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.
A case study of 10 square mile Eagle Ford Shale Trend utilizing machine learning in Paradise to apply inversion and conventional attributes. Applying multiple attributes to emphasize sweet spots in unconventional resources by understanding reservoir geology, geochemistry, geomechanics, faults and fractures.
Exploring shallow Yegua formation as an independent method to accurately identify anomalies and exposing direct hydrocarbon indicators using Self-Organizing Map (SOM) analysis to enhance conventional seismic interpretation to reveal anomalies.