FMSIM: A Multimodal Flow Matching Framework for Conditional Geomodeling

Abstract

Subsurface geomodeling plays a critical role in reservoir characterization, uncertainty quantification, and subsurface flow prediction. However, integrating heterogeneous sources of geological information, including conceptual geological descriptions, sparse well observations, and spatial prior constraints, remains a significant challenge for traditional geostatistical and data-driven geomodeling approaches. In this study, we present FMSIM, a multi-modal conditional flow matching framework for subsurface facies model generation. FMSIM utilizes a deep learning formulation to learn a velocity field that transports samples from a simple prior distribution to a complex geological facies distribution. Global geological semantic information is incorporated through a learned semantic representation framework and a learned prior model, while local hard constraints are enforced via an iterative projection strategy during sampling to ensure 100% fidelity to well observations. Additionally, a temporal guidance gating mechanism is introduced to regulate the influence of spatial probability maps, balancing large-scale trend alignment with fine-scale geological variability. Benefiting from the framework design, the model enables efficient and stable training with a simple loss function. The framework's fully convolutional architecture also demonstrates promising generalization to moderately larger grid sizes not seen during training without retraining. Results on a synthetic fluvial channel dataset indicate that FMSIM captures complex non-stationary geological features and produces geologically consistent realizations under multi-modal conditioning. This approach offers a flexible tool for incorporating conceptual geological knowledge, sparse observational data, and spatial priors into probabilistic subsurface geomodeling workflows.