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Characterization of complex fluvial-deltaic deposits in Northeast India using Poisson impedance inversion and non-parametric statistical technique.

作者信息

Nagendra Babu M, Ambati Venkatesh, Nair Rajesh R

机构信息

Computational Petroleum Geomechanics Laboratory, Department of Ocean Engineering, Indian Institute of Technology Madras, Chennai, India.

出版信息

Sci Rep. 2022 Oct 8;12(1):16917. doi: 10.1038/s41598-022-21444-5.

DOI:10.1038/s41598-022-21444-5
PMID:36209316
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9547880/
Abstract

Characterizing complex fluvial-deltaic deposits is a challenging task for finding hydrocarbon discoveries. We described a methodology for predicting the hydrocarbon zones from complex well-log and prestack seismic data. In this current study, data analysis involves an integrated framework based on Simultaneous prestack seismic inversion (SPSI), target correlation coefficient analysis (TCCA), Poisson impedance inversion, and non-parametric statistical analysis, and Bayesian classification. First, seismic elastic attributes from prestack seismic data were estimated. They can provide the spatial distribution of petrophysical properties of seismic data. Then target correlation coefficient analysis (TCCA) was estimated roration factor "c" from well-log data. Using the seismic elastic attributes and rotation factor "c", Poisson impedance inversion was performed to predict the Poisson impedance volume. Finally, Bayesian classification integrated the Poisson impedance volume with non-parametric probabilistic density functions (PDFs) to estimate the spatial distribution of lithofacies. Despite complex characteristics in the elastic properties, the current study successfully delineated the complex fluvial-details deposits. These results were verified with conventional findings through numerical analysis.

摘要
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1b73/9547880/e8cc6f045d8c/41598_2022_21444_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1b73/9547880/b25a049f6536/41598_2022_21444_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1b73/9547880/0d21cb125725/41598_2022_21444_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1b73/9547880/058132e5be66/41598_2022_21444_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1b73/9547880/8a131774f003/41598_2022_21444_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1b73/9547880/842f7a69c074/41598_2022_21444_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1b73/9547880/3334deb9f00d/41598_2022_21444_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1b73/9547880/704d443b03f6/41598_2022_21444_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1b73/9547880/761b86964e24/41598_2022_21444_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1b73/9547880/e8cc6f045d8c/41598_2022_21444_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1b73/9547880/b25a049f6536/41598_2022_21444_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1b73/9547880/0d21cb125725/41598_2022_21444_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1b73/9547880/058132e5be66/41598_2022_21444_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1b73/9547880/8a131774f003/41598_2022_21444_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1b73/9547880/842f7a69c074/41598_2022_21444_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1b73/9547880/3334deb9f00d/41598_2022_21444_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1b73/9547880/704d443b03f6/41598_2022_21444_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1b73/9547880/761b86964e24/41598_2022_21444_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1b73/9547880/e8cc6f045d8c/41598_2022_21444_Fig9_HTML.jpg

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