Extensive dolomitization is prevalent in the platform and periplatform carbonates in the Lower-Middle Permian strata in the Midland and greater Permian basin. Early workers have found that the platform and shelf-top carbonates were dolomitized while slope and basinal carbonates were remained calcitic, proposing a Reflux Dolomitization model as the possible diagenetic mechanism. More importantly, they underline that this dolomitization pattern controls the porosity and forms an updip seal. These studies are predominately conducted using well logs, cores, and outcrop analogs, and while exhibiting high resolution vertically, such determinations are laterally sparse. In this study, we employed supervised Bayesian classification and probabilistic neural networks on 3D seismic volume to create an estimation of the most probable distribution of dolomite and limestone within a subsurface 3D volume petrophysically constrained. Combining this lithologic information with porosity we then illuminate the diagenetic effects on a seismic scale. We started our workflow by deriving lithology classifications from well log cross plots of neutron porosity and acoustic impedance to determine a priori proportions of lithology, and probability density functions calculation for each lithology type. Then, we applied these probability distributions and a priori proportions to 3D seismic volumes of acoustic impedance and predicted neutron porosity volume to create a lithology volume and probability volumes for each lithology type. Acoustic impedance volume was obtained by model-based post-stack inversion and the neutron porosity volume was obtained by the probabilistic neural network. Results best support a regional reflux dolomitization model, in which the porosity is increasing from shelf to slope while dolomitization is decreasing, but with sea level forcing. With this study, we demonstrate that diagenesis and corresponding reservoir quality in these platforms and periplatform strata can be directly imaged and mapped on a seismic scale by quantitative seismic interpretation and supervised classification methods.

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