arXiv subjects

Elling Svee

Publications and source records attributed to Elling Svee.

3 recordsLinked to original sources

arXiv2026

Bayesian inversion of multilayer $\mathrm{CO}_2$ migration from seismic plume observations using a graph-based finite-rate invasion-percolation model

Vertical migration of $\mathrm{CO}_2$ in layered sandstone reservoirs is controlled by thin shale barriers whose properties are often poorly known. We develop a Bayesian framework that uses time-lapse seismic plume observations to estimate effective parameters governing lateral and vertical $\mathrm{CO}_2$ migration. The forward model is a fast graph-based invasion-percolation model that extends conventional IP by representing both capillary-controlled filling of structural traps beneath shale barriers and finite-rate transfer through them. Parameters are inferred with approximate Bayesian computation and sequential Monte Carlo sampling, and the posterior samples are propagated into forecasts. Applied to real data from Sleipner, posterior simulations reproduce the broad distribution of $\mathrm{CO}_2$ across nine sand units and its redistribution between 2010 and 2023. In contrast, the quasi-static model fails to reproduce this temporal evolution. Complementary synthetic experiments assess parameter recovery, forecasting, monitoring duration, and model misspecification. These experiments show that the information gained from monitoring depends on the migration events captured, with breakthrough and post-breach redistribution providing particularly strong constraints. This combination of fast simulation, probabilistic updating, and interpretable effective parameters makes the framework well suited to repeated forecast revision during active injection, especially when full-physics inference is too computationally demanding.

physics.geo-ph↗

arXiv2026

Agentic Scientific Simulation: Execution-Grounded Model Construction and Reconstruction

LLM agents are increasingly used for code generation, but physics-based simulation poses a deeper challenge: natural-language descriptions of simulation models are inherently underspecified, and different admissible resolutions of implicit choices produce physically valid but scientifically distinct configurations. Without explicit detection and resolution of these ambiguities, neither the correctness of the result nor its reproducibility from the original description can be assured. This paper investigates agentic scientific simulation, where model construction is organized as an execution-grounded interpret-act-validate loop and the simulator serves as the authoritative arbiter of physical validity rather than merely a runtime. We present JutulGPT, a reference implementation built on the fully differentiable Julia-based reservoir simulator JutulDarcy. The agent combines structured retrieval of documentation and examples with code synthesis, static analysis, execution, and systematic interpretation of solver diagnostics. Underspecified modelling choices are detected explicitly and resolved either autonomously (with logged assumptions) or through targeted user queries. The results demonstrate that agent-mediated model construction can be grounded in simulator validation, while also revealing a structural limitation: choices resolved tacitly through simulator defaults are invisible to the assumption log and to any downstream representation. A secondary experiment with autonomous reconstruction of a reference model from progressively abstract textual descriptions shows that reconstruction variability exposes latent degrees of freedom in simulation descriptions and provides a practical methodology for auditing reproducibility. All code, prompts, and agent logs are publicly available.

cs.SE↗

arXiv2025

Non-stationary Spatial Modeling Using Fractional SPDEs

We construct a Gaussian random field (GRF) that combines fractional smoothness with spatially varying anisotropy. The GRF is defined through a stochastic partial differential equation (SPDE), where the range, marginal variance, and anisotropy vary spatially according to a spectral parametrization of the SPDE coefficients. Priors are constructed to reduce overfitting in this flexible covariance model, and we estimate parameters using a gradient-based optimization approach based on automatic differentiation. In a simulation study, we investigate how many observations are required to reliably estimate fractional smoothness and non-stationarity, and find that one realization containing 500 observations or more is needed in the scenario considered. We also find that the proposed penalization prevents overfitting across varying numbers of observation locations. Two case studies demonstrate that the relative importance of fractional smoothness and non-stationarity is application dependent. Non-stationarity improves predictions in an application to ocean salinity, whereas fractional smoothness improves predictions in an application to precipitation. Predictive ability is assessed using mean squared error and the continuous ranked probability score. In addition to prediction, the proposed approach can be used as a tool to explore the presence of fractional smoothness and non-stationarity.

stat.ME↗