Geological Exploration Research Articles

Performance management is a key link in an enterprise's human resource management. Its scientific nature and effectiveness have a significant impact on the implementation of corporate strategies, employee motivation, and the cultivation of core competitiveness. As a state-owned geological exploration unit, against the background of deepening the reform of state-owned enterprises and industrial transformation, the optimization of the performance management mechanism has become a core issue for Y Company to promote its sustainable development. Based on the expectancy theory, this study conducts an in-depth analysis of the current situation of Y Company's performance management through a questionnaire survey method, and finds that there are problems such as fragmented employee cognition, defects in the design of performance indicators and processes, and insufficient pertinence of the incentive mechanism. These shortcomings have weakened employee motivation and also restricted the achievement of the company's goals. Based on this, this study puts forward optimization suggestions from three dimensions: internal optimization, external adaptation, and implementation guarantee, aiming to provide a practical path for Y Company's performance management reform, and at the same time offer reference for the performance management optimization of similar state-owned geological exploration units.

This study presents a unique Logsigm Function (LSF) filter for edge detection, established to improve the delineation of geological structures from potential field data. The filter’s performance was first validated using synthetic gravity and magnetic models simulating complex geological configurations with varying depths and contrasts. Results confirm the LSF’s high precision in resolving both shallow and deep structural boundaries, even in the presence of noise, while maintaining computational simplicity and ease of implementation. The method was then applied to real gravity and magnetic data from Northern Sinai, Egypt, a geologically complex region affected by extensional and inversion tectonics. To complement and validate the geophysical results, surface lineaments were extracted from enhanced remote sensing datasets, including Landsat8 OLI and ALOS PALSAR DEM imagery. The comparison between surface and subsurface trends revealed systematic vertical variations in structural orientations, highlighting the role of inherited basement faults and deformation decoupling. The LSF results successfully matched known structures and uncovered previously unrecognized lineaments, offering new insights into the tectonic architecture and basin evolution of Northern Sinai. The integrated approach demonstrates the value of combining advanced filtering techniques with remote sensing to achieve robust structural interpretations. The simplicity, stability, and high resolution of the LSF method make it a powerful tool for structural geology, tectonic analysis, and resource exploration in complex geologic terrains.

This paper presents the results of geochemical studies on core samples from Neocomian sediments located in the northern part of Western Siberia. In traditional geological exploration of deep-submerged facilities with complex geological structures, productive intervals are often overlooked. It underscores the necessity for additional diagnostic methods. The aim of this work is to develop a methodological model for conducting geochemical analysis of core samples to enhance the reliability of detecting valuable hydrocarbon fluids. To achieve this, the authors of this paper selected a set of geochemical studies on core samples, including extraction-weight analysis, chromatographic analysis, and the study of deeply sorbed gases. Based on this approach, the authors developed criteria for identifying the type of reservoir fluid in the reservoir. The conclusions regarding the fluid saturation of the studied deposits were validated by well test results, demonstrating the effectiveness of geochemical methods in exploration efforts.

Hyperspectral unmixing aims to extract pure spectral signatures (endmembers) and estimate their corresponding abundance fractions from mixed pixels, enabling quantitative analysis of surface material composition. However, in geological mineral exploration, existing unmixing methods often fail to explicitly identify informative spectral bands, lack inter-layer information transfer mechanisms, and overlook the physical constraints intrinsic to the unmixing process. These issues result in limited directionality, sparsity, and interpretability. To address these limitations, this paper proposes a novel model, CResDAE, based on a deep autoencoder architecture. The encoder integrates a channel attention mechanism and deep residual modules to enhance its ability to assign adaptive weights to spectral bands in geological hyperspectral unmixing tasks. The model is evaluated by comparing its performance with traditional and deep learning-based unmixing methods on synthetic datasets, and through a comparative analysis with a nonlinear autoencoder on the Urban hyperspectral scene. Experimental results show that CResDAE consistently outperforms both conventional and deep learning counterparts. Finally, CResDAE is applied to GF-5 hyperspectral imagery from Yunnan Province, China, where it effectively distinguishes surface materials such as Forest, Grassland, Silicate, Carbonate, and Sulfate, offering reliable data support for geological surveys and mineral exploration in covered regions.

