DoodleTrain

Seismic Attributes for Prospect Identification and Reservoir Characterization

Instructor: Kurt Marfurt / Satinder Chopra
Date: Nov. 3 - Nov. 4, 2016
Duration: 2 days
Members (early bird/price): CAD$ 800/1000 (plus GST)
Non-members (early bird/price): CAD$ 900/1100 (plus GST)
Location: TBA
Time: 8:30 am - 4:30 pm

Course Description

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 geo-hazards 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. Pre-stack 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 re-stimulation. 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.

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 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, Montney Shale, and Mississippi Lime resource plays.

Biography

Kurt J. Marfurt joined The University of Oklahoma in 2007 where he serves as the Frank and Henrietta Schultz Professor of Geophysics within the ConocoPhillips School of Geology and Geophysics. Marfurt’s primary research interest is in the development and calibration of new seismic attributes to aid in seismic processing, seismic interpretation, and reservoir characterization. Recent work has focused on applying coherence, spectral decomposition, structure-oriented filtering, and volumetric curvature to mapping fractures and karst with a particular focus on resource plays. Marfurt earned a Ph.D. in applied geophysics at Columbia University’s Henry Krumb School of Mines in New York in 1978 where he also taught as an Assistant Professor for four years. He worked 18 years in a wide range of research projects at Amoco’s Tulsa Research Center after which he joined the University of Houston for 8 years as a Professor of Geophysics and the Director of the Allied Geophysics Lab. He has received SEG best paper (for coherence), SEG best presentation (for seismic modeling) and as a coauthor with Satinder Chopra best SEG poster (for curvature) and best AAPG technical presentation. Marfurt also served as the EAGE/SEG Distinguished Short Course Instructor for 2006 (on seismic attributes). In addition to teaching and research duties at OU, Marfurt leads short courses on attributes for the SEG and AAPG.

Satinder Chopra has 30 years of experience as a geophysicist specializing in processing, reprocessing, special processing and interactive interpretation of seismic data.  He has rich experience in processing various types of data like VSP, well log data, seismic data, etc, as well as excellent communication skills, as evidenced by the several presentations and talks delivered and books, reports, and papers written. He has been the 2010/11 CSEG Distinguished Lecturer, the 2011/12 AAPG/SEG Distinguished Lecturer and the 2014/15 EAGE e-Distinguished Lecturer. His research interests focus on techniques that are aimed at characterization of reservoirs.  He has published 8 books and more than 340 papers and abstracts and likes to make presentations at any beckoning opportunity. His work and presentations have won several awards, the most notable ones being the CSEG Honorary Membership (2014) and Meritorious Service (2005) Awards, 2014 APEGA Frank Spragins Award, the 2010 AAPG George Matson Award and the 2013 AAPG Jules Braunstein Award, SEG Best Poster Award (2007), CSEG Best Luncheon Talk award (2007) and several others. He is a member of SEG, CSEG, CSPG, EAGE, AAPG, CHOA (Canadian Heavy Oil Association), APEGGA (Association of Professional Engineers, Geologists and Geophysicists of Alberta) and TBPG (Texas Board of Professional Geoscientists).