Vol 8, No 3 (2026)

Background: The geological structure of the Jurassic deposits in the Zhetybay East field is characterized by pronounced facies heterogeneity and complex spatial variability of reservoir properties driven by changes in depositional environments. The insufficient understanding of formation patterns and spatial distribution of facies bodies limits the accuracy of geological modeling and the prediction of productive zones. The study and interpretation of facies settings enable a detailed characterization of depositional environments, hydrodynamic regime parameters, and paleoenvironmental conditions, which form the basis for stratigraphic correlation, paleogeographic reconstructions, and applied geological predictions. In this context, there is a need for detailed sedimentological analysis of core material followed by integration of the obtained results with well log data and seismic survey data in order to improve the reliability of interpretation and refine the geological model of the field.

Aim: The aim of this study is to determine the facies environments of the Zhetybay East field based on core analysis results.

Materials and Methods: Historical data for each well in the field and laboratory results of core material analysis were used as the main sources of information. Core interpretation was performed using a sedimentological approach, including the analysis of lithological composition, textural and structural features, and diagnostic sedimentary structures. Based on the integrated interpretation of core and well data, facies analysis was carried out with the identification of genetic types of deposits and the reconstruction of their formation conditions.

Results: As a result of the sedimentological analysis of core material, a facies interpretation of the Jurassic productive deposits of the Zhetybay East field was performed. Based on the study results, the main depositional environments were identified and a conceptual sedimentological model of the productive interval of the section was developed.

Conclusion: The obtained results allow refinement of the geological model and improvement of the prediction of productive horizon distribution. In addition, they contribute to increased reliability of stratigraphic correlation. Overall, the data can be used to substantiate directions for further geological exploration and pilot production studies.

Background: The challenging geological and thermobaric conditions of the Western Kazakhstan oil fields impose stringent requirements on cement slurry systems, while standard Portland cement formulations frequently fail to provide adequate zonal isolation over the well’s production lifecycle.

Aim: This study aims to identify the key limitations of conventional cement compositions and substantiate the feasibility of introducing SiO₂, Al₂O₃ nanoparticles and carbon nanotubes to improve the rheological and mechanical properties of cement slurries.

Materials and Methods: A comparative analysis was conducted using actual cementing program data from a well at the North Uaz field and a well at the Western Prorva field, evaluating slurry density, viscosity, thickening time, fluid loss, and compressive strength.

Results: Standard formulations exhibited fluid loss exceeding acceptable thresholds and compressive strength below the required minimums; nanomodified compositions demonstrated a fluid loss reduction of up to 40% with SiO₂ addition and a 15–25% increase in early compressive strength with Al₂O₃.

Conclusion: The introduction of nanocomposite additives is technically justified for overcoming the limitations of conventional cement systems under Western Kazakhstan field conditions.

Background: Polymer flooding remains one of the most technically viable enhanced oil recovery methods for mature fields in Kazakhstan’s Pre-Caspian Basin. Predicting its sweep efficiency in heterogeneous carbonate reservoirs is challenging due to complex pore-throat geometry, high-salinity formation waters, and pronounced permeability contrasts. Conventional reservoir simulation is computationally prohibitive for real-time operational decisions and probabilistic uncertainty analysis.

Aim: This study develops and validates a hybrid surrogate framework integrating gradient boosting regression with physics-informed constraints derived from polymer transport theory to predict incremental oil recovery factor and injectivity ratio for polymer flooding in carbonate formations of the Pre-Caspian Basin.

Materials and Methods: A dataset of 412 polymer flooding operations was compiled from published literature, Society of Petroleum Engineers (SPE) technical reports, and internal field data from three Kazakhstani assets: Tengizchevroil, Karachaganak Petroleum Operating, and the Uzen field operated by Ozenmunaigas. Eighteen reservoir and fluid parameters were used as input features. Gradient boosting regression was trained using a stratified 80/20 split with five-fold cross-validation. Physics-informed penalty terms derived from Darcy-scale polymer transport equations were embedded in the loss function to ensure physical consistency. Model performance was benchmarked against artificial neural networks, support vector regression, and full compositional simulation.

Results: The physics-informed gradient boosting model achieved a coefficient of determination R² = 0.924 and root mean square error of 2.31% on the held-out test set for recovery factor prediction, outperforming artificial neural networks (R² = 0.891) and support vector regression (R² = 0.857). The physics penalty reduced physically inconsistent predictions by 78%. SHapley Additive exPlanations (SHAP) analysis identified the permeability variation coefficient, polymer concentration, and formation water salinity as dominant predictors. Surrogate estimates agreed with full compositional simulation within ±1.8% absolute for the Uzen carbonate pilot.

