Iron‐rich rocks undergoing oxidation in the presence of water are promising sources of geologic hydrogen (H ₂ ). Yet, such reactions are often considered self‐limiting due to passivation and reduced permeability if volume expansion occurs. This study investigates hydrogen generation during a flow‐through experiment on fractured iron‐rich cores packed with proppants, simulating a stimulated subsurface environment. Hydrogen generation initially declined over time. Introducing a thermochemical fluid to accelerate the reaction via thermal enhancement did not yield measurable hydrogen gains. Subsequent acid treatment for 2 hr led to a 30‐fold increase in hydrogen generation, which was sustained for 144 hr before the onset of a decline. Post‐acidizing hydrogen generation remained larger than pre‐acidizing hydrogen. Post‐experiment characterizations confirmed the development of preferential flow paths, mineral precipitation along fracture surfaces, and fluid‐induced surface alteration. These results demonstrate that periodic chemical stimulation can significantly enhance and prolong hydrogen generation by reactivating mineral‐fluid interfaces.

This study investigates the impact of CO₂-enhanced oil recovery (CO₂-EOR) on the petrophysical properties and oil recovery potential of sandstone reservoirs in the oilfields located in the east-southern Precaspian region of Kazakhstan. Despite the recognized potential of CO₂-EOR for improving oil recovery and aiding carbon sequestration, there is limited understanding of how CO₂-EOR specifically affects the petrophysical properties of sandstone reservoirs in this region. Laboratory experiments were conducted using two core samples from the selected oilfields to examine changes in porosity, permeability, and oil recovery coefficients. The results demonstrated that porosity changes ranged from a slight increase of 1.1% to a decrease of 1.5%, while permeability reduction was significant, with decreases ranging from 29% to 50% due to clay alteration and halite precipitation. The oil recovery coefficient after CO₂ flooding was found to be between 0.49 and 0.54. These findings underscore the complex interactions between CO₂ and reservoir rocks, emphasizing the need for tailored EOR strategies in different geological settings.

This paper describes a comprehensive approach to predict sand production in the Karazhanbas oilfield using machine learning (ML) techniques. By analyzing data from 2000 wells, the research uncovered the complex dynamics of sand production and emphasized the critical need for accurately predicting the peak sand mass and its occurrence time. ML techniques can have a significant impact on prediction of sand production and on the optimization of oilfield operation, which can be improved with the combined use of enriched training data and domain-specific knowledge. The research underscored the influence of geological factors, especially fault proximity, on prediction accuracy. Domain and field knowledge is needed to formulate different production scenarios for prediction purposes such that the relevant data can be selected for the training of ML models. Moreover, new metrics are needed to evaluate model performance as the applied method is tailored for different operational strategies. As the peak sand mass is considered a pivotal event in field operation, new metrics in terms of peak prediction accuracy and peak time prediction accuracy were introduced to evaluate the performance of ML models. A suite of ML algorithms was employed in the study, which demonstrated notable accuracy in the classification of sand-producing wells.

Enhanced oil recovery (EOR) techniques are essential for maximizing hydrocarbon extraction from mature reservoirs. CO₂ injection (CO₂-EOR) is a promising technology that improves oil recovery while contributing to greenhouse gas reduction. This study investigates the potential of miscible CO₂-enhanced oil recovery (CO₂-EOR) in the MakXX oilfield of southeastern Kazakhstan. The aim is to assess oil displacement efficiency and its impact on key rock properties, including porosity, permeability, and mineral composition, under reservoir conditions. Core flooding experiments were conducted at 13 MPa and 42 °C using high-precision equipment to replicate reservoir conditions. The core was analyzed before and after CO₂ injection using SEM, EDS, and XRD. The results revealed a 54% oil recovery efficiency, accompanied by a 19% decrease in permeability and 8% reduction in porosity due to mineral precipitation and clay transformation. These findings provide insight into the performance and limitations of CO₂-EOR and support its application in similar lithology. To confirm and upscale laboratory observations, numerical simulation was conducted using a compositional model. The results demonstrated improved oil recovery, pressure stabilization, and enhanced sweep efficiency under CO₂ injection, supporting the scalability and field applicability of the proposed EOR approach.