Digital Twins & Scientific Simulations

Both statistical and physical simulations are important to create subsurface Earth models honoring sparse well and seismic ground truthing. Geostatistics and forward stratigraphic modelling (e.g., CRONOS, DionisosFlow, CarboCAT) are two well-known approaches to produce Earth models, and these can then be used in flow simulations. Increasingly, deep learning is important to create fast, calibrated Earth models. We have a long history of working with simulations for the subsurface.

Much of our research explores how different modelling philosophies can be combined to understand sedimentary systems and reservoirs. Geostatistical methods, for example, are fast and honour well data perfectly, but because they are not anchored in physics they can produce unrealistic reservoir geometries; we have developed geostatistical tools such as pluri-Gaussian simulations to capture cyclical sedimentary successions. Forward stratigraphic models offer the complementary trade-off: they may not reproduce well data exactly, but they always yield physically realistic geometries. This makes them well suited to questions about depositional controls — from Cretaceous carbonates in Oman, to high net-to-gross deposits in the Gulf of Mexico, to Jurassic organic shales in the Middle East. A third strand of our work turns to dynamic flow simulation, for instance predicting water breakthrough in oil and gas reservoirs, including NURBS-based techniques for modelling channel deposits in Saudi Arabia. Together these approaches illustrate the range of numerical methods we use, and how their contrasting strengths can be brought to bear on a given problem.

One of the drawback of physics-driven simulation is that they can be very slow to compute. This is especially problematic for geological simulations: because the inital conditions of the model are usually poorly constrained, an ensemble of simulations needs to be run, each costing a significant amont of computation. We use deep learning as a surrogate for these simulators: training convolutional and recurrent neural networks on libraries of forward model runs, then using the trained surrogates to explore parameter space orders of magnitude faster. This enables rapid sensitivity analysis, quick identification of most compatible simulation parameters, and orders of magnitude faster solving of complex simulations. We couple this with data assimilation techniques to improve the calibration of models, thus ensuring that our ensembles are composed of mostly calibrated runs.

Numerical weather prediction and climate modelling have long been dominated by physics-based models that are computationally expensive and imperfect in their parameterisations. AI offers a complementary approach: learn the statistical relationships embedded in decades of reanalysis and observational data to produce fast, accurate forecasts. Although our team is focusing mostly on geological simulation, we have estabished some collaboration with atmospheric scientists and have competed - and won - in weather forecast hackatons.