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Seismic Attributes for Resource Plays

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Seismic Attributes for Resource Plays

Seismic attributes are routinely used to map seismic geomorphology and reservoir quality. With the more recent focus on unconventional resource plays, seismic attributes are also being used to evaluate completion quality and potential drilling hazards. Geometric attributes such as coherence and curvature are invaluable in identifying geohazards from 3D seismic data. Curvature and aberrancy are direct measures of strain, which along with thickness and lithology can indicate the location and intensity of natural fractures. Prestack inversion for Young’s modulus and Poisson’s ratio (or equivalently for λρ and µρ) can be used (when calibrated against core and ECS logs) to estimate TOC and “brittleness”. A more quantitative estimate of brittleness and completion quality requires the use of microseismic and production log data. Velocity and amplitude anisotropy, calibrated against image logs and microseismic data provide measurements of open natural fractures and the present-day direction of maximum horizontal stress that can be used to guide the placement of lateral wells.

Much of today’s resource play drilling activity focuses on prioritizing properties, reducing costs, and holding acreage. As resource plays mature, we will want to identify bypassed pay and evaluate the benefits of restimulation. Geology, and hence seismic data and seismic attributes is only one of the components necessary to predict successful completion and estimate ultimate production. Conversely, while typically considered to be the driller’s problem, we predict that seismic data and seismic attributes will be able to statistically identify areas of slower rate of penetration and number of bit trips.

In this course, we will gain an intuitive understanding of the kinds of seismic features identified by 3D seismic attributes, the sensitivity of seismic attributes to seismic acquisition and processing, and of how ‘independent’ seismic attributes are coupled through geology. Attributes are only as good as the data that goes into them. For this reason, we will also address components of seismic acquisition, reprocessing, and data conditioning. We will review a sufficient amount of theory for inversion, bandwidth extension, cluster analysis, and neural networks to elicit the implicit assumptions made using these technologies. Advanced knowledge of seismic theory is not required; this course focuses on understanding and practice.

Concepts and algorithm descriptions will be general, but workflows will be illustrated through application to the Barnett Shale, Woodford Shale, and Mississippi Lime resource plays. Case studies will discuss workflows for Vaca Muerta Shale, Utica Shale, Bone Spring and Wolfcamp Shale and the Duvernay Shale resource plays.