Purpose. Development and application of a methodology for predicting the occurrence of a coal seam using numerical interpolation methods and three-dimensional geoinformation modeling. Methodology. The study is based on geological exploration data of the c 42 coal seam at the “Samarska” mine. Numerical interpolation methods and three-dimensional modeling in AutoCAD 3D were used. Based on geological data, a digital terrain model of the seam’s floor was constructed, and its thickness and variations across the area were determined. Using mathematical methods, an analysis of thinning and thickening zones of the seam was carried out, allowing for the estimation of coal extraction volumes, waste rock yield, and the operational ash content of coal. Findings. As a result of the study, a three-dimensional model of the c 42 coal seam of the “Samarska” mine was developed and its geological thickness was determined. Areas of thinning and thickening of the seam were identified, which made it possible to optimize the location of extraction pillars and preparatory workings. Volumes of coal to be mined and waste rock to be cut were calculated. The estimated operational ash content of coal was determined to be 34.4 %, which is an important factor for controlling the quality of the extracted product. The data obtained made it possible to optimize the parameters of cleaning operations, adapting the technological process to the geological conditions of the coal seam. Originality. The article proposes an improved approach to predicting coal seam occurrence using numerical interpolation and three-dimensional modeling, adapted to conditions with limited geological data. For the first time, a step-by-step construction with dynamic uprating of seam geometry is implemented, enhancing the accuracy of reserve estimation and the efficiency of mining design. Practical value. The developed methodology makes it possible to minimize exploration costs, improve the reserve estimation accuracy, reduce risks, and optimize mining operations. The results can be used to design production technology for other mines in Western Donbas, contributing to increased mining efficiency.

The Upryamoye ore field is located in the Chukotka metallogenic belt in Northeast Russia. The orebodies are hosted within Late Jurassic–Early Cretaceous greenschist-facies metamorphosed rocks and structurally controlled by NW-trending fold-and-thrust dislocations. Based on geological exploration, petrographic, mineralogical, and geochronological studies, new data on the geological structure and composition of gold–quartz mineralization of the Upryamoye ore field are presented. Optical and scanning microscopy were used to study the lithological features of the host rocks and determine the ore textures and the morphology and internal structure of native gold, auriferous pyrite, and arsenopyrite. Qualitative and quantitative characterization of the ore minerals was carried out using SEM-EDS and EPMA. To determine the age of the gold mineralization, Re-Os dating of arsenopyrite and U-Th/He dating of pyrite were performed. The results show that the orebodies comprise carbonate–quartz and sulphide–carbonate–quartz saddle reef veins in both the fold hinge and limbs, as well as mineralized shatter zones and mylonite zones that trace thrust faults. The main ore minerals are arsenopyrite and pyrite, associated with minor amounts of galena, sphalerite, chalcopyrite, tetrahedrite, and bournonite. Native gold is distributed extremely unevenly, forming thin and finely dispersed inclusions in pyrite and arsenopyrite. U-Th/He isotopic analyses of auriferous pyrites suggest that gold mineralization in the Upryamoye ore field occurred at 123 ± 4 Ma. The data obtained by Re–Os dating of auriferous arsenopyrite are inconsistent with direct geological observations but indicate that Os in the arsenopyrite was derived from the crustal source. According to a number of characteristic features of mineralization, the Upryamoye ore field is attributed to a metamorphic genetic type of orogenic low-sulphide gold–quartz deposits. The ore-forming process was long and multi-stage, occurring during the final collisional phase and the beginning of the extensional phase of the Chukotka orogen.

