Both steam and hot water flooding of high-viscosity oils in the presence of swelling clays are difficult methods for producing oil efficiently because of potential formation permeability reduction. This paper pertains to heavy oil recovery from the East Moldabek formation where the oil API gravity is about 22 and is inundated with swelling clays. To achieve this, we used the Intersect^TM reservoir simulator to compare oil recovery economics using both hot water and steam injection as a function of steam cycle duration, temperature, and steam dryness. We also studied clay swelling in the East Moldabek formation where clay poses a significant challenge due to its impact on permeability reduction. In this research, we developed an equation based on experimental data to establish a relationship between water mineralization and permeability in the East Moldabek formation. The equation provides valuable insight on how to mitigate clay swelling which is crucial for enhancing oil recovery efficiency—especially in sandstone reservoirs. Our modeling studies provide the recovery efficiencies for salinities of the hot water EOR versus cyclic steam EOR methods in a formation containing swelling clays. Specifically, the reduction in formation permeability as a function of the distilled water fraction is the controlling parameter in hot water or steam flooding—when the formation water mixture becomes less saline, oil recovery decreases. Our research shows that clay swelling can significantly impact cyclic steam stimulation outcomes, potentially reducing its effectiveness, while hot water flooding may offer a more cost-effective and operationally feasible solution in formations where clay swelling is a concern. Economic analysis reveals the potential for achieving an optimal favorable condition for hot water injection. Therefore, this paper provides a guideline on how to conduct thermal oil recovery for heavy oils in fields with high clay content such as the East Moldabek deposit.

The Shu-Sarysu Basin in central-southern Kazakhstan remains one of the underexplored gas-prone provinces, with 12 discovered gas fields including Amangeldy (884 Bcf) and Pridorozhnoye (225 Bcf). In the context of global energy transition, such basins require integrated geological assessment to constrain exploration potential. Historical studies within the region were spatially limited and prematurely discontinued, resulting in fragmented datasets and a lack of modern interpretation. This review reassesses published geological data within a petroleum systems framework, applying contemporary geodynamic and stratigraphic concepts. Analysis shows that tectonostratigraphic evolution of the basin during Devonian–Permian time (390–250 Ma) favored formation of mature, gas-prone systems within structurally compartmentalized troughs, with effective source, reservoir, and seal configurations. Building on these findings, a three-tier classification of exploration zones is proposed based on system maturity, trap integrity, and gas shows, reflecting geological success probability. This provides a basis for prioritizing future exploration despite limited seismic and drilling coverage in many areas. Recommended priorities include digitization of archival data, structural modeling, modern geochemical and diagenetic analysis, and focused evaluation of promising areas to support future exploration.

Anthropogenic CO₂ emissions are a major driver of climate change, highlighting the urgent need for effective mitigation strategies. Carbon Capture, Utilization, and Storage (CCUS) offers a promising approach, particularly through CO₂-enhanced gas recovery (EGR) in shale reservoirs, which enables simultaneous hydrocarbon production and CO₂ sequestration. This study employs a numerical simulation model to compare two injection strategies: CO₂ flooding and huff-and-puff (H&P). The results indicate that, without accounting for key mechanisms such as adsorption and molecular diffusion, CO₂ H&P provides minimal improvement in methane recovery. When adsorption is included, methane recovery increases by 9%, with 14% of the injected CO₂ stored over 40 years. Incorporating diffusion enhances recovery by 19%, although with limited storage potential. In contrast, CO₂ flooding improves methane production by 26% and retains up to 94% of the injected CO₂. Higher storage efficiency is observed in reservoirs with high porosity and low permeability, particularly in nano-scale pore systems. Overall, CO₂ H&P may be a viable EGR option when adsorption and diffusion are considered, while CO₂ flooding demonstrates greater effectiveness for both enhanced gas recovery and long-term CO₂ storage in shale formations.

The purpose of this study is to evaluate the feasibility of different oil fields in Kazakhstan for Carbon Capture, Utilization, and Storage (CCUS) projects using advanced algorithms in Python. Using automated methods, the approach greatly simplifies and accelerates the selection process, allowing efficient analysis of large data sets. Taking into account key geological and operational parameters, with particular emphasis on the importance of the Dykstra-Parsons coefficient, the study presents a comprehensive ranking system for evaluating reservoir suitability. This coefficient is critical to accurately assess the fluid displacement efficiency, which significantly influences the selection of candidates for Enhanced Oil Recovery (EOR). The results show that the inclusion of the Dykstra-Parsons coefficient improves the accuracy of field evaluation by accounting for key reservoir heterogeneity factors along with conventional properties. The comparative analysis shows that this approach provides more reliable field selection compared to the existing methods that do not consider this parameter, thereby improving the efficiency of CO2 storage projects.

The composition of hydrocarbon systems plays an essential role in ensuring reliability in field operation processes, the design of oil onshore facilities, and the availability of reliable information about the PVT properties of systems. In this article, experimental and numerical methods are used to obtain data on the component composition of the extracted oil products and their PVT properties. These methods help obtaining a detailed composition and phase state of the hydrocarbon system. The numerical delumping method is utilized in this article. It is worth noting that all calculations were made on the basis of data on the component composition of Kazakhstani oil samples. The results and comparative evaluation show that the proposed delumping mix-method has excellent consistency with the data obtained with experiments and with the Nichita’s analytical approach. In the future, the results on the detailed composition can improve and link the compositional modeling of the reservoir with the modeling of surface structures.

This study presents an in-depth investigation into the fusion properties, specifically the melting point and solid-state transition temperature, of crude oil samples from five distinct fields in Kazakhstan. These properties are critical for understanding and predicting wax precipitation, which poses significant challenges in the petroleum industry, particularly in cold climates where wax deposition can obstruct pipelines. Using advanced analytical techniques, including gas chromatography and pour point testing, new correlations were developed to more accurately predict these fusion properties for Kazakhstani crude oil. The proposed correlations outperform the existing models, offering closer alignment with the experimental data across a wide range of hydrocarbon compounds. The novelty of this research lies in its tailored approach, which integrates experimental data, existing predictive models, and Python programming to develop a region-specific solution for Kazakhstani crude oil. By addressing the limitations of generalized models, the study highlights the importance of adapting predictive frameworks to specific oil compositions and regional conditions. These findings have substantial implications for the optimization of crude oil transportation and storage in cold environments, where wax deposition is a prevalent issue. The improved accuracy of the proposed correlations enables better predictability of wax-related flow assurance problems, contributing to more efficient and safer operations in the oil and gas industry. Furthermore, this work establishes a robust methodological framework that can be extended to other crude oil types and operational scenarios, paving the way for advancements in predictive modeling of wax precipitation under diverse environmental conditions.