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Machine learning is changing the way interpretation is done. Find out how geoscientists are using machine learning to reveal unprecedented levels of detail in seismic data

Unsupervised Machine Learning Techniques for Subtle Thumbnail

Technical Paper | 23 July 2020 | By Marwa Hussein, Robert R. Stewart and Jonny Wu

Unsupervised Machine Learning Techniques for Subtle Fault Detection

Authors investigate the different geometric attributes that are sensitive to small faults by using a dataset from Maui field in Offshore Taranaki Basin, New Zealand. In this paper, we will investigate the different geometric attributes that are sensitive to small faults by using a dataset from Maui field in Offshore Taranaki Basin, New Zealand.

Machine learning for detailed reservoir — Wisting case study Featured

Technical Paper | 1 July 2020 | By Sharareh Manouchehri, Nam Pham, Terje A. Hellem and Rocky Roden

A multi-disciplinary approach to establish a workflow for the application of machine learning for detailed reservoir description – Wisting case study

This study is part of a multidisciplinary activity performed by Idemitsu Petroleum Norge AS (IPN) aiming at establishing a full-field geological model of the Wisting discovery as input to the production planning and development.

Systematic Workflow for Reservoir Characterization in Northwestern Colombia using Multi-attribute Classification

Technical Paper | 24 March 2020 | By Deborah Sacrey and Camilo Sierra

Systematic Workflow for Reservoir Characterization in Northwestern Colombia using Multi-attribute Classification

In this study, the SOM results were calibrated to wells that have been drilled to date. Geobodies from these winning neurons, which tied to productive intervals registered in these wells, were then visualized for their thicknesses and areal extents within the reservoir field.

Net Reservoir Discrimination through Multi-Attribute Analysis at Single Sample Scale

Technical Paper | 12 September 2019 | By Jonathan Leal, Rafael Jerónimo, Fabian Rada, Reinaldo Viloria and Rocky Roden

Net Reservoir Discrimination through Multi-Attribute Analysis at Single Sample Scale

The authors generated petrophysical labels to apply statistical validation techniques between well logs and SOM results. Based on the application of PCA to a larger set of attributes, a smaller, distilled set of attributes were classified using the SOM process to identify lithological changes in the reservoir

Machine Learning Applied to 3D Seismic Data from the Denver-Julesburg Basin Improves Stratigraphic Resolution in the Niobrara

Technical Paper | 25 July 2019 | By Carolan Laudon, Sarah Stanley, Patricia Santogrossi

Machine Learning Applied to 3D Seismic Data from the Denver-Julesburg Basin Improves Stratigraphic Resolution in the Niobrara

The purpose of the study was to evaluate the effectiveness of a machine learning workflow to improve resolution within the reservoir intervals of the Niobrara and Codell formations, the primary targets for development in this portion of the basin.

Applications of Machine Learning for Geoscientists – Permian Basin

Technical Paper | 15 May 2019 | By Carrie Laudon

Applications of Machine Learning for Geoscientists – Permian Basin

This paper focuses on an unsupervised machine learning workflow utilizing Self-Organizing Maps (Kohonen, 2001) in combination with Principal Component Analysis to produce classified seismic volumes from multiple instantaneous attribute volumes.

Image of Seismic amplitude co-rendered Lines A and B

Technical Paper | 27 December 2018 | By Tao Zhao

A Fault Detection Workflow Using Deep Learning and Image Processing

Based on the traditional coherence attribute, Qi et al. (2017) introduced an image processing-based workflow to skeletonize faults. In this study, Dr. Zhao and his team regularize the raw output from a CNN fault detector with an image processing workflow built on Qi et al. (2017) to improve the fault images.


Technical Paper | 15 October 2018 | By Tao Zhao

Seismic Facies Classification Using Deep Convolutional Neural Networks

Dr. Zhao introduce an encoder-decoder CNN model for seismic facies classification, which classifies all samples in a seismic line simultaneously and provides superior seismic facies quality comparing to the traditional patch-based CNN methods.

