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Understanding the linkages between grain mineralogy and diagenetic and sedimentary processes enhances the reliability of petrophysical models to predict reservoir deliverability from permeability. Petrographic data within well-defined depositional facies reveal the diagenetic evolution of porosity-permeability relationships. Formation evaluation methods relying solely on petrophysical rock typing are seriously limited when predicting ultimate reservoir performance in complex pore structures. The Almond Formation, Wyoming, is characterized by three depositional facies associations — shoreface, deltaic (bay head and flood tide), and fluvial-coastal plain — which present three distinctive porosity-permeability trends. Textural features resulting from depositional processes, such as grain size and sorting, vary little between facies associations, yet permeability can vary by up to four orders of magnitude for the same porosity value. Differences between petrophysical facies are primarily driven by diagenetic (cementation and grain dissolution) effects on different framework grain compositions (petrographic facies). Therefore, the main difference between the facies associations is diagenetic, due to provenance and transport mechanisms. The characterization of depositional and diagenetic controls on pore geometry allows the narrowing of uncertainty in absolute permeability prediction. We have quantified the relationship between depositional facies, with their specific mineral composition and diagenetic overprint, and the steepness functions in porosity-permeability space. This analysis allowed us to effectively reduce the uncertainty in the prediction of initial gas production from wireline logs.

We describe the successful application of a new prestack stochastic inversion algorithm to the spatial delineation of thin reservoir units otherwise poorly defined with deterministic inversion procedures. The inversion algorithm effectively combines the high vertical resolution of wireline logs with the relatively dense horizontal coverage of 3D prestack seismic amplitude data. Multiple partialangle stacks of seismic amplitude data provide the degrees of freedom necessary to estimate spatial distributions of lithotype and compressional-wave (P-wave) and shear-wave (S-wave) velocities in a high-resolution stratigraphic/sedimentary grid. In turn, the estimated volumes of P-and S-wave velocity permit the statistical cosimulation of lithotype-dependent spatial distributions of porosity and permeability. The new stochastic inversion algorithm maximizes a Bayesian selection criterion to populate values of lithotype and P-and Swave velocities in the 3D simulation grid between wells. Property values are accepted by the Bayesian selection criterion only when they increase the statistical correlation between the simulated and recorded seismic amplitudes of all partial-angle stacks. Furthermore, inversion results are conditioned by the predefined measures of spatial correlation (variograms) of the unknown properties, their statistical cross correlation, and the assumed global lithotype proportions. Using field data acquired in a fluvial-deltaic sedimentary-rock sequence, we show that deterministic prestack seismic-inversion techniques fail to delineate thin reservoir units (10-15 m) penetrated by wells because of insufficient vertical resolution and low contrast of elastic properties. By comparison, the new stochastic inversion yields spatial distributions of lithotype and elastic properties with a vertical resolution between 10-15 m that accurately describe spatial trends of clinoform sedimentary sequences and their associated reservoir units. Blind-well tests and cross validation of inversion results confirm the reliability of the estimated distributions of lithotype and P-and S-wave velocities. Inversion results provide new insight to the spatial and petrophysical character of existing flow units and enable the efficient planning of primary and secondary hydrocarbon recovery operations.