Feasibility study of rapid CO2 plume forecasting for CO2CRC Otway Project Stage 3

Abstract:

Permanent reservoir surveillance is an invaluable monitoring tool for CO₂ storage projects, as it tracks spatial-temporal evolution of the gas plume. The frequent images of CO₂ plumes will facilitate history-matching of the reservoir simulations and increase confidence of early leakage detection. However, continuous data acquisition and real-time interpretation require a new approach to data analysis. Here we propose a data-driven approach to forecasting future time-lapse seismic images based on the observed past images and test this approach on the Otway Stage 2C data.

The core component of the predictor is a convolutional neural network, which considers subsequent plume maps as colour layers, similarly to standard red-green-blue blending. Based on the spatial distribution of these ‘colours’, we may predict the future contour of the seismically visible part of the plume. The neural networks absorb the physics of CO₂ migration through training on reservoir simulations for a wide range of injection scenarios and subsurface models. Extensive testing shows that realistic plumes for Stage 2C are too complicated and the neural network should be pre-trained on simpler reservoir simulations that include only one or two geological features, such as: faults, spill-point etc. Such staged training can be seen as a gradual descent of the neural network optimisation to a global minimum.

In an upshot, the proposed algorithms are proven accurate. The approach is practical, because the synthetic training set is a necessary component of pre-injection study at each CO₂ storage site. Once the predictor has been trained, it is capable of rapid plume forecasting.