Orison: A modular data assimilation environment for subsurface digital twins

Subsurface Digital Twins rely critically on assimilation of data frameworks to continuously integrate multi-source, multi-type observations. While numerous methods have been developed to improve quantitative subsurface predictions, there is currently no clear consensus or standardised guidance on their appropriate computational deployment within digital twin workflows. Instead, research communities often adopt specific algorithms primarily because they are prevalent within their discipline, rather than because they are demonstrably optimal for the problem at hand. This lack of consensus reflects our limited understanding of how to rigorously characterise the mathematical structure of subsurface assimilation problems involving coupled multi-physics processes, multiple spatial and temporal scales, and heterogeneous data streams. As a result, current efforts frequently focus on empirical experimentation with algorithms rather than on the design of problem-adapted methodologies. This challenge extends to the formulation of the inverse problem itself, including parameterisation, parameter ranges, objective functions, and performance metrics, as well as to the selection of optimisation or inference strategies in multi-source data environments. Furthermore, comprehensive uncertainty quantification through global multi-factor sensitivity analysis is often infeasible due to the prohibitive computational cost of large-scale problems. To address these challenges, we propose Orison, a modular data assimilation environment designed to support systematic benchmarking and comparative analysis of classical model update algorithms for subsurface digital twin workflows. Orison enables controlled experimentation across a range of thematical problems, facilitating insight into algorithm performance and robustness. We demonstrate the capabilities of Orison through representative case studies in geothermal systems and groundwater management, illustrating how such a benchmarking framework can support more transparent methodological choices and contribute to the development of reliable, pragmatical subsurface digital twins.