Unsupervised learning monitors the carbon-dioxide plume in the subsurface carbon storage reservoir

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 seismic 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 0.88, a Calinski-Harabasz index of 271145, and a Davies-Bouldin index of 0.30, which are indicative of high quality, reliable clustering. The second level of clustering further differentiated the CO2 -bearing regions into regions containing low, medium-low, medium-high, and high CO2 content. Overall, the multi-level clustering achieved a silhouette score, Calinski-Harabasz index, and Davies-Bouldin index of 0.68, 86750, and 0.46, 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 geological carbon-storage reservoir. Further, we find certain geophysical signatures, such as Fourier transform and wavelet transform, represent the distribution of CO2 content in subsurface.

Automated summary

This study developed an unsupervised-learning-based visualization method for subsurface CO2 plume that can adapt and scale based on the data without assuming a geophysical model. A multi-level clustering approach was used to accurately differentiate CO2-bearing regions from non-CO2 bearing regions and further classify regions with different levels of CO2 content. The proposed method was validated in a real-world project and demonstrated high quality and reliability.