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SWLGS Luncheon Topics

Updated January, 2004

Lily Boom Prospect, Ramos Field: It's Good to Have a d Plan B
LGS Continued Education
Progressive Seismic Data Mining For Reservoir Characteristics
Seismic Interpretation of Sonic Logs
Rejuvenation Using Unique Frequency Enhancement Technology
Turning Rays and Anisotropy in Prestack Time Migration
Prestack Depth Imaging in the Eastern GOM
Imaging of a Salt Face and Truncating Updip Sands...
3D Seismic Recognition Of The Jurassic Smackover Reservoir
Imaging Through Gas Clouds
The Role of Visualization in Resource Expl. & Dev.
AVO Analysis in the Middle Miocene, Central GOM
Liuhua 11-1 Field, South China Sea
Turtle Bayou - Development History
Geologic Overview of NE Miss. Fan & Delta
DeepWater AUV Experiences
Workstation Visualization Techniques
Improved AVO Crossplot Evaluation
Baldpate Field (GB 260)
4th Wave Imaging
The Seismic Well Log
Subsalt Exploration in the Deepwater Foldbelts, GOM
Seismic Attributes Past, Present, and Future

April, 2003

Progressive Seismic Data Mining For Reservoir Characterization
Strecker, U., Taylor, G., Smith, M., and Pou, M.
Rock Solid Images, 2600 S. Gessner, Suite 650, Houston TX 77063 USA


Abstract Summary: Seismic interpreters are required to work with larger and larger seismic volumes as the amount of seismic data we acquire and process continues to increase. Rapid advances in seismic attribute methods further increase our data-set sizes by providing many coincident seismic attribute volumes for each data set.

These exponential increases in available data represent huge data management and data interpretation challenges to our industry. There are clear similarities between the seismic exploration industry and the Internet in terms of the volume of information that is available for analysis, and therefore it makes sense to deploy data mining tools and methodologies developed for other industries to address the needs of the oil and gas exploration business. We employ some aspects of the data mining workflow in two case studies (South Africa, South Louisiana) to enrich and discover knowledge about productive regions within 3D seismic data volumes.

Seismic data mining is applied to multiple seismic attribute volumes calculated from a 3D dataset acquired over the Ibhubesi Field in the Orange River Basin, RSA. Large amplitude anomalies are present on the full-stack data, also discernible when transitioning from near to mid angle sub-stacks. Most wells exhibiting a Class III AVO anomaly tested productive, however, the dilemma of just using the seismic amplitude response as a fluid discriminator is that the sand in the well bore associated with the largest Class III AVO anomaly tested wet.

Integration of well data driven synthetic seismic data is critical to finding attributes to constrain the Class III AVO response. The analysis of the well data indicated the importance of Poisson’s Ratio for discriminating pay from wet sands. Attributes derived from band-limited inversion of the seismic sub-stacks retain the discrimination observed in the well data. Additionally, other seismic attributes that discriminate facies and fluid types, can utilize a further data mining technique employing neural network technology to generate a single attribute volume of the multi-attribute response.

The results of this study demonstrate the value of applying data mining techniques to seismic data volumes to rapidly seek prospective zones using some well calibration, thereby mitigating future drilling risk.

A second case study from South Louisiana demonstrates the use of various attributes in seismic data reduction for the rapid delineation of a possibly prospective, faulted subsurface channel system and the seismic properties of its constituting fill.

Biographical Sketch: Uwe Strecker graduated from Georg-August-Universität Göttingen, Germany, in 1987. In the following year he received a Fulbright travel grant to attend the geosciences department at Cornell University, Ithaca, New York. Dr. Strecker received a Ph.D in Geology from the University Of Wyoming under Professor James Richard Steidtmann in 1996. In the energy industry, Uwe worked in a corporate acquisitions and strategic transactions group (Union Pacific Resources) and on exploration/exploitation teams in the Mid-continent and Gulf Onshore of the United States (Union Pacific Resources; Belco Energy Corporation). His academic and industry research interests have included unconventional gas (coal-bed methane), fission-track thermochronology, and seismic facies interpretation. Uwe is currently Manager of Training at Rock Solid Images.

Rock Solid Images was founded in 1998 to conduct seismic reservoir characterization services. In short, our approach involves using rigorous rock physics modeling (MOSS), seismic forward modeling, multiple seismic attributes (e.g. AI, EI, AVO, Hilbert, Q) and artificial neural networks (Data Mining) to predict rock properties from seismic data. Our philosophy is to use rock physics/seismic modeling to dictate which attributes to use for discrimination of the various rock and fluid properties of interest in the reservoir. More information can be found on our website.

www.rocksolidimages.com