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Date submitted2023-10-02
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Date accepted2024-11-07
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Date published2025-04-28
Laboratory studies of hydraulic fracturing of intersecting boreholes in a non-uniform stress field
This study focuses on the features of hydraulic fracture propagation in intersecting boreholes in polymethyl methacrylate blocks in a non-uniform stress field. Glycerol aqueous solution and plasticine were used as the working fluids. According to linear fracture mechanics, a stress concentrator at the borehole intersection contributes to the beginning of crack formation, with further crack propagation occurring in the plane containing their axes. The relevance of this study is due to the search for innovative approaches and the development of technological solutions to address the issue of effective longitudinal crack formation and its further propagation in a rock mass under unfavourable stress field conditions. This paper provides a scheme of laboratory stand operation and a general view of the sealing packers used to isolate a specified interval when performing tests. The graphs of glycerol pressure versus injection time are presented, and the breakdown pressure in the blocks is specified. The shape of fractures formed during the indentation of plasticine into the borehole system was investigated. The findings of physical modelling indicate that longitudinal cracks are predominantly formed in the boreholes. The deviation of the crack trajectory from the vertical plane containing the borehole axes is primarily affected by the magnitude of the horizontal compressive stress field rather than the increase in the angle between them. In addition, the angles of inclination of the longitudinal crack plane measured at its intersection with the side face of the block are specified.
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Date submitted2023-10-29
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Date accepted2024-04-08
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Date published2025-02-25
Evaluation of the impact of the distance determination function on the results of optimization of the geographical placement of renewable energy sources-based generation using a metaheuristic algorithm
- Authors:
- Andrei M. Bramm
- Stanislav A. Eroshenko
Since the United Power System was created electrical supply of remote and hard-to-reach areas remains one of the topical issues for the power industry of Russia. Nowadays, usage of various renewable energy sources to supply electricity at remote areas has become feasible alternative to usage of diesel-based generation. It becomes more suitable with world decarbonization trends, the doctrine of energy security of Russia directives, and equipment cost decreasing for renewable energy sources-based power plants construction. Geological exploration is usually conducted at remote territories, where the centralized electrical supply can not be realized. Placement of large capacity renewable energy sources-based generation at the areas of geological expeditions looks perspective due to development of industrial clusters and residential consumers of electrical energy at those territories later on. Various metaheuristic methods are used to solve the task of optimal renewable energy sources-based generation geographical placement. The efficiency of metaheuristics depends on proper tuning of that methods hyperparameters, and high quality of big amount of meteorological and climatic data. The research of the effects of the calculation methods defining distance between agents of the algorithm on the optimization of renewable generation placement results is presented in this article. Two methods were studied: Euclidean distance and haversine distance. There were two cases considered to evaluate the effects of distance calculation method change. The first one was for a photovoltaic power plant with installed capacity of 45 MW placement at the Vagaiskii district of the Tyumen region. The second one was for a wind power plant with installed capacity of 25 MW at the Tungokochenskii district of the Trans-Baikal territory. The obtained results show low effects of distance calculation method change at average but the importance of its proper choose in case of wind power optimal placement, especially for local optima’s identification.
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Date submitted2023-11-10
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Date accepted2024-06-03
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Date published2025-02-25
Enhancing the interpretability of electricity consumption forecasting models for mining enterprises using SHapley Additive exPlanations
- Authors:
- Pavel V. Matrenin
- Alina I. Stepanova
The objective of this study is to enhance user trust in electricity consumption forecasting systems for mining enterprises by applying explainable artificial intelligence methods that provide not only forecasts but also their justifications. The research object comprises a complex of mines and ore processing plants of a company purchasing electricity on the wholesale electricity and power market. Hourly electricity consumption data for two years, schedules of planned repairs and equipment shutdowns, and meteorological data were utilized. Ensemble decision trees were applied for time series forecasting, and an analysis of the impact of various factors on forecasting accuracy was conducted. An algorithm for interpreting forecast results using the SHapley Additive exPlanation method was proposed. The mean absolute percentage error was 7.84 % with consideration of meteorological factors, 7.41 % with consideration of meteorological factors and a load plan formulated by an expert, and the expert's forecast error was 9.85 %. The results indicate that the increased accuracy of electricity consumption forecasting, considering additional factors, further improves when combining machine learning methods with expert evaluation. The development of such a system is only feasible using explainable artificial intelligence models.
