The article discusses the prospects of productive strata of deposits of the pre-Jurassic complex of Western Siberia for searching, forecasting and involving in the development. Some features of the geological structure of Triassic volcanic-sedimentary deposits and formations of the Paleozoic basement have been identified within the pre-Jurassic complex. The construction of digital geological models of Triassic and Paleozoic formations, which are fundamentally different in the geometry of the internal structure of the hydrocarbon deposits discovered in them, is based on 3D seismic information and its interpretation. In deposits of Triassic and Paleozoic sediments, significant differences in the composition and filtration-capacitive properties of rock reservoirs have been recorded; the principles for creating the structural frameworks of their digital models are also different. 3D seismic data makes it possible to correlate the Triassic strata and identify individual cycles, the boundaries and geometry of which are used to create digital three-dimensional geological models. When creating models of hydrocarbon deposits in Paleozoic formations confined to basement high areas, based on the interpretation of 3D seismic survey materials and analysis of seismic attributes, in combination with information on core, petrophysics and well testing, a forecast is made for the development of areas and strata of increased fracturing of rocks, which are the main inflow strata in production wells. Using the example of individual developed deposits confined to the pre-Jurassic complex in the fields of Western Siberia, the actual results of integrating geological and geophysical information with 3D seismic data are shown. Based on these results not only an assessment of the geological reserves of hydrocarbons was carried out, but also hydrodynamic calculations were conducted to perform history matching, as well as recommendations were made for the locations for drilling wells, their geometry and test strata. The accumulated experience in exploration and development of productive deposits of the pre-Jurassic complex allows us to assess the geometry and features of objects of interest in this interval, select the most informative methods for their mapping and methods for creating digital geological models that are the basis for designing the development.

   The study of terrigenous Jurassic deposits in the Vilyuy syneclise has become increasingly important. This is because the fields contains shallow gas reserves and were first discovered in anticline traps during the 1960^s. Renewed geological exploration in this region is now providing valuable new geological and geophysical data.

   The aim of research is to select exploration criteria for locating hydrocarbon deposits in the territory.

   Seismic exploration is a leading method of research. Based on this, an analysis is conducted on the characteristics of hydrocarbon gas deposit formation and distribution within the early Jurassic stratigraphic complex of the Vilyuy oil and gas Vilyuy oil and gas bearing region. In particular, the Khapchagay megaswell has been analyzed. The study has identified the Nedzhelinskoye local uplift as an inversion structure. A dynamic anomaly of the "bright spot" type is observed at the roof of the productive lower Jurassic deposits of the Kyzylsyr suite in the central block. Well tests confirmed the difference in hydrocarbon fluid saturation between the western and eastern parts of the Nedzhelinskaya structure. The presence of dynamic anomalies of inversion type can be seen as an exploration indicator for hydrocarbon accumulation zones in the terrigenous deposits of the Vilyuy syneclise. This is a practical significance of the work.

   Forecasting the phase-genetic types of hydrocarbons is the most important stage in modelling geological hydrocarbon systems. This is because accurate qualitative and quantitative assessment of potential reserves is necessary for making informed investment decisions in exploration activities. Improving the precision of qualitative and quantitative oil and gas forecast can help reduce geological risks in asset development and increase investment efficiency. Therefore, technologies that allow modelling of hydrocarbon systems are becoming ever more important.

   This study aims to explore the key geological and geochemical aspects of hydrocarbon system modelling.

   Hydrocarbon systems refer to interconnected elements and geological processes influencing hydrocarbon formation in sedimentary basins. The study emphasizes that the "oil window" concept, or the theory of hydrocarbon formation phases, provides the theoretical basis for hydrocarbon systems. This concept consistently encompasses the entire history of sedimentary basin development, the evolution of thermal and thermobaric regimes in sedimentary deposits, the transformation of organic matter, hydrocarbon generation, migration, accumulation, and preservation processes. Using the results of modelling of hydrocarbon systems to evaluate geological risks and the economic impact of exploration activities can be a powerful instrument for companies that make investment decisions in geological exploration.

