International Journal of Engineering
Volume:38 Issue: 7, Jul 2025

Energy monitoring using statistical process control (SPC) methods makes it more straightforward to identify patterns and trends to decrease energy consumption more effectively. The literature review of energy consumption monitoring with SPC techniques generally focuses on the temporal aspect of variation. However, due to the spatial nature of energy data, enhancing these methods to incorporate temporal and spatial aspects would improve the accuracy of the diagnostic information, underscoring simultaneous detection of the time and location of changes. Thus, the main novelty of this work is the spatial modeling and spatiotemporal monitoring of electricity consumption. For this purpose, the study used actual electricity consumption data from eight western cities of Mazandaran province in the north of Iran for spatial modeling using spatial regression models and a geographically weighted regression (GWR) model. The prediction performance evaluation of spatial models showed GWR as an appropriate model, whose coefficients were monitored through a generalized likelihood ratio (GLR) chart in phase II. The GLR chart detected two changes in consumption, and its performance was confirmed based on the statements from electricity experts relying on meteorological information and floating population data. Furthermore, the performance of the GLR chart was evaluated using out-of-control average run length (ARL1) across three different scenarios. The findings indicate that the GLR chart can effectively detect any sizes of shifts (δ), ranging from 5% to 100% of the model's parameter value. Additionally, with larger values of δ, the ARL1 decreases, resulting in faster detection of changes in the model.

The surface layer of pavements, usually made of bitumen, is crucial for performance, especially in preventing rutting that affects pavement quality and lifespan. The performance of asphalt mixes in road construction largely depends on the bitumen used. Enhancing bitumen properties helps address the shortcomings of pure bitumen, thereby improving asphalt pavement performance, quality, and longevity. In this research, to enhance the properties of bitumen and asphalt mixes, the effect of using a nanocomposite modifier of polyurethane with hydroxyl-functionalized multi-walled carbon nanotubes (OH-MWCNTs) was investigated. First, isocyanate-terminated polyurethane prepolymer (NCO-PP) was synthesized and incorporated with OH-MWCNTs. According to Fourier transform infrared (FT-IR) analysis, the successful synthesis and also presence of nanoparticles in the matrix were confirmed. Next, based on thermogravimetric and tensile analysis, the nanocomposite containing 1.5 wt.% OH-MWCNT was chosen as the optimum composition. By performing rotational viscosity (RV), multiple stress creep recovery (MSCR), and dynamic creep tests, it was found that the asphalt based on modified bitumen samples had improved properties, with the polymer nanocomposite (PNC)-modified samples showing better results. Using the synthesized modifiers, not only the rutting resistance was improved, but the traffic rating was enhanced by 2 grades. Then, by performing the scanning electron microscopy (SEM) test and examining the morphology of the modified bitumen samples, a uniform distribution of the polymer nanocomposite in the entire matrix was observed. Thus, it could be expected that the application of nanocomposite modifiers in asphalt pavements can largely prevent damages resulting from rutting.

This article presents an analysis of the effectiveness of hydraulic fracturing for increasing hydrocarbon production, emphasizing the challenges and limitations of this method. Sustainable alternatives are explored, including waterless hydraulic fracturing technologies such as the use of carbon dioxide, liquid nitrogen, petroleum gas, and liquefied natural gas. A systematic analysis highlights their practical applications, advantages, and limitations. These methods are shown to reduce environmental impact and are suitable for use in water-scarce regions. However, their implementation is associated with accelerated equipment wear due to the effects of cryogenic fluids and high pressures, necessitating careful material selection and system design. Furthermore, attention is given to the issue of hydro- and gas-abrasive wear, which damages critical equipment components such as turbine blades and pump gears. The wear patterns are found to depend on the angle of impact of abrasive particles, underscoring the need for innovative solutions to mitigate these effects. The study's findings confirm the significant potential of waterless hydraulic fracturing technologies to enhance hydrocarbon production efficiency and highlight the importance of further research aimed at improving their economic and environmental sustainability. The proposed approaches contribute to advancing the oil and gas industry's transition to more efficient and sustainable practices.

