those poorly logged areas. The resultant facies classes were calibrated and used to predict the lithology of the recognized facies patterns in an intraslope minibasin, away from well control. Three types of facies classes were identified: Convergent thinning, convergent baselaping and bypassing. The convergent baselaping are found to be the most sand rich among all other facies

Investigation of Neotectonic Activity within the Shallow, Unconsolidated Stratigraphy of the Pearl River Delta Area, Louisiana

During the last half century researchers have suggested that active deformation driven by neotectonic activity has locally influenced areas of southeastern Louisiana in the form of wetland loss and coastal erosion. This study, within the Pearl River Delta Area of Louisiana, applied geomorphologic and stratigraphic methods of analysis to assess whether evidence of recent fault motion is present within the shallow, unconsolidated Holocene strata of the study area. Geomorphological historical change analyses focused on meander patterns, elongated water bodies and spatial changes in vegetation identify areas where fault motion may have recently occurred. The shallow stratigraphy was then investigated in these locations using vibracores and seismic reflection profiling. Facies relationships coupled with radiocarbon ages of select stratigraphic intervals led to the development of a detailed stratigraphic framework. Based on these relationships, data suggest that subsurface deformation, resultant of neotectonic activity, has recently occurred within the shallow, unconsolidated Holocene strata

Controlling realism and uncertainty in reservoir models using intelligent sedimentological prior information

Forecasting reservoir production has a large associated uncertainty, since this is the final part of a very complex process, this process is based on sparse and indirect data measurements. One the methodologies used in the oil industry to predict reservoir production is based on the Baye’s theorem. Baye’s theorem applied to reservoir forecasting, samples parameters from a prior understanding of the uncertainty to generate reservoir models and updates this prior information by comparing reservoir production data with model production response. In automatic history matching it is challenging to generate reservoir models that preserve geological realism (obtain reservoir models with geological features that have been seen in nature). One way to control the geological realism in reservoir models is by controlling the realism of the geological prior information. The aim of this thesis is to encapsulate sedimentological information in order to build prior information that can control the geological realism of the history-matched models. This “intelligent” prior information is introduced into the automatic history-matching framework rejecting geologically unrealistic reservoir models. Machine Learning Techniques (MLT) were used to build realistic sedimentological prior information models. Another goal of this thesis was to include geological parameters into the automatic history-match framework that have an impact on reservoir model performance: vertical variation of facies proportions, connectivity of geobodies, and the use of multiple training images as a source of realistic sedimentological prior information. The main outcome of this thesis is that the use of “intelligent” sedimentological prior information guarantees the realism of reservoir models and reduces computing time and uncertainty in reservoir production prediction