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Date submitted2021-12-20
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Date accepted2024-05-02
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Date published2024-08-26
A new formula for calculating the required thickness of the frozen wall based on the strength criterion
- Authors:
- Mikhail А. Semin
- Lev Yu. Levin
The study delves into the elastoplastic deformation of a frozen wall (FW) with an unrestricted advance height, initially articulated by S.S.Vyalov. It scrutinizes the stress and displacement fields within the FW induced by external loads across various boundary scenarios, notably focusing on the inception and propagation of a plastic deformation zone throughout the FW's thickness. This delineation of the plastic deformation zone aligns with the FW's state of equilibrium, for which S.S.Vyalov derived a formula for FW thickness based on the strength criterion. These findings serve as a pivotal launchpad for the shift from a one-dimensional (1D) to a two-dimensional (2D) exploration of FW system deformation with finite advance height. The numerical simulation of FW deformation employs FreeFEM++ software, adopting a 2D axisymmetric approach and exploring two design schemes with distinct boundary conditions at the FW cylinder's upper base. The initial scheme fixes both vertical and radial displacements at the upper base, while the latter applies a vertical load equivalent to the weight of overlying soil layers. Building upon the research outcomes, a refined version of S.S.Vyalov's formula emerges, integrating the Mohr – Coulomb strength criterion and introducing a novel parameter – the advance height. The study elucidates conditions across various soil layers wherein the ultimate advance height minimally impacts the calculated FW thickness. This enables the pragmatic utilization of S.S.Vyalov's classical formula for FW thickness computation, predicated on the strength criterion and assuming an unrestricted advance height.
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Date submitted2023-03-14
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Date accepted2023-06-20
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Date published2023-07-19
Forecasting planned electricity consumption for the united power system using machine learning
The paper presents the results of studies of the predictive models development based on retrospective data on planned electricity consumption in the region with a significant share of enterprises in the mineral resource complex. Since the energy intensity of the industry remains quite high, the task of rationalizing the consumption of electricity is relevant. One of the ways to improve control accuracy when planning energy costs is to forecast electrical loads. Despite the large number of scientific papers on the topic of electricity consumption forecasting, this problem remains relevant due to the changing requirements of the wholesale electricity and power market to the accuracy of forecasts. Therefore, the purpose of this study is to support management decisions in the process of planning the volume of electricity consumption. To realize this, it is necessary to create a predictive model and determine the prospective power consumption of the power system. For this purpose, the collection and analysis of initial data, their preprocessing, selection of features, creation of models, and their optimization were carried out. The created models are based on historical data on planned power consumption, power system performance (frequency), as well as meteorological data. The research methods were: ensemble methods of machine learning (random forest, gradient boosting algorithms, such as XGBoost and CatBoost) and a long short-term memory recurrent neural network model (LSTM). The models obtained as a result of the conducted studies allow creating short-term forecasts of power consumption with a fairly high precision (for a period from one day to a week). The use of models based on gradient boosting algorithms and neural network models made it possible to obtain a forecast with an error of less than 1 %, which makes it possible to recommend the models described in the paper for use in forecasting the planned electricity power consumption of united power systems.
