This paper presents the results of applying forced fluid withdrawal (FFW) technology to enhance oil recovery in fields located in Western Siberia. The authors expand upon the classical FFW approach originally developed by A. M. Shchelkachev, V. N. Mamedov, and G. G. Sarkisyan by proposing an integration of natural gravity forces and wave effects in natural reservoirs. The analysis, which utilizes artificial intelligence methods to process geological and production data, demonstrates the high efficiency of FFW during the late stages of development in highly water-flooded wells. Using the productive sediments of the Pokamasovskoye oil field as an example, the authors can observe an increase in total oil production rate of 15 to 57% at well water-cut levels ranging from 75 to 95%. Furthermore, the project's economic efficiency improved by 15 to 30 % due to the development of low-permeability layers. The results of this study meet SPE and API standards. The researches recommend them for designing reservoir stimulation systems for other fields in Western Siberia. The proposed model's error does not exceed 5% compared to field data.

One of the most pressing challenges of the oil and gas industry is the development of new and effective methods to enhance oil production. This study presents the results of experimental research into the properties of crude oils from the Samotlor field, utilizing electrothermal treatment methods. The author of this article conducted measurements using an experimental setup developed at the Branch of Industrial University of Tyumen in Nizhnevartovsk. Specifically, the author assessed the polarization properties, density, and viscosity of the oil while exposing it to thermal, electrostatic, and electromagnetic fields. The experiments revealed a reduction in both the density and viscosity of the oil when subjected to simultaneous thermal and electrostatic fields. An even more pronounced decrease in these properties was observed under the combined effect of thermal and electro-magnetic fields. The article gives explanations for the obtained results: increases motility of dipolar molecules, which leads to a reduction in intermolecular forces. In conclusion, the author noted a viscosity reduction of 100% or more, suggesting forecast for the same increase in oil production.

Achimov sequence sediments in northern West Siberia are a classic example of a complex-built reservoir. Traditional petrophysical interpretation methods often fall short for such reservoir due to high geological heterogeneity, which manifests in wide variety in mineral composition and reservoir properties. The main method for enhancing the reliability of geological interpretation of well log data (WLD) in these sediments is robust lithological typing of the rocks. In this article, the authors suggest an approach to lithological typing based on the development of a Volumetric-Component Model (VCM). In the first stage, the researchers built two VCMs. The number of components for each model is determined using two distinct sets of WLD: an extended set (including Gamma Ray, Neutron Porosity, Litho-Density, Elemental Spectroscopy) and a standard set (Gamma Ray, Neutron Porosity, Litho-Density). In the second stage, these VCMs serve as input data for configuring machine learning algorithms aimed at lithological typing of the rocks. This approach improves the accuracy of lithotype predictions in wells without core compared to the traditional statistical analysis performed directly on original well log curves.

This paper presents a systematic analysis of modern machine learning methods and their practical applications in solving key problems in petroleum geology and geophysics. This study discusses the advantages and limitations of major neural network architectures, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and deep feedforward networks (RNNs), and deep feedforward networks (DNNs). The authors of this paper paid special attention to the integration of various types and scales of data, ranging from seismic surveys to core samples and borehole geophysical measurements. The open-source machine learning platform, Orange, is highlighted as a very effective tool for geological data analysis and visualization tasks. Real-world examples illustrate how machine learning can significantly enhance interpretation accuracy, reduce time costs, and minimize subjective bias. The paper concludes that neural network technologies are transitioning from experimental tools to binding instruments for improving the economic efficiency of geological exploration activities.

Most oil and gas fields in the Russian Federation are currently in a late stage of development. This stage includes declining production rates, decreasing reservoir pressures, and in-creasing water cut, which in turn requires repair and isolation operations in production wells. Isolating the productive reservoir before eliminating behind-casing flow with a cement plug can lead to deterioration of reservoir properties and significantly increase well repair time. In recent years, packer plugs of various designs have been used to shut off the productive reservoir. The main advantage of such plugs is the ability to set them at the required interval within the well, regardless of deviation angles, and without losing cementing materials into the reservoir. The authors of this paper propose the use of dissolvable packer plugs for repair and isolation operations, as well as for eliminating behind-casing communication. The installation technology for these plugs is similar to that of conventional packer plugs. A key benefit is that dissolvable packer plugs can independently degrade when exposed to saline solutions that contain active chloride ions.

The depletion of readily accessible oil has increased the focus on recovering harder-to-access reserves. Consequently, the industry requires new, improved, low-cost, and efficient methods for developing reservoirs with complex geological structures. Cyclic waterflooding provides simple and inexpensive approach for enhancing oil recovery. Operators have been utilizing cyclic waterflooding since the late 1950s in several regions of Russia, including Western Siberia, the Republic of Tatarstan, the Samara Region, Perm Krai, and Krasnodar Krai. Today, researchers abroad study cyclic waterflooding most actively in China. In this study, we examine two modifications of cyclic waterflooding: (1) cyclic water injection through injectors and (2) asynchronous cyclic waterflooding, which involves the simultaneous operation of both injection and production wells. We also study cyclic forced liquid withdrawal through a producing well. The aim of this work is to analyze the behavior of these three cyclic waterflooding modifications under different geological conditions. We analyze how each modification influences oil-saturation patterns in a two-dimensional synthetic reservoir model. Researchers and engineers should consider these results when designing cyclic-waterflooding programs for real reservoirs with complex geology. Our study reveals that the location of low-permeability layers near injection and/or production wells significantly impacts the effectiveness of cyclic waterflooding. When such layers are situated close to the production wells, relying solely on cyclic injection provides minimal benefits. The results indicate that asynchronous cyclic waterflooding offers the highest efficiency.

