Download the Latest Technical Papers
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.
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Technical Paper | June 2022 | By Carolan Laudon, Jie Qi, Yin-Kai Wang, Geophysical Insights, and University of Houston
Applying Machine Learning Technologies in the Niobrara Formation, DJ Basin, to Quickly Produce an Integrated Structural and Stratigraphic Seismic Classification Volume Calibrated to Wells
This study will demonstrate an automated machine learning approach for fault detection in a 3D seismic volume. The result combines Deep Learning Convolution Neural Networks (CNN) with a conventional data pre-processing step and an image processing-based post processing approach to produce high quality fault attribute volumes of fault probability, fault dip magnitude and fault dip azimuth. These volumes are then combined with instantaneous attributes in an unsupervised machine learning classification, allowing the isolation of both structural and stratigraphic features into a single 3D volume. The workflow is illustrated on a 3D seismic volume from the Denver Julesburg Basin and a statistical analysis is used to calibrate results to well data.
Technical Paper | October 2021 | By Carolan Laudon, Jie Qi, Aldrin Rondon, Lamia Rouis and Hana Kabazi
An enhanced fault detection workflow combining machine learning and seismic attributes yields an improved fault model for Caspian Sea asset
Through theory and results, the paper will demonstrate that a combination of ML tools generates superior results versus a single method. The ML technologies applied are a supervised, deep learning, fault classification followed by an unsupervised, multi-attribute classification combining fault probability and instantaneous attributes.
Technical Paper | August 2021 | By Rocky Roden, Carrie Laudon and Jie Qi
Advanced Trends in Machine Learning for Seismic Fault Delineation
The two primary machine learning applications include supervised and unsupervised learning approaches. Supervised learning takes a known set of descriptors or labels to known responses and trains a model, which is applied to new data to get reasonable results. In unsupervised learning, there is no prior knowledge of the data, and training adapts to the data identifying natural patterns and clusters. This article specifically addresses the application of machine learning to seismic fault delineation.
Technical Paper | June 2021 | By Tao Zhao and Pradip Mukhopadhyay
A Fault Detection Workflow Using Deep Learning and Image Processing
Within the last a couple of years, deep learning techniques, represented by convolutional neural networks (CNNs), have been applied to fault detection problems on seismic data with impressive outcome. As is true for all supervised learning techniques, the performance of a CNN fault detector highly depends on the training data, and post-classification regularization may greatly improve the result.
Technical Paper | December 2020 | By Marwa Hussein, Robert R. Stewart and Jonny Wu
Unsupervised Machine Learning Techniques for Subtle Fault Detection
In this paper, the 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. They suggest a workflow that enables interpreters to apply principal component analysis (PCA) and self- organizing maps (SOM) on the most appropriate mathematically independent seismic attributes to get one classification volume. The authors’ aim is to obtain one clustered volume that best shows all small faults affecting the area.
Technical Paper | October 2020 | By Jie Qi, Bin Lyu, Xinming Wu, and Kurt Marfurt
Comparing Convolutional Neural Networking and Image Processing Seismic Fault Detection Methods
In this paper, the authors build a Convolutional Neural Network (CNN) architecture to predict faults from 3D seismic data, and then compare the results to those obtained using an image processing-based fault detection for datasets exhibiting different data quality.
Technical Paper | 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 paper presents a multidisciplinary approach, maximizing information extraction from seismic data to predict lithofacies and reservoir properties. The study focused on the benefits and additional information that can be gained with this new approach compared to traditional quantitative interpretation, i.e., a prediction from acoustic impedance.
Technical Paper | March 2020 | By Deborah Sacrey and Camilo Sierra
Systematic Workflow for Reservoir Characterization in Northwestern Colombia using Multi-attribute Classification
A workflow is presented which includes data conditioning, finding the best combination of attributes for ML classification aided by Principal Component Analysis, unsupervised ML through SOM multi-attribute seismic sample training and then survey classification in the zone of interest and, finally, geobodies created from classified samples of selected winning neurons, Vizualization of these results are outlined in this paper. The result are potential reservoir estimates calculated through geobodies which have been interpreted with unsupervised ML classifications.
Technical Paper | 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
In this paper, 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.
Technical Paper | October 2018 | By Tao Zhao
Seismic Facies Classification Using Deep Convolutional Neural Networks
In this study, the author uses an encoder-decoder CNN model as an implementation of the aforementioned second approach. The author applies both the encoder-decoder model and patch-based model to seismic facies classification using data from the North Sea, with the objective of demonstrating the strengths and weaknesses of the two CNN models. The author concludes that the encoder-decoder model provides much better classification quality, whereas the patch-based model is more flexible on training data, possibly making it easier to use in production.
Technical Paper | May 2017 | By Rocky Roden and ChingWen Chen, Ph.D.
Interpretation of DHI Characteristics with Machine Learning
In this paper, the authors incorporate a machine learning workflow where principal component analysis (PCA) and self-organizing maps (SOM) are employed to analyze combinations of seismic attributes for meaningful patterns that correspond to direct hydrocarbon indicators. A machine learning multi-attribute approach with the proper input parameters can help interpreters to more efficiently and accurately evaluate DHIs and help reduce risk in prospects and projects.
Technical Paper | April 2017 | By Rocky Roden, Thomas A. Smith, Patricia Santogrossi, Deborah Sacrey, and Gary Jones
Seismic Interpretation Below Tuning with Multi-attribute Analysis
In this paper, the authors present a seismic multiattribute approach that employs self-organizing maps to identify natural clusters from combinations of attributes that exhibit below-tuning effects. These results may exhibit changes as thin as a single sample interval in thickness. Self-organizing maps employed in this fashion analyze associated seismic attributes on a sample-by-sample basis and identify the natural patterns or clusters produced by thin beds.
Technical Paper | November 2015 | By Rocky Roden, Thomas A. 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 multi-attribute analysis is to enable the geoscientist to produce a more accurate interpretation and reduce exploration and development risk.