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Date submitted2022-08-01
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Date accepted2022-11-17
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Date published2023-02-27
Use of machine learning technology to model the distribution of lithotypes in the Permo-Carboniferous oil deposit of the Usinskoye field
- Authors:
- Denis V. Potekhin
- Sergei V. Galkin
Permo-Carboniferous oil deposit of the Usinskoye field is characterized by an extremely complex type of the void space with intense cross-sectional distribution of cavernous and fractured rock. In this study, for this production site, the process of 3D geological modeling has been implemented. At the first stage, it provided for automated identification of reservoir volumes by comparing the data of core and well logging surveys; at the second stage, identification of rock lithotypes according to Dunham classification is performed on the basis of comparison of thin sections examination and well logging data. A large array of factual information enables the use of machine learning technology on the basis of Levenberg – Marquardt neural network apparatus toward achievement of our research goals. The prediction algorithms of reservoir and rock lithotype identification using well logging methods obtained on the basis of the training samples are applied to the wells without core sampling. The implemented approach enabled complementing the 3D geological model with information about rock permeability and porosity, taking into account the structural features of the identified lithotypes. For the Permo-Carboniferous oil deposit of the Usinskoye field, the volumetric zoning of the distribution of different rock lithotypes has been established. Taking into account the lithotypes identified based on machine learning algorithms, density and openness of fractures were determined, and fracture permeability in the deposit volume was calculated. In general, during the implementation, the machine learning errors remained within 3-5 %, which suggests reliability of the obtained predictive solutions. The results of the research are incorporated in the existing 3D digital geological and process model of the deposit under study.
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Date submitted2021-05-13
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Date accepted2022-11-28
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Date published2022-12-29
Reproduction of reservoir pressure by machine learning methods and study of its influence on the cracks formation process in hydraulic fracturing
Hydraulic fracturing is an effective way to stimulate oil production, which is currently widely used in various conditions, including complex carbonate reservoirs. In the conditions of the considered field, hydraulic fracturing leads to a significant differentiation of technological efficiency indicators, which makes it expedient to study in detail the crack formation patterns. For all affected wells, the assessment of the resulting fractures spatial orientation was performed using the developed indirect technique, the reliability of which was confirmed by geophysical methods. In the course of the analysis, it was found that in all cases the fracture is oriented in the direction of the development system element area, which is characterized by the maximum reservoir pressure. At the same time, reservoir pressure values for all wells were determined at one point in time (at the beginning of hydraulic fracturing) using machine learning methods. The reliability of the used machine learning methods is confirmed by high convergence with the actual (historical) reservoir pressures obtained during hydrodynamic studies of wells. The obtained conclusion about the influence of the formation pressure on the patterns of fracturing should be taken into account when planning hydraulic fracturing in the considered conditions.
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Date submitted2022-05-13
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Date accepted2022-09-24
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Date published2022-11-03
Rapid detection of coal ash based on machine learning and X-ray fluorescence
Real-time testing of coal ash plays a vital role in the chemical, power generation, metallurgical, and coal separation sectors. The rapid online testing of coal ash using radiation measurement as the mainstream technology has problems such as strict coal sample requirements, poor radiation safety, low accuracy, and complicated equipment replacement. In this study, an intelligent detection technique based on feed-forward neural networks and improved particle swarm optimization (IPSO-FNN) is proposed to predict coal quality ash content in a fast, accurate, safe，and convenient manner. The data set was obtained by testing the elemental content of 198 coal samples with X-ray fluorescence (XRF). The types of input elements for machine learning (Si, Al, Fe, K, Ca, Mg, Ti, Zn, Na, P) were determined by combining the X-ray photoelectron spectroscopy (XPS) data with the change in the physical phase of each element in the coal samples during combustion. The mean squared error and coefficient of determination were chosen as the performance measures for the model. The results show that the IPSO algorithm is useful in adjusting the optimal number of nodes in the hidden layer. The IPSO-FNN model has strong prediction ability and good accuracy in coal ash prediction. The effect of the input element content of the IPSO-FNN model on the ash content was investigated, and it was found that the potassium content was the most significant factor affecting the ash content. This study is essential for real-time online, accurate, and fast prediction of coal ash.
