Geobodies in Paradise: a Machine Learning Application

Geobodies in Paradise: a Machine Learning Application

Dr. Tom Smith presents “Geobodies in Paradise: a Machine Learning Application” at the 2018 SEG Convention in Anaheim, California. Dr. Smith explains four geobody examples including Golden 3D Survey Geobodies, Eagle Ford Sweet Spot Predictions, Niobrara Sweet Spot Predictions and Stratton Field Strategraphic Fabric by Geobody Shape Classification.

Machine Learning Revolutionizing Seismic Interpretation

Machine Learning Revolutionizing Seismic Interpretation

The science of petroleum geophysics is changing, driven by the nature of the technical and business demands facing geoscientists as oil and gas activity pivots toward a new phase of unconventional reservoir development in an economic environment that rewards efficiency and risk mitigation.

Seismic Interpretation with Machine Learning

Seismic Interpretation with Machine Learning

Today’s seismic interpreters must deal with enormous amounts of information, or ‘Big Data’, including seismic gathers, regional 3D surveys with numerous processing versions, large populations of wells and associated data, and dozens if not hundreds of seismic attributes that routinely produce terabytes of data. Machine learning has evolved to handle Big Data. This incorporates the use of computer algorithms that iteratively learn from the data and independently adapt to produce reliable, repeatable results. Multi-attribute analyses employing principal component analysis (PCA) and self-organizing maps are components of a machine-learning interpretation workflow (Figure 1) that involves the selection of appropriate seismic attributes and the application of these attributes in an unsupervised neural network analysis, also known as a self-organizing map, or SOM.

Approach Aids Multiattribute Analysis

Approach Aids Multiattribute Analysis

How Self-Orgazining Maps (SOM) and Principal Componenrt Analysis (PCA) greatly enhances the interpretation process to identify geology in diffferent settings. Geophysicists interpret multiple attributes of seismic data using principal component analysis and self-organizing maps of machine learning.

Distillation of Seismic Attributes to Geologic Significance

Distillation of Seismic Attributes to Geologic Significance

Seismic attributes identify many geologic features in seismic data where PCA helps identify optimal attributes and help determine which attributes to use in a multi-attribute analysis using SOM. The process in Paradise reveals natural clustering by pattern recognition in the data helping define aspects like stratigraphy, seismic facies, DHI features and sweet spots for shale.

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