SiliciclasticReservoirs: A Million-Reservoir Dataset and Flow-Matching Foundation Model for 3D Siliciclastic Reservoir Generation

3D subsurface geological models underpin hydrocarbon exploration, geothermal energy, underground gas storage, and geological carbon storage. A decade of deep generative facies modeling has yet to yield a model that generalizes across geological settings, scales to realistic reservoir extents, and conditions naturally on well data, bottlenecked by the absence of a large-scale, multi-environment, property-rich dataset. We release SiliciclasticReservoirs, the largest and most diverse public 3D dataset of siliciclastic (sandstone-bearing) subsurface reservoirs to date: $10^6$ voxelized reservoirs at $64{\times}64{\times}32$ resolution spanning eight depositional settings (deep-water turbidite lobes, deltas, and six fluvial channel architectures from the Alluvsim family), each annotated with binary and six-class facies labels, per-voxel porosity and permeability, and a natural-language caption describing the reservoir's geological setting and petrophysical attributes. To our knowledge, it is the first siliciclastic dataset to pair 3D geological models with petrophysical properties at this scale, the first to span this breadth of settings in a unified release, and the first to ship text captions, supporting future multimodal and language-conditioned training for text-to-reservoir generation and retrieval. Building on SiliciclasticReservoirs, we train ResFlow, a flow-matching foundation model that, from a single set of weights, generates any environment under continuous parametric control, conditions on arbitrary well configurations via classifier-free guidance, and assembles reservoirs at arbitrary spatial extent through overlapping-tile MultiDiffusion, with no retraining. SiliciclasticReservoirs (CC-BY 4.0), ResFlow (MIT), and the data-generation engine ResMill (MIT) are publicly released.