   The constantly growing need for fresh water in the Shaim oil and gas region necessitates the exploration of new areas and the development of measures to improve the operation of water intakes. To predict the quality of groundwater, which is an operational parameter of the aquifer aquifer, it is needed up-to-date information on the chemical composition of ground-water.

   The aim of this study to identify main spatial-temporal regularities of the state of the chemical composition of groundwater of the oligocene aquifer in the Shaim oil and gas bearing area, compared to average values from adjacent areas.

   Research methods include the systematization of laboratory data on water samples, analysis of groundwater chemistry, and mapping of the main spatial-temporal patterns in component concentration changes. The average values of chemical composition indicators in the studied area are similar to those of neighbouring areas, with some indicators exceeding drinking water standards. Similarities are observed in the variability of the chemical composition, spatial patterns of changes in most chemical indicators, and the presence of areas with extreme values. Current data on the chemical composition of the oligocene aquifer ground-water, including key characteristics and spatial-temporal trends presented in distribution maps, can be used in designing groundwater intakes and forecasting long-term groundwater quality.

   Current core plugs preparation standards for laboratory studies are developed for water-wet reservoirs and do not consider other types of wettability. Researching how different core preparation methods affect laboratory studies and wettability changes is relevant and important for petrophysical reserve estimation. Preconditions of doing this work was the question how core plugs impacts wettability changes in carbonate formations from several Eastern Siberian fields. The article describes the SCAL results of measuring the USBM wettability on core plugs at various states: a core plug with preserved saturation (before extraction), a core plug after extraction, and a core plug after wettability restoration. The authors showed how each of the stages of core plugs preparation affects wettability changes in rocks, the study of which is complicated by strong diagenetic changes: uneven salinization, bituminization, and anhydritization. The most accurate results were obtained from core plugs with preserved saturation. Overall, wettability of core plugs shifted towards a more hydrophilic state after extraction, but extraction did not fully change wettability from hydrophobic to hydrophilic.

   The prerequisites for the study are the calculation results for the cycling process, where carbon dioxide is proposed as the injection agent into the Achimov formations instead of dry gas, with the goal of increasing the condensate recovery factor.

   The work is focused on the efficiency assessment of carbon dioxide reinjection technology and reducing carbon footprint at a late stage of field development.

   The research object is the Аch_3-4 formation within the Novo-Urengoy license area of the Urengoy field.

   The leading method to identify this problem is the results of the full-scale composite dynamic model in the ECLIPSE 300 format.

   The model takes into account the history of field development on depletion. The articles deals with two schemes for injecting carbon dioxide into the formation. In the first scheme, pure carbon dioxide is injected in a closed-loop system, but carbon neutrality through storage is not achieved. In the second scheme, carbon dioxide is injected using reinjection technology. Once injection begins, gas production stops. Only the condensate separated from the formation gas during low-temperature separation is sold and sent for further processing. After allocation of the condensate, the mixture of natural gas and carbon dioxide, in a specific proportion, is sent to the compressor station for reinjection into the formation in a gaseous state. Injecting pure carbon dioxide achieves a condensate recovery factor similar to that of gas injection with a 30 % carbon dioxide mixture. However, this option is less economically viable compared to the base and other scenarios due to high capital costs for upgrading the existing gas processing equipment (requiring the construction of an amine treatment unit). With carbon dioxide injection using reinjection technology, in addition to recovering extra condensate that had condensed during natural depletion, a reduction in the carbon footprint is also achieved. To maximize the condensate recovery factor, the optimal concentration of carbon dioxide in the injection mixture has been determined. The optimal timing for the start of injection was identified to maximize gas recovery. Economic efficiency is expected from the additional recovery of condensate trapped in the reservoir and from achieving carbon neutrality through the monetization and storage of carbon dioxide.