Solving Exploration Problems with Machine Learning Figure 7

Technical Paper | 1 June 2018 | By Deborah Sacrey and Rocky Roden

Solving Exploration Problems with Machine Learning

Authors demonstrate how the use of machine learning with multi-attribute classification applied to seismic samples instead of wavelets can solve some of the most difficult problems geoscience interpreters face today.

wiggle trace seismic data

Technical Paper | 3 September 2017 | By Rocky Roden and Patricia Santogrossi

Significant Advancements in Seismic Reservoir Characterization with Machine Learning

The application of machine learning to classify seismic attributes at single sample resolution is producing results that reveal more reservoir characterization information than is available from traditional interpretation methods.

Geobody Interpretation Figure 1

Technical Paper | 5 June 2017 | By Tom Smith

Geobody Interpretation Through Multi-Attribute Surveys, Natural Clusters and Machine Learning

This paper sets out a unified mathematical framework for the process from seismic samples to geobodies. SOM is discussed
in the context of inversion as a dimensionality reducing classifier to deliver a winning neuron set.

Conventional Stacked Seismic Amplitude Display

Technical Paper | 15 May 2017 | By Rocky Roden and ChingWen Chen

Interpretation of DHI Characteristics with Machine Learning

Authors incorporate a machine learning workflow where principal component analysis (PCA) and self-organizing maps (SOM) analyse combinations of seismic attributes for meaningful patterns that correspond to direct hydrocarbon indicators.

Seismic Interpretation Below Tuning with Multiattribute Analysis figure 10

Technical Paper | 12 April 2017 | By Rocky Roden, Tom Smith, Patricia Santogrossi, Deborah Sacrey and Gary Jones

Seismic Interpretation Below Tuning with Multi-attribute Analysis

This thin-bed analysis utilizing self-organizing maps has been corroborated with extensive well control to verify consistent results. Therefore, thin beds identified with this methodology enable more accurate mapping of facies below tuning and are not restricted by traditional frequency limitations.

SOM results defining hydrocarbon contacts

Technical Paper | 16 December 2016 | By Rocky Roden and Deborah Sacrey

Seismic Interpretation with Machine Learning

Machine learning resolves two significant issues in seismic interpretation: firstly, the ‘Big Data’ problem of trying to interpret dozens, if not hundreds, of volumes of data and secondly, the fact that humans cannot understand the relationship of more than three types of data all at once.

Som Results B.2

Technical Paper | 12 November 2015 | By Rocky Roden, Tom Smith and Deborah Sacrey

Geologic Pattern Recognition from Seismic Attributes: Principal Component Analysis and Self-Organizing Maps

Recent work using SOM and PCA has revealed geologic features that were not previously identified or easily interpreted from the seismic data. The ultimate goal in this multiattribute analysis is to enable the geoscientist to produce a more accurate interpretation and reduce exploration and development risk.

BEG Line 53 Full Classification - Copy

Technical Paper | 5 November 2015 | By Tom Smith and Sven Treitel

Self-Organizing Neural Nets for Automatic Anomaly Identification

Self-organizing maps are a practical way to identify natural clusters in multi-attribute seismic data. Curvature measure identifies neurons that have found natural clusters from those that have not. Harvesting is a methodology for measuring consistency and delivering the most consistent classification.

2D Colormaps

Technical Paper | 13 May 2015 | By Rocky Roden

Distillation of Seismic Attributes to Geologic Significance

The generation of seismic attributes has enabled geoscientists to better understand certain geologic features in their seismic data. Seismic attributes are a measurable property of seismic data, such as amplitude, dip, frequency, phase and polarity.

Well locations for the DHI Consortium

Technical Paper | 7 September 2012 | By Rocky Roden, Mike Forrest and Roger Holeywell

Relating Seismic Interpretation to Reserve / Resource Calculations

In the process of quantifying resources/reserves, geoscientists attempt to employ all the available pertinent information to produce the most accurate results. The presence of direct hydrocarbon indicators (DHI) on seismic data can have a significant impact on the reserve/resource calculations not only for volumes, but also uncertainty levels.

Time slice

Technical Paper | 25 January 2011 | By Tom Smith and Sven Treitel

Introduction to Self-Organizing Maps in Multi-Attribute Seismic Data

Computerized information management has become an indispensable tool for organizing and presenting geophysical and geological data for seismic interpretation. Data bases provide the underlying environment to achieve this goal. Machine learning is another area in which computers may one day offer an indispensable tool as well.

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