Judging from the current global exploration trend, ultra-deep layers have become the main battlefield for energy exploration. China has made great progress in the ultra-deep field in recent decades, with the Tarim Basin and Sichuan Basin as the focus of exploration. The Sichuan Basin is a large superimposed gas-bearing basin that has experienced multiple tectonic movements and has developed multiple sets of reservoir–caprock combinations vertically. Notably, the multi-stage platform margin belt-type reservoirs of the Sinian–Lower Paleozoic exhibit inherited and superimposed development. Source rocks from the Qiongzhusi, Doushantuo, and Maidiping formations are located in close proximity to reservoirs, creating a complex hydrocarbon supply system, resulting in vertical and lateral migration paths. The structural faults connect the source and reservoir, and the source–reservoir–caprock combination is complete, with huge exploration potential. At the same time, the ultra-deep carbonate rock structure in the basin is weakly deformed, the ancient closures are well preserved, and the ancient oil reservoirs are cracked into gas reservoirs in situ, with little loss, which is conducive to the large-scale accumulation of natural gas. Since the Nvji well produced 18,500 cubic meters of gas per day in 1979, the study of ultra-deep layers in the Sichuan Basin has begun. Subsequently, further achievements have been made in the Guanji, Jiulongshan, Longgang, Shuangyushi, Wutan and Penglai gas fields. Since 2000, two trillion cubic meters of exploration areas have been discovered, with huge exploration potential, which is an important area for increasing production by trillion cubic meters in the future. Faced with the ultra-deep high-temperature and high-pressure geological environment and the complex geological conditions formed by multi-stage superimposed tectonic movements, how do we understand the special geological environment of ultra-deep layers? What geological processes have the generation, migration and enrichment of ultra-deep hydrocarbons experienced? What are the laws of distribution of ultra-deep oil and gas reservoirs? Based on the major achievements and important discoveries made in ultra-deep oil and gas exploration in recent years, this paper discusses the formation and enrichment status of ultra-deep oil and gas reservoirs in the Sichuan Basin from the perspective of basin structure, source rocks, reservoirs, caprocks, closures and preservation conditions, and provides support for the optimization of favorable exploration areas in the future.

Accurate lithology identification is a crucial prerequisite for sedimentary environment analysis, oil and gas exploration, and development. Traditional identification methods have problems such as low efficiency, high cost, or limited applicability. This paper proposes an ADASYN-IRMO-CatBoost combined model. Firstly, the ADASYN algorithm is used to perform adaptive sampling on the logging datasets of three wells in the southern Ordos Basin to solve the problem of data imbalance. Then, the Improved Radial Movement Optimization (IRMO) algorithm is utilized to optimize the hyperparameters of the CatBoost model. Finally, CatBoost is used as the core classifier for lithology identification. Experimental results show that the overall accuracy of the combined model on the test set reaches 92%, which is 13% higher than that of the single CatBoost model. The F1 scores of various lithologies and the AUC values of the ROC curves are significantly better than those of the single model, demonstrating stronger classification performance and robustness. It provides an efficient and accurate new method for lithology identification of sandstone and mudstone reservoirs and has good application prospects in the field of geological exploration.

Understanding thethermodynamic properties of mineralsis crucialfor elucidating the physicochemical conditions of their formationand mutual conversion, which support geological exploration and mineralresource development. The dehydration of gypsum to form anhydriteapparently occurs readily in nature. There have been a number of studiesof the equilibrium temperature of the dehydration reaction, suggestingthat the transition temperature of gypsum to anhydrite is mostly between40 and 60 °C. The phase transformation temperature of gypsumto anhydrite was investigated by means of density functional theory(DFT) calculations of thermodynamic properties in this paper. Thecalculated lattice parameters of gypsum and anhydrite are in goodagreement with the experimentally reported data. The thermodynamicproperties of gypsum and anhydrite, including isobaric heat capacity,entropy, enthalpy, and Gibbs free energy functions, were then computedby performing phonon calculations as a function of temperature inthe range of 0–1000 K. The results showed that (1) the computedvolume exhibits minimal error, approximately 0.49% for gypsum and0.27% for anhydrite, compared to experimental data; (2) the calculatedisobaric heat capacity function of gypsum (DH) and anhydrite (AH)are Cp(T, DH) = 191.17+ 0.38T + 3.76 × 104T–2 – 1.38 × 103T–0.5 – 2.13 × 10–4T2 and Cp(T, AH) = 107.90 + 0.21T + 2.10× 104T–2 –0.77 × 103T–0.5 – 1.24 × 10–4T2, respectively; (3) at 298.15K, the calculated values forthe isobaric specific heat and entropy of gypsum were 206.21 and 206.32J·mol–1·K–1, respectively;(4) at 298.15 K, the calculated values for the isobaric specific heatand entropy of anhydrite were 114.53 and 116.29 J·mol–1·K–1, respectively; and (5) the calculatedvalue for the free energy of formation of gypsum and anhydrite at298.15K are −1849.72 KJ·mol–1 and −1350.79KJ·mol–1, respectively. The Gibbs energy variationequation with temperature of the dehydration process of gypsum toanhydrite was obtained by calculations: ΔRG­(T) = −17622.08 + 282.41T –67.53T ln T+0.085T2 + 0.83× 104T–1 + 2.44× 103T0.5 – 1.50× 10–5T3. It isshown that careful assessment of Gibbs energy of the dehydration processbased on DFT calculations yields a transition temperature of 46.58°C. This research provides valuable insights into the thermodynamicproperties and phase transition of gypsum to anhydrite, highlightingthe efficacy of theoretical methods as predictive tools for analogouscases.

