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Entropy QC for Bayesian facies estimations

SEG - Society of Exploration Geophysicists, September, 2019
John Pendrel | Henk Schouten
©2019 SEG

We use the concepts of entropy and information theory to design a confidence measure for Bayesian facies estimations. Bayesian analyses provide the probabilities of occurrence of each constituent facies in a set. The entropy analysis uses all of these to establish a Confidence Index describing the reliability that the most-probable facies is in fact a clear best choice. We apply these ideas to various facies estimates from Gulf of Mexico inversions.

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Minimum number of azimuth sectors for seismic anisotropy estimation

SEG - Society of Exploration Geophysicists, September, 2019
Peter Mesdag | Leonardo Quevedo
©2019 SEG

In this paper we present a practical extension of earlier work on the estimation of anisotropy parameters from isotropic techniques. We will take a closer look at the implications of working with effective elastic parameters in anisotropic (TI) seismic reflection inversion. In particular, for HTI media, the magnitude of the azimuthal Fourier terms is assessed. For many natural rocks the harmonic equations describing effective HTI anisotropy can be simplified, allowing for faster and more cost effective estimation of the magnitude and orientation of the anisotropy. Limits to these approximations in terms of the number of input azimuthal sectors used in the estimations are discussed.

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Seismic-driven pore and fracture pressure prediction, Permian Basin, Martin County, TX

SEG - Society of Exploration Geophysicists, September, 2019
Ahmed Mohamed | Vishnu Pandey | Bertrand Six | Kevin Chesser | Vivek Swami | Kit Clemons (LARIO Oil & Gas)
©2019 SEG

3D seismic data is only geophysical data which has good lateral sampling. Therefore, the use of seismic data to predict elastic properties away from a well is a widely used process. However transforming these elastic properties into geomechanical and other reservoir properties requires integration of different data sets (e.g. wireline logs, core and cuttings) at various scales. The integration of all data sets is essential for de-risking seismic amplitude supported interpretations. This study demonstrates how this approach and methodology was used to help predict important engineering properties in 3D space and away from any well control. The final inverted volumes were used to pick minimum intermediate casing points and, to predict maximum and minimum mud-weights both of which provide significant capital savings in casing design and/or lost bottom hole assemblies.

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Gini 3D High Productivity Acquisition & Imaging – A case study from the Delaware Basin

SEG - Society of Exploration Geophysicists, September, 2019
Anna Leslie | Vincent Durussel | Terence Krishnasamy | Olivier Winter
©2019 SEG

The Gini 3D survey, acquired in the summer of 2018 in the Delaware Basin, provided a test area for which this type of design and acquisition could be tested and compared with traditional operations used onshore US. The test was planned to be operationally efficient & cost effective (in terms of equipment), with the aim of proving that blended acquisition can be as effective as traditional designs in terms of imaging. Operationally the test exceeded expectations and with processing still ongoing, initial results show that the two designs are comparable.

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Least-squares RTM with ocean bottom nodes: potentials and challenges

SEG - Society of Exploration Geophysicists, September, 2019
Yan Liu | Yi Chen | Hongda Ma | Chao Peng (CGG); Gopal Mohapatra | Wisley Martins | Gregory Duncan (Hess) | Steve Checkles (Formerly Hess)
©2019 SEG

Stampede field is a faulted subsalt four-way reservoir in Green Canyon, Gulf of Mexico. Imaging for part of the field has remained challenging due to interference from the complex overburden, which carries large velocity errors and creates non-uniform illumination for the subsalt. Before correcting the velocity error, least-squares reverse time migration (LSRTM) does not produce desirable subsalt image, even when using newly acquired ocean bottom node (OBN) data. With an improved OBN full-waveform inversion (FWI) model, combined with the benefits from the full-azimuth and long-offset coverage of OBN data, LSRTM greatly improves the subsalt image. However, further improving LSRTM is still challenging due to the remaining velocity uncertainty and un-modeled physics, as well as un-attenuated multiples and converted waves.

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