Hybrid nanofluids (HNFs) and trihybrid nanofluids (THNFs) have many industrial, engineering, and medical applications since they increase heat transfer rate. Because of these applications of THNFs, in the present problem the 3D hydrodynamic flow over exponentially stretching surfaces in a magnetohydrodynamic (MHD) field for the base fluid of water and nanoparticles of copper, aluminum oxide, and titanium oxide are analyzed. In this study, a new mathematical model for enhancing heat transfer using THNFs in light of the exciting potential of THNFs is presented. This comparative model is considered for the exponential flow of a new model in the presence of a magnetic field. Partial differential equations (PDEs) are derived using continuity, momentum, and energy equations. Numerical results are obtained using the bvp-4c algorithm in MATLAB software. The major outcomes disclosed that the Nusselt number, which measures the rate at which heat is transferred, is higher in ternary hybrid nanomaterial when compared to hybrid. The value of the Nusselt number for ternary hybrid nanofluid is 38.4% higher than for hybrid nanofluid. The highest value of the Nusselt number for the ternary hybrid nanofluid, which occurs at Parantel number of 8.2, was found to be 1.5090. The heat transfer rate of the tri-hybrid nanofluid is also superior to that of the hybrid nanofluid and the traditional nanofluid. An increase in the A and β also results in a decrease in the temperature. Furthermore, raising the values of Ha and β causes the skin friction coefficient to increase. Besides, the Nusselt number (Nu) increases due to an increase in β, A, Pr, and Bi. Comparing the graphs show that the temperature and Nu have a higher rate of increase in THNFs (Cu-Al2O3-TiO2/H2O) than in NHFs (Cu-Al2O3/H2O).

Handling time-varying uncertainties in chemical reactor dynamics poses a significant challenge when designing reliable model-based controllers. This study introduces a novel optimization-based approach to update a nominal dynamic model, initially selected for a continuous stirred tank reactor (CSTR). In this approach, a complementary term is calculated online using the system output. This term is then appended to the state space model of the system to compensate for time-varying uncertainties. After confirming the accuracy and reliability of the constructed model, it is used to design a reliable model-based controller for the nonlinear CSTR. The efficiency of the designed adaptive controller, which upgrades itself to actual conditions, is compared with a sliding mode controller in the presence of perturbations. Additionally, the saturation of the control input is modeled using the framework of a constrained optimization problem solved by Karush-Kuhn-Tucker (KKT) Theorem. The constrained stability of the controller is analyzed by the Lyapunov method. Comparative simulation results show the superior performance of the proposed controller by accurate estimation of time-varying uncertainties under saturated control input.

This study investigates the mechanical properties of aluminum matrix syntactic foams fabricated using perlite, LECA, and pumice aggregates through the vacuum casting method. The process involved packing the aggregates in a mold, applying a multi-step filling procedure with vibration, and using a vacuum pressure of -1 bar and a heating temperature of 560°C to achieve proper infiltration and distribution of the aluminum melt. The study found that the density of syntactic foams varied based on the type of aggregate particles used: perlite (0.24 g/cm³), LECA (0.55 g/cm³), and pumice (1.30 g/cm³). Quasi-static compressive tests revealed that higher density foams exhibited uniform deformation and higher energy absorption efficiency, with porosity values of 63% for perlite, 51% for LECA, and 33% for pumice. The energy absorption efficiency (η) at 50% strain was 85% for perlite, 78% for LECA, and 70% for pumice. The compressive properties and energy absorption capabilities of the foams increased with density, making them suitable for various protective applications.

The need for robots in corn farming arises from the complexity and labor-intensive nature of tasks such as planting, weeding, harvesting, and monitoring. Robots offer precision, efficiency, and data-driven decision-making, addressing challenges in resource management, crop health, and productivity to meet the demands of modern agriculture. In this paper, the current landscape of robotics in corn farming, exploring various robotic systems, their functionalities, and their potential impacts on agricultural practices were studied. Additionally, analyzing the benefits and challenges associated with the adoption of robotic technology in corn farming, considering factors such as cost, compatibility with existing infrastructure, and regulatory considerations. Furthermore, the future directions and opportunities for research and development in the field of agricultural robotics, emphasizing the need for interdisciplinary collaboration and innovation to maximize the benefits of robotic technology in corn farming and contribute to sustainable food production are being discussed. The primary focus of this article is to analyze the components and design features of robots employed in our corn fields. This analysis not only serves as a comparison tool for designers but also encourages the development of more diverse designs. The structure of robots in corn farming plays a crucial role in advancing agricultural practices by boosting efficiency, precision, adaptability, data collection capabilities, environmental sustainability, and safety standards.