Abstract. Groundwater contaminant transport problems remain challenging with respect to their computing requirements. This often limits the exploration of the conceptual uncertainty that is mainly related to large-scale geological features – such as faults, fractures, and stratigraphic variations – and limited characterization. Here, to facilitate geological conceptual uncertainty exploration, we develop further the use of graph representation for geological models to approximate groundwater flow and transport. We consider a faulted multi-heterogeneous-layer medium to test our approach. The existing rank correlation between the shortest path distribution from a contaminant source to the model domain outlet and the cumulative mass distribution at the outlet enables us to perform scenario selection. The scenario selection approach relies on a metric combining the Jaccard dissimilarity and the Wasserstein distance to compare binary images. Among a set combining eight alternative scenarios, where three faults can act as either a flow barrier or a preferential path, we show that the use of graph approximations allows us to retain or reject scenarios with confidence, as well as to estimate the individual probability of a fault to act as a barrier or a path. This methodology framework opens up possibilities to explore more thoroughly conceptual geological uncertainty for processes affected by flow and transport.

Geothermal energy is a recognized renewable resource in the present scenario, with over 30 nations generating electricity and 88 using it for heating and heat pumps. Despite its substantial geothermal potential, India has yet to develop this resource because of limited geological exploration, reservoir characterization, and technological constraints. The Indian Himalayas host over 300 geothermal fields, offering a promising avenue for sustainable energy deployment in remote regions. This study evaluates the geothermal potential of the Badrinath geothermal field in the northwest Himalayas using a volumetric heat estimation method integrated with Monte Carlo simulations, addressing uncertainties in subsurface temperature distribution, heat capacity, and fluid recharge. Results indicate a maximum thermal power potential of 39 MWt and an electrical generation potential of 3.0 MWe, demonstrating its viability for small-scale power production. Furthermore, the design of a district heating system for the guesthouse is investigated, with particular attention to optimizing thermal energy transfer efficiency from the geothermal spring to the radiators. Despite extreme winter temperatures reaching − 10 °C, our findings confirm that geothermal water can be effectively utilized for heating applications without endangering the structural integrity of heat exchangers and distribution pipelines. This study represents the first systematic assessment of the Badrinath geothermal field, providing insights for future geothermal energy utilization in the Indian Himalayas.Supplementary InformationThe online version contains supplementary material available at 10.1038/s41598-025-19120-5.

