arXiv subjects

Dimitrios Voulanas

Publications and source records attributed to Dimitrios Voulanas.

3 recordsLinked to original sources

arXiv2025

Updating DMD Operators for Changes in Domain Properties

Fast and reliable surrogate models are critical for optimization, control and uncertainty analysis in geological carbon-storage projects, yet high-fidelity multiphase simulators remain too expensive. Dynamic Mode Decomposition (DMD) offers an attractive data-driven reduction framework, but its operators are trained for a single set of reservoir properties. When permeability or well location changes, conventional practice is to regenerate snapshots and retrain the surrogate, erasing most of the speed advantage. This work presents a lightweight alternative that updates an existing DMD or DMD-with-control model without incorporating new simulation data or retraining. Two complementary update strategies are introduced. For cases where permeability changes uniformly across the domain, the proposed updates adjust the models internal dynamics and control response to match the new flow timescale. When permeability varies in space, the approach modifies the spatial representation so that high-permeability zones are given greater influence on the models reduced basis. Numerical experiments demonstrate that the proposed updates recover plume migration and pressure build-up within three percent of a freshly trained surrogate yet execute hundreds of times faster than full retraining. These methods therefore enable real-time optimization and rapid what-if studies while preserving the physical fidelity demanded by carbon-storage workflows.

physics.comp-ph↗

arXiv2025

Introducing Coherent-Control Koopman Modeling to Reservoir Scale Porous Media Flow Studies

Accurate and robust surrogate modeling is essential for the real-time control and optimization of large-scale subsurface systems, such as geological CO2 storage and waterflood management. This study investigates the limits of classical Dynamic Mode Decomposition with control (DMDc) and introduces CCKM, as a robust alter-native, in enforcing control in pressure and water saturation reservoir dynamics under challenging prediction scenarios. We introduced a control-coherent incremental ({\Delta}) CCKM formulation, in which the field update is driven by actuator changes rather than rather than actuator levels as in the original level formulation and compared them both against DMDc and a Hybrid B-only surrogate that re-uses DMDcs bottom-B (same-step feed-through), showing that only CCKM remains stable and accurate under regime shifts. Two representative cases are considered: (i) an out-of-distribution shut-in and restart case, and (ii) an in-distribution bottomhole pressure (BHP) drawdown. Results show that only CCKM consistently maintains stability and accuracy across both scenarios, achieving sub-bar mean absolute error and sub-percent Frobenius norm percent change error (FPCE) even under regime shifts, while DMDc exhibit large unphysical errors during control transients. The findings demonstrate that strict control-coherence is critical for reliable surrogate modeling, particularly in settings with abrupt changes in control strategy. The proposed framework is broadly applicable to real-time reservoir optimization and can be integrated seamlessly into existing optimization and monitoring workflows, enabling fast and trustworthy deci-sion support in the presence of both expected and unexpected actuation regimes.

eess.SY↗

arXiv2024

Dynamic Mode Decomposition Accelerated Forecast and Optimization of Geological CO2 Storage in Deep Saline Aquifers

Data-driven and non-intrusive DMDc and DMDspc models successfully expedite the reconstruction and forecasting of CO2 fluid flow with acceptable accuracy margins, aiding in the rapid optimization of geological CO2 storage forecast and optimization. DMDc and DMDspc models were trained with weekly, monthly, and yearly simulation pressure and CO2 saturation fields using a commercial simulator. The domain of interest is a large-scale, offshore, highly heterogeneous reservoir model with over 100,000 cells. DMD snapshot reconstruction significantly reduced simulation times from several hours to mere minutes. DMDspc reduced the number of DMD modes for pressure without losing accuracy while sometimes even improving accuracy. Two operation cases were considered: 1. CO2 injection, 2. CO2 injection and water production for pressure maintenance. For pressure, DMDspc achieved a slightly higher than DMDc average error by removing several modes. On the other hand, DMDspc showed limited success in reducing modes for CO2 saturation. The forecast performance of DMD models was evaluated using percent change error, mean absolute error and Pearsons R correlation coefficient metrics. Almost all DMD pressure models managed to successfully forecast pressure fields, while a smaller number of DMD models managed to forecast CO2 saturation. While forecast errors have a considerable range, only DMD models with errors below 5% PCE for pressure or 0.01 MAE for saturation were considered acceptable for geological CO2 storage optimization. Optimized CO2 injection and water production amounts were consistent across selected DMD models and all time scales. The DMDspc-monitored cells approach, which only reconstructs the monitored-during-optimization cells, reduced even further optimization time while providing consistent results with the optimization that used full snapshot reconstruction.

physics.comp-ph↗