   Gas pipelines, whether main, field, or urban, often operate in non-stationary modes. Changes in the operating modes of pumping stations, equipment start-up and shutdown, an in-line sampling or pumping and various factors are causes of instability of pressure, velocity, gas flow rate. Main pipelines are complex engineering systems with the pipeline itself being the main element. Fail-safety is the major factor in the operation of this system. Ensuring operational safety requires studying the movement regimes of the transported medium, particularly study of the dynamics of pressure during
start-up or shutdown and at specific extraction points.

   This article aims to build a mathematical model and study the pressure dynamics in a gas pipeline with a sampling point.

   The theory of non-stationary motion liquid in round pipes has been strongly developed in the works of I. A. Charnyj. These works consider a large complex of engineering tasks taking into account viscous properties of the transported medium and pipe resistance in the hydraulic approximation. In the article on the basis of I. A. Charnyj's researches on the motion of real liquid in circular pipes the equation in partial derivatives of hyperbolic type is compiled. The equation describes the unsteady pressure of a horizontal section of gas pipeline with an extraction point. Using the Dirac delta function allows the formulation of the problem in the form of a single equation. Pressures are set at the ends of a given section, and the initial velocity is related to the Dirac delta function. By applying the finite Fourier sine transform, the partial differential equation is transformed into an ordinary differential equation and solved. Solution of equation is vision of a solution to the initial task. Inverse transform formulas based on Fourier theory allowed us to proceed to the solution of this task. Explicit dependences for the dynamics of unsteady pressure are obtained. The qualitative analysis of the formulas indicates the wave motion of a medium during the initial phase of operation, transitioning into a stationary mode after a brief period. The duration of the transition period depends on factors such as the hydraulic resistance coefficient and the velocity of the transported medium. Coefficient of hydraulic resistance and the velocity of the transported medium are the main factors. An example is considered for a horizontal section under isothermal flow conditions. With assumed numerical parameters, the transition to a stationary state occurs approximately 17 minutes after the process begins, as illustrated in the graphs provided in the article. Engineers can use mathematical models of the liquid and gas motion through pipes in the design of pipelines, as well as in solving tasks that arise during their operation. These tasks include monitoring the condition of the pipeline system, optimizing the operation, accumulation capacity estimates and others.

   This article examines the problem of dynamically interpreting seismic data using machine learning models, which include Extremely Randomized Trees (Extra Trees), Gradient Boosting (GB), and Adaptive Boosting (AdaBoost) for the given problem. The study analyzes some existing solutions of the problem and describes the advantages of these machine learning models. Accuracy is estimated using the root mean square error metric. The authors found that dynamic interpretation and prediction of seismic data using these machine learning methods had not been extensively explored in research on related topics, which became the main focus of the study. The article formalizes the use of the mentioned models and highlights features and advantages for the given problem. Several common machine learning methods were investigated to find functional relationships between input parameters. Computational experiments were conducted to evaluate their applicability and compare the algorithms. The results show that the Extra Trees method is the most suitable for practical use for the given problem, as it demonstrates the highest accuracy in determining functional relationships and dynamic interpretation.

   In the oil and gas industry, the measured parameters during oil and gas production are often affected by noise, which contributes to complex and non-monotonic dynamics. This makes manual analysis and interpretation extremely difficult.

   Therefore, this article aims to develop an algorithm capable of identifying and removing noise (signal changes without a clear cause) in the production parameters of well operation.

   The article examines data smoothing methods, including moving average, exponential smoothing, Kalman filter, Wiener filter, Savitzky-Golay filter, Fourier transform, and wavelet transform. The authors identified advantages and limitations. An alternative approach is proposed, combining machine learning methods with standard data filtering tools. The developed algorithm restores the true dynamics of well performance metrics and filters out and smooths noise related to technical malfunctions. The novelty of the algorithm lies in using an LSTM neural network to extract the trend component from noisy dynamics, taking into account events occurring at the well itself as well as events happening at surrounding wells.