Integrating facies‐based Bayesian inversion and supervised machine learning for petro‐facies characterization in the Snadd Formation of the Goliat Field, south‐western Barents Sea
ABSTRACT Seismic petro‐facies characterization in low net‐to‐gross reservoirs with poor reservoir properties such as the Snadd Formation in the Goliat field requires a multidisciplinary approach. This is especially important when the elastic properties of the desired petro‐facies significantly overl...
Published in: | Geophysical Prospecting |
---|---|
Main Authors: | , , , , |
Other Authors: | |
Format: | Article in Journal/Newspaper |
Language: | English |
Published: |
Wiley
2018
|
Subjects: | |
Online Access: | http://dx.doi.org/10.1111/1365-2478.12654 https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1111%2F1365-2478.12654 https://onlinelibrary.wiley.com/doi/pdf/10.1111/1365-2478.12654 https://onlinelibrary.wiley.com/doi/full-xml/10.1111/1365-2478.12654 |
id |
crwiley:10.1111/1365-2478.12654 |
---|---|
record_format |
openpolar |
spelling |
crwiley:10.1111/1365-2478.12654 2024-06-02T08:04:10+00:00 Integrating facies‐based Bayesian inversion and supervised machine learning for petro‐facies characterization in the Snadd Formation of the Goliat Field, south‐western Barents Sea Yenwongfai, Honoré Mondol, Nazmul Haque Lecomte, Isabelle Faleide, Jan Inge Leutscher, Johan Norges Forskningsråd 2018 http://dx.doi.org/10.1111/1365-2478.12654 https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1111%2F1365-2478.12654 https://onlinelibrary.wiley.com/doi/pdf/10.1111/1365-2478.12654 https://onlinelibrary.wiley.com/doi/full-xml/10.1111/1365-2478.12654 en eng Wiley http://onlinelibrary.wiley.com/termsAndConditions#vor Geophysical Prospecting volume 67, issue 4, page 1020-1039 ISSN 0016-8025 1365-2478 journal-article 2018 crwiley https://doi.org/10.1111/1365-2478.12654 2024-05-03T10:52:13Z ABSTRACT Seismic petro‐facies characterization in low net‐to‐gross reservoirs with poor reservoir properties such as the Snadd Formation in the Goliat field requires a multidisciplinary approach. This is especially important when the elastic properties of the desired petro‐facies significantly overlap. Pore fluid corrected endmember sand and shale depth trends have been used to generate stochastic forward models for different lithology and fluid combinations in order to assess the degree of separation of different petro‐facies. Subsequently, a spectral decomposition and blending of selected frequency volumes reveal some seismic fluvial geomorphological features. We then jointly inverted for impedance and facies within a Bayesian framework using facies‐dependent rock physics depth trends as input. The results from the inversion are then integrated into a supervised machine learning neural network for effective porosity discrimination. Probability density functions derived from stochastic forward modelling of endmember depth trends show a decreasing seismic fluid discrimination with depth. Spectral decomposition and blending of selected frequencies reveal a dominant NNE trend compared to the regional SE–NW pro‐gradational trend, and a local E–W trend potentially related to fault activity at branches of the Troms‐Finnmark Fault Complex. The facies‐based inversion captures the main reservoir facies within the limits of the seismic bandwidth. Meanwhile the effective porosity predictions from the multilayer feed forward neural network are consistent with the inverted facies model, and can be used to qualitatively highlight the cleanest regions within the inverted facies model. A combination of facies‐based inversion and neural network improves the seismic reservoir delineation of the Snadd Formation in the Goliat Field. Article in Journal/Newspaper Barents Sea Finnmark Finnmark Troms Wiley Online Library Barents Sea Geophysical Prospecting 67 4 1020 1039 |
institution |
Open Polar |
collection |
Wiley Online Library |
op_collection_id |
crwiley |
language |
English |
description |
