Cover Image

Automated Geological Features Detection in 3D Seismic Data Using Semi-Supervised Learning

A geological interpretation plays an important role to gain information about the structural and stratigraphic of hydrocarbon reservoirs. However, this is a time-consuming task due to the com-plexity and size of seismic data. We propose a semi-supervised learning technique to automatically and accur...

Full description

Saved in:
Bibliographic Details
Main Authors: Pratama, H., Latiff, A.H.A.
Format: Article
Published: MDPI 2022
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85133677820&doi=10.3390%2fapp12136723&partnerID=40&md5=900b9ff5da770a934397bf14cdf6bfc5
http://eprints.utp.edu.my/33360/
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.utp.eprints.33360
record_format eprints
spelling my.utp.eprints.333602022-07-26T08:19:51Z Automated Geological Features Detection in 3D Seismic Data Using Semi-Supervised Learning Pratama, H. Latiff, A.H.A. A geological interpretation plays an important role to gain information about the structural and stratigraphic of hydrocarbon reservoirs. However, this is a time-consuming task due to the com-plexity and size of seismic data. We propose a semi-supervised learning technique to automatically and accurately delineate the geological features from 3D seismic data. To generate labeling data for training the supervised Convolutional Neural Network (CNN) model, we propose an efficient workflow based on unsupervised learning. This workflow utilized seismic attributes and KernelPCA to enhance the visualization of geological targets and clustering the features into binary classes using K-means approach. With this workflow, we are able to develop a data-driven model and reduce human subjectivity. We applied this technique in two cases with different geological settings. The synthetic data and the real seismic investigation from the A Field in the Malay Basin. From this application, we demonstrate that our CNN-based model is highly accurate and consistent with the previous manual interpretation in both cases. In addition to qualitatively evaluating the interpreta-tions, we further extract the predicted result into a 3D geobody. This result could help the interpreter focus on tasks requiring human expertise and aid the model�s prediction in the next studies. © 2022 by the authors. Licensee MDPI, Basel, Switzerland. MDPI 2022 Article NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85133677820&doi=10.3390%2fapp12136723&partnerID=40&md5=900b9ff5da770a934397bf14cdf6bfc5 Pratama, H. and Latiff, A.H.A. (2022) Automated Geological Features Detection in 3D Seismic Data Using Semi-Supervised Learning. Applied Sciences (Switzerland), 12 (13). http://eprints.utp.edu.my/33360/
institution Universiti Teknologi Petronas
building UTP Resource Centre
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Petronas
content_source UTP Institutional Repository
url_provider http://eprints.utp.edu.my/
description A geological interpretation plays an important role to gain information about the structural and stratigraphic of hydrocarbon reservoirs. However, this is a time-consuming task due to the com-plexity and size of seismic data. We propose a semi-supervised learning technique to automatically and accurately delineate the geological features from 3D seismic data. To generate labeling data for training the supervised Convolutional Neural Network (CNN) model, we propose an efficient workflow based on unsupervised learning. This workflow utilized seismic attributes and KernelPCA to enhance the visualization of geological targets and clustering the features into binary classes using K-means approach. With this workflow, we are able to develop a data-driven model and reduce human subjectivity. We applied this technique in two cases with different geological settings. The synthetic data and the real seismic investigation from the A Field in the Malay Basin. From this application, we demonstrate that our CNN-based model is highly accurate and consistent with the previous manual interpretation in both cases. In addition to qualitatively evaluating the interpreta-tions, we further extract the predicted result into a 3D geobody. This result could help the interpreter focus on tasks requiring human expertise and aid the model�s prediction in the next studies. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
format Article
author Pratama, H.
Latiff, A.H.A.
spellingShingle Pratama, H.
Latiff, A.H.A.
Automated Geological Features Detection in 3D Seismic Data Using Semi-Supervised Learning
author_facet Pratama, H.
Latiff, A.H.A.
author_sort Pratama, H.
title Automated Geological Features Detection in 3D Seismic Data Using Semi-Supervised Learning
title_short Automated Geological Features Detection in 3D Seismic Data Using Semi-Supervised Learning
title_full Automated Geological Features Detection in 3D Seismic Data Using Semi-Supervised Learning
title_fullStr Automated Geological Features Detection in 3D Seismic Data Using Semi-Supervised Learning
title_full_unstemmed Automated Geological Features Detection in 3D Seismic Data Using Semi-Supervised Learning
title_sort automated geological features detection in 3d seismic data using semi-supervised learning
publisher MDPI
publishDate 2022
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85133677820&doi=10.3390%2fapp12136723&partnerID=40&md5=900b9ff5da770a934397bf14cdf6bfc5
http://eprints.utp.edu.my/33360/
_version_ 1739833211381350400
score 13.211869

Similar Items