The study of geological CO₂ sequestration and its long-term implications are crucial for ensuring the safety and sustainability of carbon capture and storage (CCS) projects. This work presents a numerical reservoir modeling study to upscale CO₂ injection in the Eastern Illinois Basin to a cumulative value of 27 Mt within the next 20 years, adding one proposed Class VI injector well to the two already existing ones in this field. Along with the reservoir simulations that include the main CO₂ trapping mechanisms that ensure a minimum of a 100-year Area-of-Review containment, we describe a step-by-step approach to enhance measurement, monitoring, and verification (MMV) plans, starting from low-cost methods such as repeated 1D VSP in existing boreholes to 2D seismic surveys and higher-cost data acquisition techniques.

Compressional velocity (V_p) and bulk density (ρ_b) logs are essential for characterizing gas hydrates and near-seafloor sediments; however, it is sometimes difficult to acquire these logs due to poor borehole conditions, safety concerns, or cost-related issues. We present a machine learning approach to predict either compressional V_p or ρ_b logs with high accuracy and low error in near-seafloor sediments within water-saturated intervals, in intervals where hydrate fills fractures, and intervals where hydrate occupies the primary pore space. We use scientific-quality logging-while-drilling well logs, gamma ray, ρ_b, V_p, and resistivity to train the machine learning model to predict V_p or ρ_b logs. Of the six machine learning algorithms tested (multilinear regression, polynomial regression, polynomial regression with ridge regularization, K nearest neighbors, random forest, and multilayer perceptron), we find that the random forest and K nearest neighbors algorithms are best suited to predicting V_p and ρ_b logs based on coefficients of determination (R²) greater than 70% and mean absolute percentage errors less than 4%. Given the high accuracy and low error results for V_p and ρ_b prediction in both hydrate and water-saturated sediments, we argue that our model can be applied in most LWD wells to predict V_p or ρ_b logs in near-seafloor siliciclastic sediments on continental slopes irrespective of the presence or absence of gas hydrate.

Elucidating the geologic history of the Greenland ice sheet (GrIS) is essential for understanding glacial instability thresholds, identified as major climate system tipping points, and how the cryosphere will respond to anthropogenic greenhouse gas emissions. To address current knowledge gaps in the evolution and variability of the GrIS and its role in Earth's climate system, International Ocean Discovery Program (IODP) Expedition 400 obtained sedimentary records from Sites U1603-U1608 across the northwest Greenland margin into Baffin Bay where thick Cenozoic sedimentary successions can be directly linked to the evolution of the northern GrIS (NGrIS). The strategy of drilling along this transect was to retrieve a composite stratigraphic succession representing the late Cenozoic era from the Oligocene/early Miocene to the Holocene.