Many important geotechnical issues (e.g., groundwater supply and contamination, subsurface waste disposal, hydrocarbon exploration and production) require a detailed understanding of porosity and permeability in subsurface clastic formations (= reservoir quality). Reservoir quality depends on the size, shape, and packing of sand grains as they are originally deposited, as well as diagenetic changes during burial. Obtaining enough samples to fully characterize the target formation is prohibitively expensive or physically impossible. Therefore, reservoir quality estimates must be extrapolated from analogues (± sparse samples) or derived from models. Forward-modeling approaches to predicting diagenetic effects on reservoir quality are well established, but they require information about the character of deposited sand, including mean grain size, sorting, matrix content, and composition of diagenetically relevant particles (i.e., all rock fragments, not just lithic fragments). In cases where deposited sand characteristics are not known, they must be estimated. To this end, we advocate an integrated genetic analysis, which simultaneously predicts multiple sand characteristics as a function of many environmental controls, including tectonic setting, provenance lithotype abundance, climate, regional topographic gradient, hinterland transport distance, basin transport distance, basin subsidence rate, and depositional environment. We have implemented this analytic procedure as a Bayesian belief network–based forward model that successfully predicts sand composition and texture in diverse settings, including provenance areas dominated by either volcanic, high-grade metamorphic, or sedimentary lithologic assemblages; climates ranging from tropical to desert; and a range of alluvial/fluvial drainage types represented by small steep drainages as well as continental-scale big rivers.