Unsupervised Learning Techniques for Microseismic and Croswell Geophysical Data

Machine learning has served to develop and explore a wide range of applications for geoscientists and petroleum engineers. Fundamental limitations of conventional methodologies include mathematical formulations of physical systems, multi-scale heterogeneity, processing of large datasets, and computational time. The impact of these new technologies has brought the interest of multiple energy industries such as renewables, oil and gas, carbon sequestration, and geothermal. The acquisition of subsurface measurements has been a key factor to characterize reservoir properties. Hence, the integration of machine learning could provide essential information and new knowledge of subsurface monitoring signals. In this work, we focus on the use of unsupervised learning to determine new insights into geophysical tools and subsurface physical properties. We propose three methodologies using microseismic, distributed acoustic sensing (DAS), seismic and electrical resistivity tomography. A critical aspect of monitoring tools is the high computational power of big data. We applied unsupervised dimensionality reduction to compress, denoise and retrieve vital information of microseismic and DAS data. To achieve this, we implemented high-order SVD for high-dimensional arrays of 3D and 4D space. For the 3D microseismic, we achieved a compression of approximately 75% and a reduction of samples from 1,728,000 to 431,303. We also tested the model to the 3D DAS data where we obtained a compression of 70.2% for a data size of 3.5 GB. Lastly, a 4D HOSVD model was established using a synthetic microseismic tensor, accomplishing a reduction of 83%. Another major application of unsupervised learning is the clustering algorithms to group observations of similar characteristics. We applied spatial-temporal clustering to identify hidden patterns of subsurface mapping for a geological carbon storage field. The studies were divided according to the geophysical method (crosswell seismic and ERT) and temporal component (single time or time-series). Using crosswell seismic, we developed a multi-level clustering approach to visualize the CO2 plume behavior. For the first level, we obtained a silhouette score of 0.85, a Calinski-Harabasz of 160666.50, and a Davies-Bouldin value of 0.43. The second level achieved a silhouette, CalinskiHarabasz, and Davies-Bouldin score of 0.74, 59656.01, and 0.32 respectively. We established a total of four clusters of non, low, medium, and high SCO2. Finally, we elaborated a spatial-temporal clustering using derived-SCO2 from daily ERT images. A novel feature extraction methodology was designed to retrieve the spatial and temporal changes of the moving CO2. Four clusters were determined and linked to the saturation levels. The interval validation of clusters was 0.58 for the DTW-silhouette score, 262791.45 for Calinski-Harabasz, and 0.71 for the Davies-Bouldin index. To evaluate the dynamics of CO2 flow regimes, we performed a second clustering where 6 distinctive plume patterns were observed. Therefore, machine learning and in particular unsupervised learning can be used to describe complex systems and optimize data processing