Using simultaneous inversion and neural networks of log and core data to generate impedance, porosity, and TOC volumes, we are able to study the behavior of reservoir qualities laterally and vertically. We conclude that high-grading of assets can be achieved by identifying and ultimately developing areas of coinciding favorable impedance, porosity, and TOC properties. Areas for favorable well stimulation placement and improved hydrocarbon recovery can also be identified. Further, this methodology can serve to illuminate non favorable areas, serving as a risk assessment methodology as a well planning tool.


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