May 4th-Rock Physics SIG: Statistical Rock Physics in Seismic Reservoir Characterization*

May 4th-Rock Physics SIG: Statistical Rock Physics in Seismic Reservoir Characterization
Sponsored by CGG and Ikon Science
Event Location:
10300 Town Park Dr.
Houston, TX  77072

NOTE: You Must be Logged in to Register

5:15pm Refreshments
5:30pm Presentation Begins
6:30pm Adjourn

Speaker: Dr. Sagar Ronghe, DownUnder GeoSolutions

This presentation will demonstrate the concepts and practical application of statistical rock physics in seismic reservoir characterization using data examples from the Carnarvon Basin, offshore Western Australia.

Quantitative, calibrated reservoir characterization integrates wireline and seismic data. Apart from the obvious resolution differences a number of issues affect the integration: wells may be few in number and preferentially located so that formation sampling is biased. Particular lithologies / fluids may only be intersected over certain narrow depth ranges. Recorded data by itself provides little understanding of the rock vertically and laterally away from the logged intervals. Wireline data, used in deterministic interpretation, provide a single solution without estimates of associated uncertainty. Under-sampled formations mean that the recorded logs are not representative of the population behavior of the particular lithology or fluid type under consideration.

Statistical rock physics addresses these issues and forms the cornerstone for the integration of well and seismic data in reservoir characterization. The workflow is illustrated with reference to well data from offshore Western Australia. Statistical rock physics comprises selecting and upscaling log intervals associated with particular lithologies, establishing depth dependent rock physics trends per lithology type, and stochastic modelling of the trends to generate probability density functions representative of the population behavior of key lithology and fluid combinations as a function of end member rock types, fluid content, reservoir quality and depth.

Seismic reservoir characterization needs to be fit for purpose, given project objectives and data availability. Reservoir characterization methodologies span qualitative attribute analysis, deterministic inversion, probabilistic lithology predictions and stochastic inversion. Statistical rock physics provides the calibration between formation properties and the seismic response for all reservoir characterization techniques. Statistical rock physics models formation behavior in terms of both absolute rock property variation and amplitude variation with angle (AVA).  This presentation will show, using multiple data sets and results from published case studies, how statistical rock physics can be used to characterize expected seismic responses, ascertain whether lithologies of interest can be discriminated from a rock physics standpoint, provide AVA constraints to guide seismic inversion, derive probabilistic interpretations of lithology and fluid distributions from seismic attributes / inversion outputs, and quantify their petrophysical properties along with associated prediction uncertainties.

Biography:  Dr. Sagar Ronghe
Dr Sagar Ronghe specializes in Quantitative Interpretation integrating wireline and seismic data. He has 20 years of  experience in seismic reservoir characterization.  Sagar is a Geoscience Manager for Quantitative Interpretation with DownUnder GeoSolutions, based in Perth, Australia. His educational qualifications comprise a Bachelors in Geological Oceanography from the University College of North Wales, UK, a Masters Distinction in Petroleum Geology and a PhD in Geophysics, both from the University of Aberdeen, UK. He began his career in 1996 as a Lecturer in Geophysics at the University Brunei Darussalam. In 2002, he joined Fugro Jason as a Project Geoscientist based in Kuala Lumpur, relocated to Perth in 2007 and was appointed Regional Technical Manager for Fugro Jason in 2012.  Sagar has been with DownUnder GeoSolutions since 2013.


5/4/2016 5:15 PM - 5/4/2016 6:30 PM

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