The tools of machine learning, petrophysics, well logs, and bi-variate statistics are applied in an integrated methodology to identify and discriminate reservoirs with hydrocarbon storage capacity. While the use of any one of these methods is familiar, their application together is unique.
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Deborah Sacrey, owner of Auburn Energy, presented at the the AAPG Deep Learning TIG for a free lunch and learn on Wednesday, 5 May 2021 at 12–1 p.m. (CT). Deborah Sacrey Owner - Auburn Energy Deborah is a geologist/geophysicist with 44 years of oil and gas exploration experience in Texas, Louisiana Gulf Coast and Mid-Continent …
Machine Learning – New Discoveries & Reservoir Optimization Read More »
InformationBioInformation GSH-SEG Spring Symposium-Data Science and Geophysics: How Machine Learning and AI will Change Our Industry - Apr 27-28 The theme of Geophysical Society of Houston’s premier technical event was the application of the latest data science tools to solving geophysical problems. The virtual symposium was held over two days and consisted of a keynote …
The Scientific Universe From Square One to SOM – April 27-28 2021 Read More »
This webinar features a 45-minute presentation by Dr. Bob Hardage (CV below), a researcher and proponent of the application of Direct-P and Converted-P data since its inception in 2011. The technique is receiving renewed interest with the use of machine learning. An interactive Q&A with Dr. Hardage will follow his presentation. AbstractQ&A from WebinarAbstract Title: …
In the past, SOM has been used on only one attribute at a time using the seismic wavelet as the basis for the neural analysis. The approach in this presentation is using SOM on multiple seismic attributes at one time, and in a sample-based, not wavelet, format.
A multidisciplinary approach that is maximizing information extraction from seismic to predict lithofacies and reservoir properties, based on the following steps is presented:
Multi-attribute seismic analysis was applied based on an unsupervised machine learning process called Self- Organizing Maps (SOMs) in Paradise software. The selection of input attributes was thoroughly tested and optimized, based on close co-operation between geophysicists and geologists to extract more extensive and detailed geological features from seismic.
Application of the unsupervised Machine learning using SOM clearly demonstrate the strike and geomorphology of the Pliocene marine turbidities. The southern segment of the channel penetrated with the three wells are very well defined after posting the wells. No significant difference on the neurons (hexagons) at the locations of the three wells which reflects the similarity of reservoir nature, thickness, sand content and pay thickness. A significant other channel is resolved to the east of the main channel that is not detected using the conventional spectral decomposition.
Examination of vertical seismic profile (VSP) data with unsupervised machine learning technology is a rigorous way to compare the fabric of down-going, illuminating, P and S wavefields with the fabric of up-going reflections and interbed multiples created by these wavefields.
The key to this presentation is showing examples of how the SOM classification process has led to hydrocarbon discoveries in different types of depositional environments. Examples of cases in which the decision was made not to drill a well, thus avoiding a potential dry hole, will also be shown.
By using statistical tools such as Attribute Selection, which uses Principal Component Analysis (PCA), and Multi-Attribute Classification using Self Organizing Maps (SOM), a multi-attribute 3D seismic volume can be “classified.” PCA reduces a large set of seismic attributes to those that are the most meaningful. The output of the PCA serves as the input to the SOM, a form of unsupervised neural network, which when combined with a 2D color map facilitates the identification of clustering within the data volume.