Conclusions: The proposed surrogate model provides rapid and reliable predictions of polymer flooding performance suitable for field-scale integration, substantially reducing computational burden for enhanced oil recovery screening and optimization in heterogeneous carbonate reservoirs of Kazakhstan.

Background: This article presents the results of laboratory investigations of newly developed high-performance formulations intended for the removal of asphaltene–resin–paraffin deposits (ARPD) formed during oil production and transportation. ARPD are solid organomineral deposits composed mainly of paraffins, asphaltenes, resins, and mechanical impurities that accumulate on the internal surfaces of oilfield equipment, thereby reducing its productivity and causing operational problems. This issue is particularly relevant for the oil fields of the Mangystau region, where crude oils are characterized by a high paraffin content.

Aim: To develop an effective, readily available, and low-toxicity formulation for the dissolution and removal of solid ARPD.

Materials and methods: A comprehensive set of laboratory studies was conducted to evaluate the dissolving, removing, and cleaning performance of the developed formulations in accordance with the approved methodology. The composition of ARPD samples was investigated in accordance with the applicable regulatory documents (GOST).

Results: Laboratory experiments conducted at 25°C and 60°C demonstrated that temperature significantly affects both the dissolution rate and the extent of ARPD removal. Formulations containing more than 6 wt% surfactants achieved ARPD dissolution and removal efficiencies of up to 100%. Even at lower temperatures of 25°C, ARPD removal efficiency exceeded 98%, indicating that the developed formulations are suitable for application across a wide temperature range.

Conclusion: Within the framework of this study, the composition of paraffinic-type ARPD samples collected from different sections of the investigated field was analyzed. Based on the obtained data, hydrocarbon-based formulations were developed using a pentane–hexane fraction (PHF) as the solvent, with the addition of nonionic and anionic surfactants (OP-10 and sulfonol). These components provide a synergistic effect in dissolving and dispersing the deposits. The proposed formulations demonstrated high efficiency in the destruction and removal of solid deposits within a short period of time, contributing to reduced operating costs and improved operational reliability of oilfield equipment.

Background: Ensuring employees occupational safety at the oil and gas industry facilities remains a relevant objective, as far as traditional monitoring methods for the use of Personal Protective Equipment (PPE) are based on manual visual inspections and are susceptible to human factor. Most existing computer vision systems are limited to detecting only a small number of 2–6 classes of PPE categories and to verifying the anatomical consistency between detected protective equipment and the corresponding body parts of employees.

Aim: Developing and validating a digital monitoring method for compliance with PPE use requirements based on neural network architectures, integrating algorithms for object detection, human pose estimation, and anatomical matching of PPE elements in real time mode.

Materials and methods: A unique dataset of 16352 images (after augmentation) containing 13 object classes, including 6 types of PPE and 6 negative classes was created for training the model. The YOLOv8 model was used for object detection, and HRNet for human pose estimation. A two-tier video stream processing architecture was implemented, combining object tracking (BoT-SORT), spatial and anatomical matching and TensorRT quantization to improve system performance.

Results: During the training phase, the YOLOv8 model achieved Precision = 0.98, Recall = 0.97, and F1 = 0.94. When testing the developed system on 16 video files obtained from industrial sites, the system achieved a precision of 96.58%, a recall of 68.48%, and an F1 score of 0.8014. Detecting small objects (gloves) in cropped images improves detection efficiency by 2-3 times compared to full-frame processing.

Conclusion: The developed approach, combining the YOLOv8 and HRNet models and an anatomical matching algorithm, provides effective digital monitoring of compliance with PPE requirements in real-world production conditions. The obtained results confirm the potential of its application in the development of intelligent industrial safety monitoring systems at industrial facilities.

Background: The production of high-octane gasoline meeting stringent environmental requirements remains an important task for petroleum-refining industries, including Kazakhstan. At the same time, artificial intelligence and digitalization are increasingly applied to support process design and fuel-quality prediction.

Aim: To design a combined isomerization-catalytic reforming system for producing AI-92, AI-95, and AI-98 gasoline grades using an AI-based model for gasoline-quality prediction.

