A Comparative Study of GeoAI, Simultaneous Inversion, and Deep Learning for Quantitative Seismic Reservoir Characterization: A Midland Basin Case Study
Presenter:
Venkatesh Anantharamu Senior Geoscience Advisor
In this webinar, we’ll walk through a cutting‑edge GeoAI‑driven workflow applied in the Midland Basin that enables direct prediction of elastic, reservoir, geomechanical, and compositional properties straight from pre‑stack seismic gathers. You’ll see how a rock physics–guided convolutional neural network (CNN) was designed to jointly estimate these key properties—tackling the long-standing challenge of capturing fine‑scale heterogeneity in laminated mudstones, siltstones, and thin carbonate intervals.
You will also see a head‑to‑head comparison of GeoAI results against traditional pre-stack simultaneous inversion and a standard deep learning approach.
Ultimately, this webinar will highlight how rock physics guided GeoAI provides high-resolution, physically meaningful reservoir characterization, unlocking insights beyond the limits of traditional inversion and supporting smarter drilling and completion decisions in the Midland Basin
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