Stochastic inversion of pre-stack seismic data to improve forecasts of reservoir production

Simple item page
dc.contributor.advisorTorres-VerdĆ­n, Carlosen
dc.contributor.advisorLake, Larry W.en
dc.creatorVarela LondoƱo, Omar Javieren
dc.date.accessioned2011-07-25T21:40:55Zen
dc.date.available2011-07-25T21:40:55Zen
dc.date.issued2003-08en
dc.descriptiontexten
dc.description.abstractReservoir characterization is a significant component of the commercial evaluation and production of hydrocarbon assets. Accurate reservoir characterization reduces uncertainty in both estimation of reserves and forecast of hydrocarbon production. It also provides optimal strategies for well placement and enhanced recovery processes. Despite continued progress, often the practice of reservoir characterization does not make quantitative and direct use of seismic amplitude measurements, especially pre-stack seismic data. This dissertation develops a novel algorithm for the estimation of elastic and petrophysical properties of complex hydrocarbon reservoirs. The algorithm quantitatively integrates 3D pre-stack seismic amplitude measurements, wireline logs, and geological information. A statistical link between petrophysical properties and elastic parameters is established through joint probability density functions that are adjusted to reflect a vertical resolution consistent with both well logs and seismic data. The estimation of inter-well petrophysical properties is performed with a global inversion technique that effectively extrapolates well-log data laterally away from wells while honoring the full gather of 3D pre-stack seismic data and prescribed global histograms. In addition, the inversion algorithm naturally lends itself to an efficient and robust numerical procedure to assess uncertainty of the constructed 3D spatial distributions of petrophysical and elastic properties. Validation and testing of the inversion algorithm is performed on realistic synthetic data sets. These studies indicate that pre-stack seismic data embody significantly more sensitivity than post-stack seismic data to detecting time-lapse reservoir changes and suggest that rock and fluid properties can be reliably estimated from pre-stack seismic data. Limitations to the quantitative use of seismic data arise in cases of thin reservoir units, low-porosity formations (porosity below 15%), low contrasts in fluid densities, and lack of correlation between petrophysical and elastic parameters. Numerical experiments with the novel algorithm show that petrophysical models constructed with the use of prestack seismic data are more accurate than those generated with standard geostatistical techniques provided that a good correlation exists between petrophysical and elastic parameters. Benefits of the developed algorithm for data integration include the reduction of uncertainty in the construction of rock property distributions such as porosity, fluid saturation, and shale volume. Property distributions constructed in this manner can be used to guide the reliable estimation of other important fluid-flow parameters, such as permeability and permeability anisotropy, that could have a substantial impact on dynamic reservoir behavior.
dc.description.departmentPetroleum and Geosystems Engineeringen
dc.format.mediumelectronicen
dc.identifier.urihttp://hdl.handle.net/2152/12515en
dc.language.isoengen
dc.rightsCopyright is held by the author. Presentation of this material on the Libraries' web site by University Libraries, The University of Texas at Austin was made possible under a limited license grant from the author who has retained all copyrights in the works.en
dc.rights.restrictionRestricteden
dc.subjectSeismic waves--Measurementen
dc.subjectInverse problems (Differential equations)en
dc.subjectHydrocarbon reservoirsen
dc.titleStochastic inversion of pre-stack seismic data to improve forecasts of reservoir productionen
thesis.degree.departmentPetroleum and Geosystems Engineeringen
thesis.degree.disciplinePetroleum and Geosystems Engineeringen
thesis.degree.grantorThe University of Texas at Austinen
thesis.degree.levelDoctoralen
thesis.degree.nameDoctor of Philosophyen

Access full-text files

Original bundle

Now showing 1 - 1 of 1
No Thumbnail Available
Name:
varelalondonooj036.pdf
Size:
3.3 MB
Format:
Adobe Portable Document Format
Description:
Restricted to EID users
Download

License bundle

Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.66 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

UT Electronic Theses and Dissertations