Materials and Methods: Three commercial gasoline grades (AI-92, AI-95, and AI-98) were characterized by pycnometric density measurement, capillary viscometry, and thermogravimetric analysis with differential thermal analysis. Linear Regression, k-Nearest Neighbors, and Decision Tree models were used to predict research octane number (RON). Kinetic models were developed for light-naphtha isomerization and catalytic reforming, together with material and energy balance calculations, DIP-column design, and reformate-yield estimation.

Results: The Decision Tree model provided exact RON predictions for the three tested grades (MAE = 0.00; R² = 1.00). The isomerization unit with DIP recycle produced an isomerate with RON 87.4, while the catalytic reforming unit produced reformate with RON 98.5 at 500°C and a liquid yield of 85.4 vol%. The calculated blended RON values were 92.1, 95.3, and 98.2 for AI-92, AI-95, and AI-98, respectively.

Conclusions: The combined process configuration enables the production of the full range of studied gasoline grades by adjusting the proportions of isomerate and reformate in the blending pool. The AI-based quality model can be used as a supporting tool for defining target octane specifications during process design.

Background: The expiry of the design operating life of the production facilities at Field X requires an assessment of the cost-effectiveness of capital expenditures (CAPEX) and operating expenditures (OPEX) associated with the modernization of process facilities, as well as the development of recommendations for optimizing investment decisions, considering the forecast decline in production and the prospects for further field development.

Aim: To assess capital and operating expenditures associated with the modernization of the Phase 2 process facilities in the post-contract period.

Materials and methods: The study is based on data from the Field X Development Project and Field Development Analysis (FDA). As part of the work performed, a model of the main Units 1 and 2 was developed using QUE$TOR software to compare the cost of their potential replacement with the results presented in the previously approved project documentation. The modelling was performed based on the current production capacities of the units.

Results: The option involving the extension of the production plateau using a single liquid hydrocarbon (LH) process train with a capacity of 2.8 million tonnes per year was selected as the most optimal, based on the duration of maintaining the production plateau duration (7 years). Reducing the wellhead pressure of Unit 3 wells from a high to a medium level had a significant impact on increasing LH production, extending the production plateau by an additional 5 years.

Conclusion: The proposed concept was recognized as technically feasible and economically justified. Infrastructure optimization makes it possible to reduce capital expenditures by USD 2.97 billion, achieve additional LH production of approximately 8 million tonnes, extend the production plateau by 12 years, and extend the gas export period by 7 years.

Background: Oil and gas production facilities represent complex industrial systems that exert a significant anthropogenic impact on ambient air quality. The simultaneous presence of organized and fugitive emission sources, high density of technological infrastructure, and adverse meteorological conditions considerably complicate the processes of air pollutant dispersion. Therefore, the application of mathematical modeling techniques is of particular importance for predicting ambient air pollution levels and assessing the environmental impacts of industrial facilities.

Aim: The s tudy aimed to analyze the specific features of air pollutant dispersion modeling at oil and gas production facilities, identify the key factors affecting calculation accuracy, and assess the consistency between the results obtained from Maximum Permissible Emission (MPE) projects and verification calculations performed using the ERA-Air software package.

Materials and methods: The study utilized data from Maximum Permissible Emission (MPE) projects, atmospheric dispersion calculations, and verification modeling results obtained using the ERA software package for production facilities of Enterprises No. 1 and No. 2. The principal research methods included comparative analysis, mathematical modeling, spatial interpretation, and expert assessment of pollutant dispersion maps.

Results: The findings indicate that organized emission sources account for 60–70% of total emissions at oil and gas production facilities, while the contribution of fugitive sources may reach 20–30%. At the same time, low-level fugitive emission sources were found to have a substantial influence on the formation of ground-level pollutant concentrations. The highest concentrations were observed for nitrogen dioxide, hydrogen sulfide, soot, and benzo(a)pyrene. The modeling results demonstrated the decisive role of meteorological factors, particularly wind speed and atmospheric stability conditions, in determining pollutant dispersion patterns. The verification calculations confirmed a generally satisfactory agreement between the calculated and project-based concentration fields .

Conclusion: Air pollutant dispersion modeling is an effective tool for assessing the impact of oil and gas production facilities on ambient air quality. A comprehensive consideration of organized and fugitive emission sources, technological infrastructure density, and meteorological conditions significantly improves the reliability of modeling results. The obtained findings can be applied to support the establishment of sanitary protection zones, develop environmental protection measures, and enhance industrial environmental monitoring.