A Comparative Study of GeoAI, Simultaneous Inversion, and Deep Learning for Quantitative Seismic Reservoir Characterization: A Midland Basin Case Study

In this session, 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.

We’ll show how the workflow integrates well logs, synthetic AVO modeling, and seismic gathers to build a large, physically consistent training dataset using the Keys and Xu rock physics framework—plus how transfer learning helps bridge the gap between synthetic and real seismic data.

You will also see a head‑to‑head comparison of GeoAI results against traditional pre-stack simultaneous inversion and a standard deep learning approach:

• GeoAI delivers higher-resolution images, resolving thin beds and subtle stratigraphic features that simultaneous inversion tends to smooth out.
• Conventional deep learning performs reasonably for porosity and mineralogy—but struggles to predict bulk volume hydrocarbons reliably.
• The GeoAI workflow excels, delivering accurate, stable bulk-volume hydrocarbon predictions with strong agreement across six blind wells.

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