Generating 3D lithology probability volumes using poststack inversion, probabilistic neural networks, and Bayesian classification — A case study from the mixed carbonate and siliciclastic deposits of the Cisco Group of the Eastern Shelf of the Permian Basin, north-central Texas

The deposition and mixing of carbonates and siliciclastics in the Cisco Group of the Eastern Shelf of the Permian Basin are complicated by the temporal overlap between icehouse eustatic sea-level oscillations and fluctuations in sediment influx due to the rejuvenation of the Ouachita fold belt. Previous investigators have used well-log correlation as the primary tool in their interpretations of the area’s reciprocal depositional model, but well-log correlation alone cannot explain the full range of spatial lithology variations in the system. To better understand the lithology variation in the area, we use an integrated technique that combines wireline log information from 17 wells with 625 km 2 3D seismic data through poststack seismic inversion, probabilistic neural networks (PNNs), and Bayesian classification. We use deterministic matrix inversion to derive lithology classes from well logs. Crossplot analyses reveal that the acoustic impedance and neutron porosity log pair can be used to differentiate lithologies. We perform model-based poststack inversion to generate a P-impedance volume and use PNNs to generate a neutron porosity volume. We combine these volumes through supervised Bayesian classification to generate lithology probability volumes for each lithology and a most probable lithology volume throughout the seismic data. The lithology volumes highlight the dominant lithologies (carbonate, shale, sand, and mixed) that allowed the interpretation of major carbonate platforms, sand-to-shale ratio variations, carbonate buildups between wells, and channel fill lithologies. Our semiautomated lithology detection workflow applies to regional studies and is also valid for reservoir-scale studies to determine variations in lithologies.