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Abstract Carbon Capture, Utilization, and Storage (CCUS) offers a viable solution to reduce the carbon footprint in the petroleum industry, and foam injection presents a promising method to achieve this while simultaneously increasing oil recovery. In this work, we studied the feasibility of CO2 foam for co-optimizing enhanced oil recovery and CO2 storage in a high-salinity carbonate formation. The simulated hydrodynamic model is a depleted formation containing 30% residual oil, with three mechanisms for CO2 storage: solubility, residual, and mineralization trapping mechanisms. The results showed that after 20 years, oil recovery during foam injection was 2.7 times higher than CO2 injection, and the CO2 stored during foam flooding was 38% higher than CO2 injection. Notably, foam injection also increased CO2 storage capacity by 2.6 times, indicating the potential to store around 2 gigatons of CO2 in the simulated model. This was attributed to the ability of foam to significantly reduce gas mobility and thus form isolated bubbles through its Jamin effect. Residual trapping was the dominant trapping mechanism, contributing to over 70% of the total CO2 trapped, attributed to the reduction in the dissolution of CO2 in brine due to the high salinity of the aqueous medium. CO2 mineralization was also studied, showing the least trapping efficiency and the dissolution trend of all the carbonate minerals. This study illustrates a novel CO2 utilization and storage technique in which CO2 is concurrently sequestered while enhancing oil recovery in a depleted oil reservoir by injecting CO2 as foam. The relevance of this study lies in its potential to provide a dual benefit of reducing greenhouse gas emissions and boosting oil production, offering a sustainable approach for the petroleum industry.

Abstract Conventional assumptions about multiphase flow in gas condensate reservoirs often do not correlate with field production. This discrepancy stems from the various mechanisms influencing the multiphase process, which are inadequately represented in numerical models. One of the least understood mechanisms is the influence of the non-equilibrium thermodynamics on the flow in the wellbore region, where the reservoir pressure is below the dew point pressure. To address this problem, experimental and mathematical analyses were conducted using a microfluidic device designed to replicate the flow dynamics in a gas condensate system. The experimental results showed an 11% deviation from the initial pressure of condensate saturation when compared with the conventional assumption of local equilibrium in numerical models. Similarly, there is a 14% deviation between the experimental and simulated volumes of the condensate. These findings underscore the inadequacy of existing models to accurately predict the saturation profile of the condensate phase. A mathematical model based on a relaxation parameter was applied to account for non-equilibrium phase separation and the fog state of the aerosol as observed in the microfluidic experiment. Incorporating a relaxation parameter ( $$\tau$$ τ ) enhanced the accuracy of the prediction of the initial pressure of the condensate saturation and an improvement in the prediction of the condensate volumes from 76% to 97.2%. Consequently, it provides a valuable framework and insight on the non-equilibrium phase behavior of gas condensate systems under constant flow regimes.

Oil reservoirs are nearing maturation, necessitating novel enhanced oil recovery (EOR) techniques to meet escalating global energy demands. This demand has spurred interest in reservoir production analysis and forecasting tools to enhance economic and technical efficiency. Accurate validation of these tools, known as simulators, using laboratory or field data is pivotal for precise reservoir productivity estimation. This study delves into the application of nanoparticles in foam flooding for mobility control to improve sweep efficiency. Foam generation can occur in-situ by simultaneous injection of surfactants and gas or through pre-generated foam injection into the reservoir. In this work, a series of systematic simulations were run to investigate how much injected fluids can reduce gas breakthrough while also increasing oil recovery. Subsequently, we analyzed the most effective optimization strategies, considering their economic limits. Our primary objective is to numerically model nanofoam flooding as an innovative EOR approach, synergizing foam flooding mechanisms with nanotechnology benefits. In this work, modeling of nanoparticles in foam liquid was represented by the interfacial properties provided to the injection fluid. Additionally, we simulated Water-Alternating-Gas (WAG) injection schemes across various cycles, comparing their outcomes. Our results showed that nanofoam injection achieved a higher recovery factor of at least 38% and 95% more than WAG and gas injections, respectively. The superior efficiency and productivity of foam injection compared to WAG and gas injection suggest an optimal EOR approach within the scope of our model. These simulated optimization techniques contribute to the future development of processes in this field.