Displacement characteristics offer a fast and efficient way to forecast recoverable oil reserves and evaluate the effectiveness of geological and technical interventions. Displacement characteristics helps significantly reduce the time and financial costs associated with constructing a three-dimensional hydrodynamic model. The authors of this paper introduce a probabilistic approach that extrapolates oil production dynamics using integral water-cut curves. This study com-pares the classical displacement-characteristics method with the probabilistic approach and assess their prediction accuracy. Also, they present the forecasting algorithm and analyze how the method performs at various water-cut levels at the end of the approximation interval. During the early stages of development, the probabilistic approach demonstrates higher accuracy than the traditional approach. Additionally, the authors outline criteria to help eliminate unrealistic extrapolated results when applying the probabilistic method.

In the modern economic paradigm of Russia, pipeline transport plays a strategically important role and serves as a key component of the national energy supply system. The crucial determinants of its operation are efficiency and reliability, which serve as system-wide criteria ensuring the stable functioning of the national economy. Oil transportation is a highly organized, multi-factor technological process. The high degree of dependence of this process on external and internal parameters makes it vulnerable. Therefore, even local failures in pipeline infrastructure may potentially lead to cascading disruptions and significant economic losses. This article details the design of mainline pumps – high-tech units that determine the reliability and uninterrupted operation of the linear of pipeline sections. The longevity of these units is influenced not only by the quality of maintenance but also by various technical, technological, organizational, and other factors. To monitor the technical condition of pumping units the authors of this article considered a control and diagnostic system based on the SKiD DVT43.20 system. Real-time data collected from installed sensors enables operators to identify extreme trends in pump operating parameters. Using the operational modes of a pump as a case study, the researchers processed performance data and constructed graphs, the interpretation of which contributed to the formation of conclusions about equipment operation. The authors propose a calculation method for determining operating parameters, which aids in planning subsequent technical maintenance actions.

The authors of this article consider a comprehensive approach to designing oil and gas infrastructure that aims to enhance reliability and extend the service life of these structures in harsh climatic conditions. The article cites data from aerodynamic simulations, based on which the re searches assessed wind flow distribution and identified potential zones for snow accumulation. This information is essential for predicting changes in permafrost soil conditions, helping to pre-vent foundation deformations and reduce operational risks. Using computational fluid dynamics (CFD) methods and software programs such as ANSYS and SolidWorks, the authors performed calculations to determine the distribution of static and dynamic wind pressures on structural surfaces and to identify turbulence areas. Within the framework of the modeling process the researches formulated hypotheses to characterize the identities of snow accumulation near oil and gas infrastructure. The analysis confirmed the positive effect and necessity of integrating aerodynamic simulation into the design process of oil and gas infrastructure to enhance the operational reliability of structures. The results highlighted key patterns in snow load redistribution and the effect of wind flows based on the relative positions of objects and the prevailing wind directions, as out-lined in the wind rose for the studied region. The authors see practical significance of this study in formulating recommendations for optimizing the placement of buildings and structures. According to their plan, these recommendations allow to minimize snow accumulation, demonstrated through specific case studies. In conclusion, the researchers suggest further study directions to validate the aerodynamic modeling data against actual snow accumulation at the sites, to effectively integrate these insights into the business processes of the oil and gas extraction industry.

This paper examines the challenges associated with maintaining stable oil pipeline performance in complex natural and climatic conditions. The aim is to analyze issues related to permafrost that influence the design, construction methods, and long-term operation of pipelines until they reach their limit state. The tasks: identifying the causes of pipeline deformation in areas of permafrost; reviewing protective measures that help maintain pipeline stability in cryogenic conditions; describing the physical processes behind seasonal cooling devices that keep thawed soils at stable subzero temperatures during the summer. The authors of this paper employ a systems-based engineering and geocryological approach that integrates the analysis of cryogenic soil structure, thermal regime, and filtration-migration processes during moisture phase transitions. This study reveals that the main problems in pipeline construction and operation within permafrost include permafrost degradation (thawing), frost heave, and slope processes that lead to uneven foundation settlement and create critical stresses in the pipeline wall. To ensure stable pipeline geometry, the authors recommend the following protective measures: polyurethane foam insulation to stabilize the thermal regime; seasonal cooling devices that operate during summer, movable supports made of cold-resistant steels with fluoropolymer-based bearing components to compensate for thermal displacements. Using operational data the authors can conclude that underground installation at a depth of 2.5 to 3 meters is a technically and economically sound solution for permafrost regions, ensuring safe operation for up to 30 years. At the same time, above-ground installation remains relevant in Arctic areas with highly ice-rich soils, where thaw depths can produce deformations exceeding 500 mm. This study achieves its aim by identifying key permafrost-related factors that influence pipeline design, construction methodology, and long-term operation.