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Quantitative Seismic Interpretation (GPH47)

    Description

    In addition to structural and stratigraphic interpretation, Quantitative Interpretation of seismic data can add considerable value in the subsurface evaluation process. For exploration prospects, various seismic attributes can be used to differentiate between alternative porefill scenario's and improve the decision making. In field development settings, where more well calibration is normally available, tools like seismic inversion can extrapolate and predict key reservoir properties outside well control and thus improve the reliability of a reservoir model. The QI workflow necessitates consistent integration of the available geological, geophysical, petrophysical data, yielding also team integration and data QC as an important spin-off.

    Course Level: Foundation / Skill
    Duration: 5 days
    Instructor: Jaap Mondt

    Designed for you, if you are...

    Geologists, geophysicists and petroleum engineers involved in the processing and interpretation of 2D and 3D seismic data.

    How we build your confidence

    The course uses a mixture of lectures, practical exercises and case histories.

    The benefits from attending

    At the end of the course participants will have a solid foundation in Quantitative Interpretation concepts, workflows and data requirements. They will be able to model the seismic response of alternative geological scenario's, judge the feasibility of a QI project for a dataset in question and be able to avoid the most common pitfalls. And they can better liaise and collaborate with staff in neighbouring Geoscience (sub)disciplines. Whereas the course does not cover training in the hands-on use of common industry software tools for QI or inversion, it will provide the necessary foundation required for subsequent use of such tools in a professional manner.

    Topics

    • Seismic fundamentals for QI
    • Rock and fluid physics
    • Data preparation/conditioning
    • Log QC, seismic-to-well calibration and wavelet extraction
    • Statistics for QI
    • Forward modeling
    • AvO
    • Acoustic, elastic and lithology inversion
    • Seismically constrained reservoir modeling
    • Acquisition and processing requirements for QI
    • QI of time-lapse (4D) data (optional)
    • Pressure Prediction from seismic data (optional)


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