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Date submitted2021-09-22
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Date accepted2022-03-24
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Date published2022-04-29
Predicting dynamic formation pressure using artificial intelligence methods
Determining formation pressure in the well extraction zones is a key task in monitoring the development of hydrocarbon fields. Direct measurements of formation pressure require prolonged well shutdowns, resulting in underproduction and the possibility of technical problems with the subsequent start-up of wells. The impossibility of simultaneous shutdown of all wells of the pool makes it difficult to assess the real energy state of the deposit. This article presents research aimed at developing an indirect method for determining the formation pressure without shutting down the wells for investigation, which enables to determine its value at any time. As a mathematical basis, two artificial intelligence methods are used – multidimensional regression analysis and a neural network. The technique based on the construction of multiple regression equations shows sufficient performance, but high sensitivity to the input data. This technique enables to study the process of formation pressure establishment during different periods of deposit development. Its application is expedient in case of regular actual determinations of indicators used as input data. The technique based on the artificial neural network enables to reliably determine formation pressure even with a minimal set of input data and is implemented as a specially designed software product. The relevant task of continuing the research is to evaluate promising prognostic features of artificial intelligence methods for assessing the energy state of deposits in hydrocarbon extraction zones.
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Date submitted2020-05-26
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Date accepted2020-06-10
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Date published2020-06-30
Theoretical analysis of frozen wall dynamics during transition to ice holding stage
Series of calculations for the artificial freezing of the rock mass during construction of mineshafts for the conditions of a potash mine in development was carried out. Numerical solution was obtained through the finite element method using ANSYS software package. Numerical dependencies of frozen wall thickness on time in the ice growing stage and ice holding stage are obtained for two layers of the rock mass with different thermophysical properties. External and internal ice wall boundaries were calculated in two ways: by the actual freezing temperature of pore water and by the temperature of –8 °С, at which laboratory measurements of frozen rocks' strength were carried out. Normal operation mode of the freezing station, as well as the emergency mode, associated with the failure of one of the freezing columns, are considered. Dependence of a decrease in frozen wall thickness in the ice holding stage on the duration of the ice growing stage was studied. It was determined that in emergency operation mode of the freezing system, frozen wall thickness by the –8 °C isotherm can decrease by more than 1.5 m. In this case frozen wall thickness by the isotherm of actual freezing of water almost always maintains positive dynamics. It is shown that when analyzing frozen wall thickness using the isotherm of actual freezing of pore water, it is not possible to assess the danger of emergency situations associated with the failure of freezing columns.
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Date submitted2019-03-13
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Date accepted2019-05-07
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Date published2019-08-23
Modern Mathematical Forecast Methods of Maintenance and Support Conditions for Mining Tunnel
- Authors:
- S. A. Ignatyev
- A. E. Sudarikov
- A. Zh. Imashev
The research focuses on mathematical methods of mining pressure forecast to develop rational support patterns for mining tunnels and to ensure safety of mining operations. The purpose of research is to develop the methodology of applying advanced calculation methods and software solutions based on neural networks to reduce dispersion of factors influencing stability of mining tunnels, as well as to define rational parameters of mining tunnel support. The authors review the algorithm of geomechanical process examination, which is divided into several stages. First of all, it is proposed to use cluster analysis to examine location conditions of man-made outcrops, which allows to divide all the diversity of existing conditions for mining tunnel construction. Cluster analysis first allows to reduce the dispersion of factors that influence the stability of mining tunnels in various clusters, and then to determine rational parameters of tunnel support in each cluster. After the problem of cluster analysis is solved, it is proposed to use software programs that allow to study geomechanical processes in each cluster. At this stage, both standard methods (normative techniques, numerical modelling, analogies use, etc.) and the most advanced methods – neural networks – can be applied. Described algorithm of solving geomechanical problems, which utilizes advanced numerical methods and a software package based on neural networks, ensures an individual approach to estimation of mining pressure under varying conditions of man-made outcrop location in the rock mass.
