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See the geology in the seismic by classifying or mapping complex geologies with supervised neural networks...
Seismic Facies at Wells Seismic Facies on Horizon Shale Capacity Thinned Fault Likelihood
Identifying sands, silts and shales with The classified seismic volume can now be Shale capacities can be computed by Secondary porosity can be as important as
similar acoustic impedances is challenging. interpreted and extracted from seismic combining individual supervised neural shale capacity or lithology. Thinned-fault
Using supervised neural networks to horizons, with the relative probability for networks for brittleness, TOC and porosity, likelihood, phase congruency, and GLCM
classify the seismic lithologies or facies each facies classified. or by training directly with effective shale azimuthal texture measurements are non-
improves risking and drilling decisions. capacity from well data. This can be done at curvature based techniques to determine
The objective siltstone class is shown on sub-seismic sample rates to obtain higher fracture orientation and density. Pore-
While the acoustic impedance curves in horizon by the orange colors. Areas without resolution measurements, better matching pressure can also be computed through
each well track vary from well to well (blue silt that are limestones are displayed in log-scale observations. neural network techniques as a proxy for
curve), the neural network classification white and gray colors. The clarity obtained fracture density.
systematically determines the silt (orange) through classifying neural networks is Above, brighter colors indicate higher
and shale (blue) through generalization. straightforward. capacity, and several high capacity lenses In this example the normalized shale
can be observed within the Spraberry and capacity horizon is overlain by thin fault
Wolfcamp intervals (between the blue and likelihood. The faulting or fracturing
pink horizons). magnitude is illustrated by brighter colors.