Three-dimensional laser scanning provides high-precision spatial data for automated lithology identification in geological outcrops. However, existing methods exhibit limited performance in transition zones with blurred boundaries and demonstrate reduced classification accuracy under complex stratigraphic conditions. This study proposes a Stratigraphically Constrained Continuous Clustering (SCCC) framework to address these limitations. The framework incorporates sedimentological principles of lateral continuity through a dynamic density-threshold hierarchical clustering algorithm that optimizes lithological unit boundaries using adjacency-based cluster merging criteria. A patch-level feature aggregation module, integrated within the proposed SCCC framework, constructs a multimodal feature space by aggregating geometric covariance matrices and spectral distribution entropy into compact patch-level feature vectors. Random forest classifier subsequently performs lithology discrimination. Experimental validation using the Qingshuihe Formation outcrop dataset demonstrates that SCCC achieves overall accuracy of 94.64%, F1-score of 94.58%, and mean intersection over union of 90.87%. These results surpass traditional machine learning (SVM, XGBoost) and deep learning methods (PointNet) by 26.22-68.36%, indicating substantial improvements in classification accuracy and boundary delineation within transition zones. SCCC particularly enhances recognition capabilities for sandstone-mudstone thin interbeds and conglomerate-sandstone transitional zones. Ablation experiments confirm that stratigraphic constraints effectively suppress noise while improving computational efficiency, reducing memory usage by 83.3% and processing time by 85.7%. This method provides a high-precision, interpretable technical pathway for intelligent geological exploration through deep integration of geological principles with computational models.

In offshore drilling and geological exploration, the stability of jack-up rigs is predominantly determined by the bearing capacities of spudcan foundations during seabed penetration. The penetration depth of spudcans is relatively shallow in hard clay. The formation of a cavity on the top surface of a spudcan often complicates accurate estimation of its capacity. This study employs the finite element method, in conjunction with the Swipe and Probe loading techniques, to examine the failure surfaces of soils of varying strengths. Numerical simulations that consider different gradients of undrained shear strength and cavity depths demonstrate that cavity depth significantly influences the failure envelope. The findings indicate that higher soil strength increases the bearing capacity and reduces the area of soil displacement at failure. Moreover, an enhanced theoretical equation for predicting the vertical-horizontal-moment (V-H-M) failure envelope in hard clay strata is proposed. The equation’s accuracy has been verified against numerical simulation results, revealing an error margin of 3–10% under high vertical loads. This model serves as a practical and valuable tool for assessing the stability of jack-up rigs in hard clay, providing critical insights for engineering design safety and risk assessment.

Editor’s note: This is the last paper in the Geology and Mining series, which has aimed to introduce early career professionals and students to various aspects of mineral exploration, development, and mining in order to share the experiences and insight of each author on the myriad of topics involved with the mineral industry and the ways in which geoscientists contribute to each. The 29 chapters plus two others have been compiled into a book, sponsored by BHP and edited by Dan Wood and Jeffrey Hedenquist, which is now available Open Access on the SEG store (www.segweb.org/store). It will soon be available on GeoScienceWorld, and a limited print run will produce hard copies for purchase. Abstract Mine tailings, the residual materials from mineral extraction, present one of the mining industry’s most complex environmental and engineering challenges. Comprising finely ground rock and residual chemicals, tailings require meticulous management to prevent ecological harm and ensure public safety. For exploration geologists, understanding this is not a downstream consideration but a fundamental responsibility that begins at discovery. The consequences of mismanagement are stark; since 2010, major tailings dam failures have caused numerous fatalities, contaminated thousands of kilometers of waterways, and triggered billions of dollars in remediation costs. These disasters underscore the critical need for specific planning and risk mitigation starting with the exploration phase to prevent similar outcomes. This paper provides exploration geologists with a comprehensive overview of the tailings management life cycle, covering material characterization, surface and underground disposal methods, risk mitigation strategies, best practices in monitoring and closure, and opportunities with tailings reprocessing. It demonstrates that integrating tailings considerations into the earliest phases of exploration—by informing site selection, characterizing geologic materials, and identifying geohazards—offers the most effective and economical path to minimizing long-term liabilities. By embracing their roles as the first stewards of a project, geologists can lay the foundation for safer, more sustainable mining outcomes.