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Date submitted2018-12-25
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Date accepted2019-03-02
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Date published2019-06-25
Non-linear electrical load location identification
- Authors:
- S. Pirog
- Ya. E. Shklyarskiy
- A. N. Skamyin
The article discusses the issues of identifying the location of non-linear loads in electrical networks which makes the main contribution to the distortion of the non-sinusoidal voltage and current in the distribution network of an industrial enterprise, including mining enterprises. The existing methods for determining the location of the source of higher harmonic components in voltage and current are considered, their advantages and disadvantages are revealed. The main disadvantages of the methods used include the low accuracy and incorrectness of their use in existing enterprises. When developing a new method, the authors were faced with the task of simplicity of its use in the conditions of industrial operation of electrical equipment and the absolute correctness of the results obtained. The proposed method of identifying the source of higher harmonics is based on the variation of the parameters of the power system, in particular, the change in resistance of power transformers taking into account their transformation ratio. It is shown that by varying the transformation ratio during regulation under load, the total coefficient of the harmonic components of the voltage changes. Based on the constructed dependencies, the variation of the derivative of this function with different variations of the parameters of sources of higher harmonics is analyzed and a method is developed that allows determining the share contribution of consumers to the total harmonic component of the voltage.
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Date submitted2019-01-03
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Date accepted2019-03-23
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Date published2019-06-25
Normalization of thermal mode of extended blind workings operating at high temperatures based on mobile mine air conditioners
Thermal working conditions in the deep mines of Donbass are the main deterrent to the development of coal mining in the region. Mining is carried out at the lower technical boundaries at a depth of almost 1,400 m with a temperature of rocks of 47.5-50.0 °C. The air temperature in the working faces significantly exceeds the permissible safety standards. The most severe climatic conditions are formed in the faces of blind development workings, where the air temperature is 38-42 °С. It is due to the adopted coal seam mining systems, the large remoteness of the working faces from the main air supply openings, the difficulty in providing blind workings with a calculated amount of air due to the lack of local ventilation fans of the required range. To ensure thermodynamic safety mine n.a. A.F.Zasyadko we accepted the development of a draft of a central cooling system with ground-based absorption refrigerating machines with a total capacity of 9 MW with the implementation of the three types of generation principle (generation of refrigeration, electrical and thermal energy). However, the long terms of design and construction and installation work necessitated the use of mobile air conditioners in blind development faces. The use of such air conditioners does not require significant capital expenditures, and the terms of their commissioning do not exceed several weeks. The use of a mobile air conditioner of the KPSh type with a cooling capacity of 130 kW made it possible to completely normalize the thermal working conditions at the bottom of the blind workings 2200 m long, carried out at a depth of 1220-1377 m at a temperature of host rocks 43.4-47.5 °С. It became possible due to the closest placement of the air conditioner to the face in combination with the use of a high-pressure local ventilation fan and ducts, which ensured the air flow produced by the calculated amount of air. The use of the air conditioner did not allow to fully normalize the thermal conditions along the entire length of the blind face but reduced the urgency of the problem of normalizing the thermal regime and ensured the commissioning of the clearing face.
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Date submitted2019-01-11
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Date accepted2019-03-17
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Date published2019-06-25
Improving methods of frozen wall state prediction for mine shafts under construction using distributed temperature measurements in test wells
- Authors:
- L. Yu. Levin
- M. A. Semin
- O. S. Parshakov
Development of mineral deposits under complex geological and hydrogeological conditions is often associated with the need to utilize specific approaches to mine shaft construction. The most reliable and universally applicable method of shaft sinking is artificial rock freezing – creation of a frozen wall around the designed mine shaft. Protected by this artificial construction, further mining operations take place. Notably, mining operations are permitted only after a closed-loop frozen section of specified thickness is formed. Beside that, on-line monitoring over the state of frozen rock mass must be organized. The practice of mine construction under complex hydrogeological conditions by means of artificial freezing demonstrates that modern technologies of point-by-point and distributed temperature measurements in test wells do not detect actual frozen wall parameters. Neither do current theoretical models and calculation methods of rock mass thermal behavior under artificial freezing provide an adequate forecast of frozen wall characteristics, if the input data has poor accuracy. The study proposes a monitoring system, which combines test measurements and theoretical calculations of frozen wall parameters. This approach allows to compare experimentally obtained and theoretically calculated rock mass temperatures in test wells and to assess the difference. Basing on this temperature difference, parameters of the mathematical model get adjusted by stating an inverse Stefan problem, its regularization and subsequent numerical solution.
