A Modular Physics-Informed Neural Network Framework for Quantifying Pressure Interference Between Concurrent Deep Subsurface Activities

Deep aquifers offer significant potential for diverse energy and storage applications, these opportunities also will require synergistic multi-user subsurface management. To maximize these resources, operators require flexible modeling tools capable of rapidly evaluating how independent but concurrent projects might interact hydraulically over time. Traditional grid-based numerical models are robust but can be computationally demanding when rapid scenario testing is required across large, heterogeneous regions. We propose a modular Physics-Informed Neural Network (PINN) framework designed to provide a flexible, faster alternative for evaluating regional pressure interference between co-located subsurface activities.

Our proposed architecture treats the aquifer as a continuous volumetric field. We define injection and extraction points as dynamic operational conditions (e.g., transient rate or pressure constraints) that can be positioned anywhere in the domain. The neural network is trained to satisfy the 3D transient diffusivity equation, learning to map the relationship between these sources and the resulting pressure field without relying on fixed meshes We address this by introducing a  "modular" architecture: by training separate sub-networks for each activity type, we aim to mathematically isolate or "de-mix" the pressure contribution of specific projects from the total regional signal.

This research focuses on a case study in the Campine Basin (Belgium). We are developing the framework to infer effective aquifer properties from sparse historical monitoring data and to simulate interference patterns specifically between gas storage and geothermal operations. The expected outcome is a spatial scenario analysis tool that allows future users to dynamically test new project locations and optimize setback distances within a Subsurface Digital Twin environment. By decoupling the geological parameterization from specific well locations, we aim to provide a scalable engine that supports adaptive planning and de-risks decision-making in multi-activity aquifers.