Kurt Marfurt, University of Oklahoma, Norman, OK
February 10 - 11, 2014
Norris Conference Center, City Centre location, Houston, TX
(if purchased individually)
Registration for the entire week is $1,795 for members, $2,095 nonmembers. Goes up to $1995/$2295, and/or individual course prices increase by $50/course day on 1/13/2014. Course notes, refreshments and lunch buffet included.
No refunds for cancellations after 1/13/2014.
This course is for geologists, geophysicists, engineers and other geoscientists who need to understand how seismic attributes are being used to map reservoir quality and evaluate completion quality in unconventional resource plays.
Upon completion of this course, participants will:
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. Geometric attributes such as coherence and curvature are invaluable in identifying geohazards from 3D seismic data. Curvature and reflector rotation are direct measures of strain, which along with thickness and lithology control 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 evaluating properties and holding acreage. As resource plays mature, we will want to identify bypassed pay and evaluate the benefits of restimulation. Even with access to such modern data, geology, and hence seismic data and seismic attributes are only one of the components necessary to predict EUR.
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 this these technologies. Advanced knowledge of seismic theory is not required; this course focuses on understanding and practice.
Concepts and algorithm description will be general, but workflows will be illustrated through application to the Barnett Shale, Woodford Shale, and Mississippi Lime resource plays.