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Date submitted2018-09-08
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Date accepted2018-11-01
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Date published2019-02-22
Complexation of telecommunications and electrical systems in mines and under-ground facilities
- Authors:
- V. A. Shpenst
The possible options for the integration of telecommunications and electrical systems of mining enterprises are considered. Based on an analysis of the current state and prospects for the development of telecommunications systems, various technical solutions are proposed for sharing the power supply networks available in mines and underground structures in order to solve the problems of telecommunication, automate process control and ensure the safety of operations. The analysis of the possibilities of applying the PLC technology in underground structures and mines for solving specific telecommunication problems has been carried out, and examples of their possible technical and hardware implementation are given.
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Date submitted2018-07-02
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Date accepted2018-09-04
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Date published2018-12-21
Strategic Planning of Arctic Shelf Development Using Fractal Theory Tools
- Authors:
- V. S. Vasiltsov
- V. M. Vasiltsova
The paper justifies the necessity to utilize new methods of strategic planning in oil and gas field exploitation in the Arctic shelf during the implementation of high technology diversified model of development for oil and gas companies (OGC) based on principles and tools of fractal theory. It has been proved that despite its challenging conditions the Arctic represents not only resource potential of the country and a guarantee of national safety, but also a key driver of market self-identification and self-organization of OGCs. Identified and analyzed problems in institutional procurement of shelf development and utilized methods of strategic planning and project management, both on the levels of state and corporate governance, demonstrate that reductive approach of the fractal theory allows to take into account diversification of heterogeneous multicomponent project models, which can be reduced to a single management decision with inverse iterations of neural network modelling. Suggested approach is relevant for strategic planning not only on the stage of investment portfolio justification, but also for identification and assessment of project risks; ranking of projects according to the order of their implementation; back and - forth management (monitoring and supervision) and project completion. It has been detected that such basic properties of the fractal as self-similarity, recurrence, fragmentation and correlation between all fractal dimensions allow to systematize chaotically changing values of market parameters in the Arctic shelf development project, which provides an opportunity to forecast market development with minimal prediction errors.
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Date submitted2010-07-29
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Date accepted2010-09-27
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Date published2011-03-21
Efficiency increasing of condenser batteries operation in mining enterprise`s electric circuits
- Authors:
- A. N. Skamin
This work contains the method of effective reactive power compensation at the expense of high harmonics reduction. The decrease of condenser batteries overloading from the high harmonics is based on variation of condenser power depending on current and voltage spectral structure, electric network parameters and load power.
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Date submitted2009-10-26
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Date accepted2009-12-27
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Date published2010-09-22
Support of geodynamic safety in mining of the Khibini deposits
The paper deals with the problems of geodynamics in mining of the Khibini deposits. Description is given to the complex of organizational-technical arrangements for provision of geodynamic safety at the Apatit Co and to principal trends of its development.
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Date submitted2009-08-01
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Date accepted2009-10-08
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Date published2010-02-01
Advance of the metallurgical limestone shaft kilning process control system
- Authors:
- N. I. Koteleva
Today at management system engineering by metallurgical processes used to special methods of the control theory such as optimal, neuro-fuzzy and adaptive methods. First of all, it is connected with increase of problems complexity maintained in control process. In article possibility of application of neural networks is considered at improvement of a control system by process of mine roasting of limestone, are described the neural network scheme controls and the basic stages of its construction.