Well logs play a crucial role in geological exploration and the development of oil and gas resources. However, in practice, well logs are often missing or incomplete. Since re-logging is often impractical given how well logs are obtained, they are usually estimated from existing well logs. The complexity and variability of underground structures, along with the highly nonlinear relationships among different log types, make it challenging for empirical regression and traditional machine-learning methods to deliver accurate predictions. Existing methods have difficulty in extracting and integrating the spatial, sequential, and multiscale dependencies among known well logs to predict unavailable well logs. In this study, we developed a hybrid U-Net and long short-term memory (LSTM) model that combines U-Net’s ability to extract multiscale spatial features with LSTM’s strength in modeling sequential depth-wise trends. The architecture uses convolutional blocks and skip connections to extract and map the multiscale spatial features of logging curves, while the LSTM component models the trend changes of well-log measurements with depth. We evaluated this model alongside the state-of-the-art hybrid convolutional neural network (CNN) and LSTM model (CNN-LSTM) on well-log data from the Force 2020 machine-learning competition to predict missing compressional slowness (DTC) logs. Results from training and testing experiments in eight blind wells show that the U-Net-LSTM outperforms the benchmark CNN-LSTM, achieving lower mean absolute error (MAE), root mean square error (RMSE), and higher correlation coefficients R. This demonstrates the potential of the proposed model as an effective tool for reliable and geologically consistent missing well-log reconstruction.

Summary In the process of oil and gas exploration, lithology classification of well log data is crucial for accurately describing subsurface formations and subsequent oil and gas reserve evaluations. Traditional lithology classification methods rely on manual feature extraction and linear models, which have achieved good results in some cases. However, these methods are often constrained by issues, such as data quality, noise interference, and incomplete feature selection, when dealing with complex and variable well log data. To address this challenge, we propose a novel deep learning framework—causal intervention and sparse shift network or CISSNet—aimed at enhancing the accuracy and robustness of lithology classification by introducing causal intervention mechanisms and sparse transfer learning strategies. The core innovation of CISSNet lies in its combination of causal intervention networks and sparse transfer learning networks. First, CISSNet uses a causal intervention framework to capture the causal relationships in well log data, identifying potential causal connections between different lithology categories. By incorporating causal reasoning mechanisms into the model, CISSNet effectively filters out noise factors and highlights key factors influencing lithology classification. Additionally, CISSNet introduces a sparse transfer learning strategy, which shares knowledge and features between source and target domains to overcome the heterogeneity issue of well log data across different well locations, thereby enhancing the model’s crosswell adaptability. In the experimental section, several real well log data sets are used for validation. The results show that CISSNet outperforms traditional classification methods and existing deep learning models in both lithology classification accuracy and robustness. Notably, even in the presence of missing or incomplete data, CISSNet can still maintain high classification precision. Overall, CISSNet provides a new solution for lithology classification by combining causal intervention and sparse transfer learning, not only improving classification accuracy but also enhancing adaptability and stability in complex and incomplete data. Future research will focus on further optimizing the model, improving its generalization ability for different types of well log data, and exploring its potential applications in other geological exploration tasks.

This paper focuses on building a digital model that reflects the variability of the physical and mechanical properties of the Elginskoye deposit. The initial data came from electronic databases compiled from geological and operational exploration reports. The Orange software package was used to create a geological model of the coal-bearing rock mass of the Elginskoye deposit. Block 3D models of the variability of physical and mechanical properties such as compressive strength, tensile strength, and density of carbon-bearing rocks in stratigraphic intervals at depths U6–U5, U5–U4, U4–H16, and H16–H15 were constructed. Modern computer technologies are able to visualize the values of physical and mechanical properties corresponding to each point of a twodimensional cross-section of a geological body. Rather than constructing a complete three-dimensional digital model to assess the structure and condition of the rock mass, an approximation can be constructed using twodimensional cross-sections, which clearly and informatively display the spatial variability of one of the physical and mechanical properties. An example is given of hypsometric plans for the distribution of rock strength under tension, compression, and bulk density at the depth of the surrounding rocks, in the interlayers U6–U5. Interlayer strength was measured at intervals ranging from 0.4 m to 1 m in depth, thereby identifying changes in the physical and mechanical properties of the rock both with depth and laterally. The presented plans demonstrate a significant variability in the strength and density properties of the rock. The strength limit of rocks under uniaxial compression, within the studied intervals, varies from 20.5 MPa to 129.9 MPa, the strength limit under uniaxial tension, from 2.64 MPa to 11.3 MPa, and the bulk density varies from 2.45 g/cm3 to 2.81 g/cm3 . The results of the research can be used to design and plan the development of the deposit, as well as to draw up specifications for drilling and blasting operations, taking into account the variability of the properties of carbon-bearing rocks.