ABSTRACT Seismic petro‐facies characterization in low net‐to‐gross reservoirs with poor reservoir properties such as the Snadd Formation in the Goliat field requires a multidisciplinary approach. This is especially important when the elastic properties of the desired petro‐facies significantly overlap. Pore fluid corrected endmember sand and shale depth trends have been used to generate stochastic forward models for different lithology and fluid combinations in order to assess the degree of separation of different petro‐facies. Subsequently, a spectral decomposition and blending of selected frequency volumes reveal some seismic fluvial geomorphological features. We then jointly inverted for impedance and facies within a Bayesian framework using facies‐dependent rock physics depth trends as input. The results from the inversion are then integrated into a supervised machine learning neural network for effective porosity discrimination. Probability density functions derived from stochastic forward modelling of endmember depth trends show a decreasing seismic fluid discrimination with depth. Spectral decomposition and blending of selected frequencies reveal a dominant NNE trend compared to the regional SE–NW pro‐gradational trend, and a local E–W trend potentially related to fault activity at branches of the Troms‐Finnmark Fault Complex. The facies‐based inversion captures the main reservoir facies within the limits of the seismic bandwidth. Meanwhile the effective porosity predictions from the multilayer feed forward neural network are consistent with the inverted facies model, and can be used to qualitatively highlight the cleanest regions within the inverted facies model. A combination of facies‐based inversion and neural network improves the seismic reservoir delineation of the Snadd Formation in the Goliat Field. |
author2 |
Norges Forskningsråd |
format |
Article in Journal/Newspaper |
author |
Yenwongfai, Honoré Mondol, Nazmul Haque Lecomte, Isabelle Faleide, Jan Inge Leutscher, Johan |
spellingShingle |
Yenwongfai, Honoré Mondol, Nazmul Haque Lecomte, Isabelle Faleide, Jan Inge Leutscher, Johan Integrating facies‐based Bayesian inversion and supervised machine learning for petro‐facies characterization in the Snadd Formation of the Goliat Field, south‐western Barents Sea |
author_facet |
Yenwongfai, Honoré Mondol, Nazmul Haque Lecomte, Isabelle Faleide, Jan Inge Leutscher, Johan |
author_sort |
Yenwongfai, Honoré |
title |
Integrating facies‐based Bayesian inversion and supervised machine learning for petro‐facies characterization in the Snadd Formation of the Goliat Field, south‐western Barents Sea |
title_short |
Integrating facies‐based Bayesian inversion and supervised machine learning for petro‐facies characterization in the Snadd Formation of the Goliat Field, south‐western Barents Sea |
title_full |
Integrating facies‐based Bayesian inversion and supervised machine learning for petro‐facies characterization in the Snadd Formation of the Goliat Field, south‐western Barents Sea |
title_fullStr |
Integrating facies‐based Bayesian inversion and supervised machine learning for petro‐facies characterization in the Snadd Formation of the Goliat Field, south‐western Barents Sea |
title_full_unstemmed |
Integrating facies‐based Bayesian inversion and supervised machine learning for petro‐facies characterization in the Snadd Formation of the Goliat Field, south‐western Barents Sea |
title_sort |
integrating facies‐based bayesian inversion and supervised machine learning for petro‐facies characterization in the snadd formation of the goliat field, south‐western barents sea |
publisher |
Wiley |
publishDate |
2018 |
url |
http://dx.doi.org/10.1111/1365-2478.12654 https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1111%2F1365-2478.12654 https://onlinelibrary.wiley.com/doi/pdf/10.1111/1365-2478.12654 https://onlinelibrary.wiley.com/doi/full-xml/10.1111/1365-2478.12654 |
geographic |
Barents Sea |
geographic_facet |
Barents Sea |
genre |
Barents Sea Finnmark Finnmark Troms |
genre_facet |
Barents Sea Finnmark Finnmark Troms |
op_source |
Geophysical Prospecting volume 67, issue 4, page 1020-1039 ISSN 0016-8025 1365-2478 |
op_rights |
http://onlinelibrary.wiley.com/termsAndConditions#vor |
op_doi |
https://doi.org/10.1111/1365-2478.12654 |
container_title |
Geophysical Prospecting |
container_volume |
67 |
container_issue |
4 |
container_start_page |
1020 |
op_container_end_page |
1039 |
_version_ |
1800748804761190400 |