The automated detection of microstructural defects in additively manufactured Ti6Al4V materials presents significant challenges due to the lack of comprehensive datasets and the variability of defect types. This study introduces a novel methodology for addressing these challenges by developing a Microstructural Defect Dataset (MDD) specifically tailored for scanning electron microscopy (SEM) images. We trained and evaluated multiple YOLOv8 models—YOLOv8n, YOLOv8s, YOLOv8m, YOLOv8l, and YOLOv8x—using this dataset to assess their effectiveness in detecting various defects. The principal results demonstrate that YOLOv8m achieves a balanced trade-off between precision and recall, making it suitable for reliable defect identification across diverse defect types. YOLOv8s, on the other hand, excels in efficiency and speed, particularly for detecting 'Pore' defects. The study also highlights the limitations of YOLOv8n in detecting specific defect types and the computational challenges associated with YOLOv8l and YOLOv8x. Our methodology and findings contribute to the scientific understanding of automated defect detection in additive manufacturing. The development of the MDD and the comparative evaluation of YOLOv8 models advance the state of knowledge by providing a robust framework for detecting microstructural defects. Future research should focus on expanding the dataset and exploring advanced AI techniques to enhance detection accuracy and model generalization.

Rotating cylindrical shells have a wide range of practical applications; however, they are prone to vibrations. Despite numerous theoretical studies on vibration characteristics of rotating cylindrical shells, experimental validation remains limited.  Using non-contact vibration sensors for an experimental study offers significant advantages, such as eliminating mass effects and avoiding complex wiring associated with attachment to rotating shells. However, achieving an adequate data acquisition frequency by non-contact sensors in modal analysis of rotating cylindrical shells necessitates deploying multiple sensors circumferentially, which makes it costly and complex. This difficulty could be mitigated by correct shell selection to enable experimental validation of theoretical studies. The primary objective of the present study is to determine with which dimensions and rotational velocities, an experimental result of vibration characteristics for a rotating cylindrical shell could be attained by fewer non-contact sensors, which could be interpreted as a first pace toward experimental validation of theoretical methods. To achieve this innovative goal, a parametric study was conducted using an accurate finite element method (FEM) in ANSYS to illustrate how rotational velocity and dimensions affect the required number of sensors. Using the results of the parametric study, optimum values of rotational speed and dimensional parameters have been determined in a way that the experimental vibration analysis could be accomplished with a minimum number of required circumferential non-contact sensors. In the case of the present study, the number of required circumferential sensors is reduced from about 200 for an unsuitable choice to 24 for the choice of the present paper.

Due to increasing air pollution and fuel supply problems, the use of electric vehicles has been considered as a clean alternative to traditional cars. Extensive studies have been conducted on the environmental effects of these cars and their benefits in reducing air pollution have been proven. However, the high penetration of electric vehicles can create new challenges in the power system, especially concerning their charging demands. The change in demand due to electric vehicles creates challenges and concerns for the electric system. The electrical system must be ready to respond to a new load called “the demand for electric vehicles”. In this paper, by providing the optimal charging price and considering traffic restrictions, remained charging limits and charging rates, it is possible to choose the right charging station. This action reduces the possibility of damage to the network and leads to the improvement of network technical parameters, including losses and voltage profile. The results shows the effectiveness of the model.

COVID-19 is a respiratory disease that directly affects the lungs of infected individuals, leading to severe respiratory problems and lung infections. Although the severity of COVID-19 has decreased, the possibility of contracting the virus still exists, especially for individuals with underlying medical issues. Diagnosis of the severity of COVID-19 is very important in providing essential services to patients, improving treatment outcomes, and reducing complications and mortality rates associated with the virus. But, distinguishing of the severity of COVID-19 is a challenging task. COVID-19 is divided into four classes as far as its severity is concerned: normal-PCR+, mild, moderate, and severe. To overcome this challenge, we have introduced a novel method called DA-COVSGNet, based on a Convolutional Neural Network (CNN). In The proposed model, we preprocessed X-ray images using Synthetic Minority Over-Sampling Technique (SMOTE) and Contrast Limited Adaptive Histogram Equalization (CLAHE) techniques, and fed them to the CNN architecture. Furthermore, we used new attention mechanisms to aid in better distinguishing and classifying disease severity levels, resulting in higher accuracy in classifying disease severity classes. Finally, we evaluated our proposed method on the COVIDGR dataset. The results show that our proposed method achieved accuracies of 96.7%, 96.2%, 98.5%, and 95.2% for the categories of Normal-PCR+, mild, moderate, and severe, respectively.