Shale and tight oil reservoirs, with horizontal wells and hydraulic fractures, typically have a recovery ratio of around 10%. The exploration of tight oil and shale in North America has proven economically viable, thanks to advancements, such as horizontal wells, hydraulic fracturing, and other enhanced oil recovery techniques. Taking inspiration from the global experience (the North American shale experience), the exploration and development of the West Siberian tight and shale reserves was more focused on the reported best practices of the exploration of North American shale. In this study, the advance in the specific areas of shale and tight oil exploration was considered, with more emphasis placed on the progress in the exploration of West Siberian shales. According to the review literature, thermal enhanced recovery methods capable of converting organic matter into hydrocarbons were studied more than other methods of enhanced oil recovery. Aligned with global trends, there has been a growing focus on research aiming to integrate data-driven approaches and pore-scale simulations to enhance recovery from tight and shale formations. Multiple pilot studies have showcased promising prospects for implementing multistage hydraulic fracturing. Nevertheless, there are limited pilot studies dedicated to enhanced oil recovery methods for West Siberian shale.

Oil production is a complex process that can be made more efficient by applying gas enhanced oil recovery (EOR) methods. Thus, it is essential to know the minimum miscibility pressure (MMP) and minimum miscibility enrichment (MME) of gas in oil. Conventional slim-tube experiments for the measurement of MMP require hundreds of millilitres of real or recombined oil and last over 30 days. Advances in microfluidic technology allow the reduction of the amount of fluid and the time required in determining MMP (or MME), hence making the process rapid. In this study, we developed a microfluidic model with a stochastically distributed pore network, porosity of 74.6% and volume of 83.26 nanolitres. Although the volume was six orders of magnitude smaller than the slim tube, it retained the same proportions, guaranteeing a proper comparison between the tests. This microfluidic chip allowed the study of the MMP of n-decane with carbon dioxide at two different temperature conditions. The experimental results coincided with the results received both from conventional and microfluidic experiments. Furthermore, a numerical simulation of a section of the microfluidic model under the experimental conditions presented results within acceptable margins of the experimental ones. The results of the presented methodology indicate the potential to replace conventional technology for the measurement of MMP with microfluidic technology. Its promise lies in accelerating laboratory tests and increasing the reliability of experimental results and, subsequently, the quality of field gas EOR operations.

Shale and tight hydrocarbons are vital to global energy dynamics. The fluid flow in sub-micron pores of tight oil reservoirs varies from bulk fluid flow. The Darcy law is widely accepted to model creeping flow in petroleum reservoirs. However, traditional reservoir modeling approaches fail to account for the sub-micron mechanisms that govern fluid flow. The accuracy of tight oil reservoir simulators has been improved by incorporating the influence of sub-micron effects. However, there are still factors that affect sub-micron fluid mobility that need investigation. The influence of a chemical potential gradient on fluid flow in sub-micron pores was modeled by solving Darcy and the transport and diluted species equations. The findings indicate that when a chemical potential gradient acts in the opposite direction of a hydraulic pressure gradient (reverse osmosis), there exists a limiting pressure threshold below which a non-linear flow pattern deviating from the Darcy equation is observed. Furthermore, the simulation based on tight reservoir pore parameters shows that when the effect of a chemical potential gradient is added, the resultant flux is 8–49% less. Hence, including the effect of the chemical potential gradient will improve the accuracy of sub-micron pressure dynamics and flow velocity.

Microseismic monitoring is a useful enabler for reservoir characterization without which the information on the effects of reservoir operations such as hydraulic fracturing, enhanced oil recovery, carbon dioxide, or natural gas geological storage would be obscured. This research provides a new breakthrough in the tracking of the reservoir fracture network and characterization by detecting the microseismic events and locating their sources in real-time during reservoir operations. The monitoring was conducted using fiber optic distributed acoustic sensors (DAS) and the data were analyzed by deep learning. The use of DAS for microseismic monitoring is a game changer due to its excellent temporal and spatial resolution as well as cost-effectiveness. The deep learning approach is well-suited to dealing in real-time with the large amounts of data recorded by DAS equipment due to its computational speed. Two convolutional neural network based models were evaluated and the best one was used to detect and locate microseismic events from the DAS recorded field microseismic data from the FORGE project in Milford, United States. The results indicate the capability of deep neural networks to simultaneously detect and locate microseismic events from the raw DAS measurements. The results showed a small percentage error. In addition to the high spatial and temporal resolution, fiber optic cables are durable and can be installed permanently in the field and be used for decades. They are also resistant to high pressure, can withstand considerably high temperature, and therefore can be used even during field operations such as a flooding or hydraulic fracture stimulation. Deep neural networks are very robust; need minimum data pre-processing, can handle large volumes of data, and are able to perform multiple computations in a time- and cost-effective way. Once trained, the network can be easily adopted to new conditions through transfer learning.