To break through the technical bottlenecks of traditional UAVs in scene reproduction accuracy, situational awareness efficiency, and real-time data processing, and to promote the development of UAV technology towards intelligence, collaboration, and high precision, this paper systematically develops and elaborates on three core technology systems: real-scene multi-modal AI fusion modeling technology, global situational intelligent perception and collaboration technology, and multi-modal data real-time fusion technology based on sparse representation. In terms of technical performance, the real-scene modeling accuracy reaches the centimeter level, and the global perception response speed is increased to the millisecond level. By building a hardware verification platform and a software simulation system, application tests have been completed in scenarios such as smart cities, power inspection, and geological exploration. The results show that this technology system can achieve an equipment defect recognition rate of 97.8% and improve the inspection path planning efficiency by 40%, providing key technical support for the efficient operation of UAVs in complex environments. At present, the relevant technologies have been piloted in a number of power enterprises and municipal units, with potential for large-scale promotion, and are of great significance for promoting the upgrading of the UAV industry and the development of the digital economy.

One of the strategic objectives of the Yamalo-Nenets Autonomous Okrug (YNAO) is to develop its energy sector and to increase its share of Russia’s total oil and natural gas production. Economic development is closely linked to the dynamics of a company’s competitive position in the market, underscoring the importance of evaluating business competitiveness. This article aims to provide a comparative assessment of YNAO’s oil and gas businesses based on their levels of economic development and competitiveness, while refining existing methodological approaches. The study’s scientific novelty lies in improving the methodology for evaluating both economic development and competitiveness. The authors propose a model that systematizes indicators into three key groups reflecting the effectiveness and efficiency of operations, the utilization of assets and capital, and the management of financial resources. To facilitate comparative analysis, the study introduces a scoring matrix for evaluating economic development that incorporates both static and dynamic values. The level of competitiveness is assessed using an approach based on operating efficiency and strategic positioning coefficients. The methodology for calculating the operating efficiency coefficient was refined to incorporate operating profit as a core metric. A comparative assessment was conducted for 15 oil and gas businesses in YNAO for the period from 2019 to 2023. The analysis identified the leading companies and the factors driving their success. In terms of economic development, Gazpromneft-NNG ranked highest, primarily due to increased production of liquid hydrocarbons supported by improved geological exploration. In terms of competitiveness, Novatek-Purovsky ZPK held the leading position, driven by its high operational efficiency. Future research will focus on enhancing the assessment methodology by incorporating additional coefficients and forecast-based values alongside static and dynamic indicators. The practical significance of this study lies in identifying how specific operational indicators influence a company’s economic development and competitiveness, thereby supporting more informed management decisions to improve performance.

The paper describes experience in using pulsed neutron logging equipment (AINK-PL device) to solve prospecting and exploration work tasks, based on two wells exposing the Achimov complex reservoir rocks in a field located in the Yenisei-Khatanga structural-facies area. The domestic AINK-PL complex has been effectively implemented at the Rosneft Oil Company’s facilities since 2023. The AINK-PL processing methodology in RN-Geology Research Development quantitatively determines a detailed chemical composition of rocks (16 elements), macroscopic capture cross-section, and hydrogen content (neutron porosity). In the article, chemical elements obtained with an impulse neutron-gamma spectrometry method may serve as indicators for determining the typical mineral composition of a section, with the core data missing. There is analyzed the influence of the impulse neutron-gamma spectrometry method and gamma-gamma density logging on the volumetric component model results. Sequential exclusion of these methods from input data results in an underestimation of rock-forming minerals and decrease in the sandstone proportion (clay content increase) in the total rock volume. Exclusion of the gamma-gamma density logging from input log data leads to a porosity ratio decrease, as compared to core data (~2 % absolute). The obtained results prove efficiency of the optimal logging complex for subsequent exploratory and production drilling.