Most daily activities need using hands and fingers dexterously. Hand prostheses in disabled people can be controlled using surface Electromyography (sEMG) signals acquired non-invasively by means of surface electrodes connected to superior limbs. After preprocessing 12 electrodes sEMG signals acquired from 10 amputees, different features in time and frequency domains were computed. Considering sEMG as a complex, random, non-stationary, and nonlinear signal a complex nonlinear feature was also extracted by the method of multifractal detrended fluctuation analysis (MFDFA). Different classification methods including support vector machine (SVM), linear discriminant analysis (LDA), and Multi-Layer Perceptron (MLP) were used to compare their performance in the classification of eight different finger movements. It was observed that the SVM performed better than the two other classifiers in finger movement classification. The best classification accuracy, precision, and recall (sensitivity), by the fusion of the new and traditional features were 98.70%, 98.74%, and 98.67%, respectively. Results showed that addition of the new feature extracted by MFDFA and other traditional features was effective in improving the data acquisitions.

Cybersecurity is an increasingly critical concern for the financial services sector, which is often the target of various cyber threats. One of the primary vulnerabilities in this industry is the insufficient implementation of secure remote access controls, making it easier for malicious actors to exploit weaknesses. To address these pressing security issues, a research study proposes an innovative multi-layer, multi-factor authentication framework that combines the strengths of blockchain technology and Public Key Infrastructure (PKI). This integrated approach aims to secure access to financial services efficiently, without necessitating the development of specialized applications. The proposed security framework employs a multi-layered strategy, harnessing the advantages of both blockchain and PKI to create a robust digital authentication system. Blockchain technology provides a decentralized and tamper-resistant ledger that upholds the integrity and transparency of transactions, while PKI ensures the authenticity and confidentiality of data. In this model, security is maintained through four distinct layers: user identity verification utilizing multi-factor authentication, encrypted data signatures generated using the user’s private key, and comprehensive encryption of all data employing the server’s public key. Once encrypted, the data is systematically organized into blocks and securely stored on a blockchain network, further reinforcing both data security and traceability. Preliminary analysis indicates that this blockchain-integrated authentication system offers greater reliability and security compared to existing methods for remote access in financial services. Additionally, it fosters enhanced customer trust by facilitating secure, verifiable transactions without the need for a dedicated application for each service, ultimately ensuring crucial features such as non-repudiation and data integrity are upheld.

A new type of closed-type lightning protection multi-chamber arrester named impulse quenching line lightning protection device (LLPD) used for protection of overhead power lines is described. After the passing of lightning current, the multi-chamber system of the arrester prevents the occurrence of a network short-circuit current due to the total voltage drop across several thousand series-connected spark gaps that significantly exceeds the applied network voltage in magnitude. The total operating time of the LLPD is less than 1 ms which is not sensitive for microprocessor and relay protection of overhead lines. A computational estimation of the emergency outage rate of double-circuit overhead power lines without an overhead ground wire resulting from lightning overvoltage was carried out. Number of current impulses used when arresters are tested for quenching capacity as well as their parameters were determined by the mathematical modeling using the statistical Monte Carlo method. Results of calculation that make it possible to determine the efficiency of lightning protection of double-circuit overhead lines with various placement schemes of the line lightning protection device LLPD-110 of the company Streamer Electric AG are presented in the conclusion.

