Case Study

Using machine learning to classify a 100-square-mile seismic volume in the Niobrara, geoscientists were able to interpret thin beds below seismic tuning and identify anomalies at resolutions not possible with traditional interpretation.

Utilizing machine learning in Paradise to define and reveal features not seen in conventional interpretation in an offshore Gulf of Mexico oil/gas field. The SOM analyses using DHI characteristics and seismic attributes to reveal hydrocarbon contacts, amplify attenuation features and define ampliltude conformance in a Class 3 AVO.

Latest Technology for Seismic Interpretation: Direct detection & delineation of facies architecture in the Eagle Ford Group or How did the Eagle Ford GP get Made? A presentation by Patricia Santogrossi at the 2016 SEG Annual Convention.

Using multiple attributes to evaluate a 3D volume in offshore South America containing unexpected high pressure zone and the application of seismic attributes in a SOM to help define seismic facies and isolate the pressure zone.

Part 1 of a 2-part Paradise Application Brief series demonstrating better well planning, identifying more productive perforation intervals and aiding in the retention of critical leases while identifying good productivity areas and expose structure (karst), stratigraphy and rock properties (flat spots) in carbonates in the Viola via machine learning.

Part 2 of a 2-part Paradise Application Brief series applying multiple seismic attributes to enable easy high-grading of leaseholds, asses efficacy of perforations and identify under-perforated zones. It also describes revealing anomolies and flat spots in Hunton and Viola using SOM to indicate facies contrasts, diagenetic changes and/or fluid effects.

Using machine learning to analyze 5 instantaneous attributes helped reveal patterns across 5 instantaneous attributes and unique Eagle Ford facies.

Results of a Self-Organizing Map (SOM) of many instantaneous attributes to reveal different types of facies and shale that apply machine learning to improve resolution and reveal facies.

Applying Principal Component Analysis (PCA) and Self-Organizing Map (SOM) process to show faults on the base amplitude seismic survey and faults using similarity attributes showing large variance.

Concurrent analysis of multiple attributes through machine learning to spectral decomposition sub-bands and other geology that apply attributes for stratigraphic and structural resolution.

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