Visualization of the sequestered carbon-dioxide plume in the subsurface
using unsupervised learning
Abstract
Subsurface sequestration of carbon dioxide (CO2) requires long-term
monitoring of the injected CO2 plume to prevent CO2 leakage along the
wellbore or across the caprock. Accurate knowledge of the location and
movement of the injected CO2 is crucial for risk management at a
geological CO2-storage complex. Conventional methods for
locating/assessing the injected CO2 plume in the subsurface assume a
geophysical model, which is specific and may not be applicable to all
types of CO2-injection reservoirs and scenarios. We developed an
unsupervised-learning-based visualization of the subsurface CO2 plume
that adapts and scales based on the data without requiring an assumption
of the geophysical model. The data-processing workflow was applied to
the cross-well tomography data from the SECARB Cranfield carbon
geo-sequestration project. A multi-level clustering approach was
developed to account for data imbalance due to the absence of CO2 in the
large portion of the imaged reservoir. The first level of clustering
differentiated CO2-bearing regions from the non-CO2 bearing regions and
achieved a silhouette score of 0.85, a Calinski-Harabasz index of
160666, and a Davies-Bouldin index of 0.43, which are indicative of high
quality, reliable clustering. The second level of clustering further
differentiated the CO2-bearing regions into regions containing low,
medium, and high CO2 content. Overall, the multi-level clustering
achieved a silhouette score, Calinski-Harabasz index, and Davies-Bouldin
index of 0.74, 59656, and 0.32, which confirm the high quality and
reliability of the newly proposed unsupervised-learning-based
visualization. Three distinct clustering techniques, namely k-means,
mean-shift, and agglomerative, generated similar visualizations. In
terms of the adjusted Rand index, the similarity of clusters identified
by the three distinct clustering techniques is around 0.98, which
indicates the robustness of the cluster labels assigned to various
regions of the CO2-injection reservoir. Further, we find certain
geophysical signatures, such as Fourier transform and wavelet transform,
to be highly relevant and informative indicators of the spatial
distribution of CO2 content.