In today's competitive market, manufacturers and service providers are continuously seeking ways to reduce costs and save time to gain a competitive edge. One of the most significant challenges they face is the vehicle routing problem (VRP), which is crucial due to its direct impact on the delivery time of services or products. Efficient vehicle routing not only enhances delivery performance but also optimizes the overall network, resulting in reduced operational costs. This study focuses on evaluating the VRP specifically for trucks while incorporating sustainability indicators into the analysis. The key sustainability indicators considered include social, economic, and environmental aspects. By integrating these indicators, the study aims to address multiple objectives simultaneously: reducing delivery time, minimizing costs, and mitigating the environmental impact of vehicle operations.The primary objective of this research is to minimize overall costs, fuel consumption, and route complexity associated with truck deliveries. Given the growing concern over environmental issues, there is a strong emphasis on improving methods to reduce greenhouse gas (GHG) emissions and streamline logistics processes. The research addresses these concerns by proposing a model that not only aims to enhance operational efficiency but also contributes to environmental protection and social responsibility.To achieve these objectives, the study employs advanced optimization techniques, specifically the non-dominated sorting genetic algorithm (NSGA-II) and multi-objective particle swarm optimization (MOPSO). These methods are utilized to solve the VRP while balancing the trade-offs between various objectives, such as cost reduction, fuel efficiency, and route optimization.The results of the study indicate that the proposed model successfully improves aspects of environmental protection and social responsibility while simultaneously addressing economic concerns. The integration of sustainability indicators into the vehicle routing problem provides a comprehensive approach to optimizing logistics operations, highlighting the importance of considering environmental and social factors alongside economic performance.Overall, this research contributes to the field by offering a refined model for tackling the VRP, with a focus on sustainability. The findings underscore the potential for optimization algorithms to drive improvements in both operational efficiency and environmental stewardship, ultimately supporting more sustainable and socially responsible practices in the transportation and logistics industry.

In modern conditions of the rapidly developing market of the mineral and raw materials complex the question of increasing the profitability of the enterprise is acute. However, cost reduction should not affect the safety of production works. In the present study the authors analyzed the activity of underground complex of Kirovsky mine of APATIT JSC, Kirovsk, Murmansk region. They identified the possibility of cost reduction through modernization of the mine (reduction of downtime during ventilation). We built a conceptual and mathematical model of the mine. In the work development of dust concentration control system was made, which is expressed in reducing the ventilation time from 60 to 30 minutes and achieving a more favorable dust concentration of 5 mg/m3, within generally accepted dust concentration of 8 mg/m3. Developed P, PI, PID regulators for regulating the ventilation system, determined the optimal regulator. We have developed a technical solution (patent №2799233) and implemented it in production. The present work is a report study on the fulfillment of the work within the framework of the thesis for the degree of Candidate of Technical Sciences.

Formation damage is typically regarded as a detrimental phenomenon that necessitates effective treatment to optimize production efficiency and extend wellbore lifetime. This study introduces new standards to guide laboratory-based investigations into the efficiency of chemical solvents for mitigating formation damage in sandstone reservoirs, emphasizing pore-scale property changes and permeability recovery. Three sandstone core samples from an anonymous reservoir, characterized by varying geological and petrophysical properties, were subjected to initial damage through the injection of oil-based drilling fluids. Each sample was subsequently treated with a distinct chemical solvent to evaluate their respective efficiencies in restoring porosity and permeability. Advanced tomographic imaging and pore-scale analyses were employed to quantify changes before and after treatment, focusing on representative elementary volumes (REVs) along the samples. The findings highlight the critical influence of initial sedimentary heterogeneities on solvent efficiency. Variations in CT number, porosity, permeability, and tortuosity demonstrated spatial heterogeneity in solvent effectiveness along the injection pathway. Notably, tortuosity decreased across most REVs, particularly near the outlet, post-treatment, indicating improved fluid flow pathways. However, uniform and consistent improvements in porosity, pore size distribution, and permeability were not observed, underscoring the role of intrinsic geological variability. This study concludes that solvent efficiency is strongly influenced by the severity of initial sedimentary heterogeneities induced by formation damage, rather than being solely dependent on solvent type. To achieve reliable comparisons, future solvent performance evaluations must account for comparable levels of geological heterogeneity pre- and post-treatment. These findings provide actionable insights for optimizing solvent selection and developing more effective strategies for reservoir stimulation and formation damage remediation.

This article discusses in detail the issue of killing gas wells with abnormally low reservoir pressure. The reason of discrepancies between the calculated data of the killing process according to the IWCF standard and the actual ones has been identified. It describes the physical mechanism of rupture of the liquid flow during injection. A mathematical formula describing the rupture of the fluid flow in the pipes is derived and verified using production data. The average deviation is 4.49%, which is acceptable for the problem being addressed. The authors proposed a mathematical model describing the killing of a gas well with abnormally low reservoir pressure. The killing process is analyzed at each stage. Verification calculations and comparison of their results with field data are presented. The average deviation of the calculated values from the actual ones falls within an acceptable range, amounting to 8.84%. The model is recommended for use in calculating the killing of gas wells with abnormally low reservoir pressure.

This study aims to optimize the flight endurance of a 12-passenger turboprop air taxi using two metaheuristic optimization algorithms: Grey Wolf Optimization (GWO) and Ant Colony Optimization (ACO). Initially, the gradient descent method was employed to estimate the aircraft's maximum weight. Subsequently, the aircraft's performance characteristics were utilized as design variables and flight endurance was optimized under specific constraints without altering the physical structure of the aircraft. The optimization process was implemented, and the results were evaluated and compared in terms of performance and efficiency. This research demonstrated that the two mentioned algorithms, utilizing random and collective strategies, were able to enhance the aircraft's efficiency. Additionally, the optimization of flight endurance for three real aircraft—Piper, Beechcraft, and Bombardier—was examined compared to their original endurance. In this context, the Ant Colony Optimization algorithm exhibited better performance than the Grey Wolf Optimization algorithm, which could have a positive impact on flight operations without refueling or the process of finding alternative airports.

In conditions of gas pipelines operation, significant longitudinal deformations may occur at the sections adjacent to the receiving/ launching chambers of pig and diagnostic equipment. These deformations, caused by stress and temperature fluctuations, lead to undesirable deformations and damage of piping elements. To reduce the magnitude of longitudinal deformations the device of expansion joints is proposed. Geometric parameters of the expansion joint are determined based on the results of strength and stability calculations of the gas pipelines. The use of the software package START allowed to simulate the behavior of the pipeline before and after installation of the expansion joint and to analyze changes in longitudinal deformations.  An important part of the work is the methodology of calculation justification of the necessity to install such units to reduce longitudinal deformations. Optimization of the expansion joint design and calculation of its parameters contributes to the reliability of gas pipeline systems.

The purpose of this study is to investigate the effectiveness of turbulence promoters—spring, twisted tape, and propeller—in reducing sediment accumulation within domestic heating radiators, a challenge that impacts thermal efficiency and system longevity. The research involved a 72-hour experimental analysis under controlled conditions, using a dual-radiator setup to evaluate the performance of each turbulator type at various temperature. The study adhered to international standards, ensuring the reliability and replicability of the results. Methodologically, the turbulator geometries were selected to maximize shear stress and compatibility with the radiator dimensions, with the aim of achieving optimal sediment reduction without compromising thermal efficiency. The experiments measured key parameters, including heat output, pressure drop, and the weight of residual particles, to validate the turbulator's effectiveness. The results revealed that the propeller turbulator was particularly effective, reducing the heat output reduction due to sedimentation to 5.1% at a Reynolds number of 1680, compared to an 18.3% reduction in the control system without a turbulator. Additionally, the introduction of turbulators resulted in a minimal increase in the pressure drop, demonstrating their efficiency in sediment control without negatively impacting hydraulic performance. The weight of residual particles was significantly lower in systems using turbulators, with the propeller showing the greatest reduction. The study concludes that mechanical turbulence promoters, especially the propeller, provide a sustainable alternative to chemical methods that can cause corrosion and reduce the durability of radiators. These findings contribute to the development of more efficient and long-lasting domestic heating systems, aligning with the broader goals of advancing residential thermal management.

The study of the geology of Russia’s Far East and Sakhalin projects has gained particular relevance for the Russian Federation due to their resource potential and proximity to export markets. The interaction between tectonic and sedimentary processes is especially visible on the Okhotsk Sea offshore. This study aims to clarify the geological conditions for the formation of reservoirs in the Daginskaya formation, which formed under the influence of the extensive paleo-delta system of the Amur River, based on a comprehensive analysis and processing of geological and seismic data. The main focus of the study is on the application of modern paleotectonic modeling technologies. Using Geoplat Pro-S and Petrel software, this research qualitatively examines the specific influence of structural-tectonic factors from the uplifts in the Kirinsky licensed area on reservoir distribution, employing stochastic inversion algorithms and paleotectonic analysis of paleo-history and paleotectonic cubes. The results confirm that the vaults of the uplifts acted as tectono-sedimentation barriers, restricting the advance of the Amur paleo-delta and the accumulation of high-capacity terrigenous reservoirs in the Daginskaya formation. Based on the findings, structural-tectonic zoning of the area was conducted, identifying blocks with distinct tectonic evolution. The results reaffirm the interdependence of tectonic and accumulation processes, the importance of comprehensive research, and provide critical insights into the geological mechanisms that led to the forming of productive formations on the Sakhalin offshore.