Exploring of the Incompatibility of Marine Residual Fuel: A Case Study Using Machine Learning Methods

Sultanbekov, Radel; Белоглазов, И. И.; Islamov, Shamil; Ong, Muk Chen

doi:10.3390/en14248422

Cited by 29 publications

(22 citation statements)

References 38 publications

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“…The resulting critical viscosity value serves as an upper limit for the viscosity of the technological liquid, above which a technogenic fracture as a result of an autohydraulic fracture may occur, so the current viscosity value must be compared with the specified limit value [42].…”

Section: Calculation Of Hydraulic Fracturing Parametersmentioning

confidence: 99%

Technique for calculating technological parameters of non-Newtonian liquids injection into oil well during workover

Mardashov

Bondarenko

Raupov

2022

PMI

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Technique for automated calculation of technological parameters for non-Newtonian liquids injection into a well during workover is presented. At the first stage the algorithm processes initial flow or viscosity curve in order to determine rheological parameters and coefficients included in equations of rheological models of non-Newtonian fluids. At the second stage, based on data from the previous stage, the program calculates well design and pump operation modes, permissible values of liquid flow rate and viscosity, to prevent possible hydraulic fracturing. Based on the results of calculations and dependencies, a decision is made on the necessity of changing the technological parameters of non-Newtonian liquid injection and/or its composition (components content, chemical base) in order to prevent the violation of the technological operation, such as unintentional formation of fractures due to hydraulic fracturing. Fracturing can lead to catastrophic absorptions and, consequently, to increased consumption of technological liquids pumped into the well during workover. Furthermore, there is an increased risk of uncontrolled gas breakthrough through highly conductive channels.

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Section: Calculation Of Hydraulic Fracturing Parametersmentioning

confidence: 99%

Technique for calculating technological parameters of non-Newtonian liquids injection into oil well during workover

Mardashov

Bondarenko

Raupov

2022

PMI

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“…This experience of using machine learning methods proves the high efficiency of their application in practice. They are suitable for solving a large number of problems that enterprises face on a daily basis when developing oil and gas fields [31,32]. One of such important tasks is the preventive detection of such a negative phenomenon as the behind-casing crossflow.…”

Section: Introductionmentioning

confidence: 99%

Application of machine learning methods for predicting well disturbances

et al. 2023

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In the process of field exploration, along with regular flooding, a significant part of the wells is flooded prematurely due to leakage of the string and outer annulus. In an effort to intensify the flow of oil to the bottom of wells in field conditions, specialists often try to solve this problem by using various technologies that change the reservoir characteristics of the formation. Any increase in pressure that exceeds the strength of the rocks in compression or tension leads to rock deformation (destruction of the cement stone, creation of new cracks). Moreover, repeated operations under pressure, as a rule, lead to an increase in water cut and the appearance of behind-the-casing circulations. For that reason, an important condition for maintaining their efficient operation is the timely forecasting of such negative phenomena as behind-casing cross flow and casing leakage. The purpose of the work is to increase the efficiency of well interventions and workover operations by using machine learning algorithms for predicting well disturbances. Prediction based on machine learning methods, regression analysis, identifying outliers in the data, visualization and interactive processing. The algorithms based on oil wells operation data allow training the forecasting model and, on its basis, determine the presence or absence of disturbances in the wells. As a result, the machine forecast showed high accuracy in identifying wells with disturbances. Based on this, candidate wells can be selected for further work. For each specific well, an optimal set of studies can be planned, as well as candidate wells can be selected for further repair and isolation work. In addition, in the course of this work, a set of scientific and technical solutions was developed using machine learning algorithms. This approach will allow predicting disturbances in the well without stopping it.

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“…According to this document, it is necessary to increase the production of metallurgical products with high added value. Moreover, the priority of the state is to stimulate the improvement of the technical level of production of Russian companies in order to increase the efficiency of processing mineral raw materials [48][49][50][51][52][53][54].…”

Section: Introductionmentioning

confidence: 99%

Investigation of the ytterbium reduction process in the synthesis of Al – Yb master alloys for the modification of aluminum alloys

Bazhin¹,

Savchenkov²,

Gordevnin³

2022

nfm

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This work investigates the synthesis process and characterization of a novel type of aluminum-ytterbium master alloy in molten salts. The significance of the obtained results for the research conducted in the field so far is due to the previously proven effectiveness of the influence of ytterbium on the structure and properties of aluminum alloys. During experimental work, the thermodynamic analysis of the aluminothermic process of ytterbium fluoride reduction from molten salts was performed and revealed that the process might occur and develop in the presence of sodium fluoride and potassium chloride if the NaYbF 4 precursor is formed. Subsequently, the temperature intervals of thermal effects during the process of reduction of ytterbium from its fluoride in molten salts (NaF -KCl) by aluminum were determined as a result of thermal analysis. During Al -Yb master alloys synthesis experiments, the maximal value of ytterbium extraction to master alloy of 82.5% was achieved by changing the process parameters, such as temperature, reaction time, and molten salts/aluminium ratios. Besides, master alloys containing ytterbium from 1.1 to 5.1 wt.% were synthesized using the optimized technological process conditions and examined with metallographic analysis. The analysis revealed that the microstructure of the synthesized master alloys is presented by eutectic colonies of Al + Al 3 Yb composition located along the boundaries of α-Al dendritic cells. It was found that about 85.5% of the area of the studied samples with ytterbium content of 5.1 wt.% consist of aluminum dendrites with an average cell size of 25 μm. The presented in this research results are of significant importance for the development of cheap and high-quality aluminum alloys.

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Exploring of the Incompatibility of Marine Residual Fuel: A Case Study Using Machine Learning Methods

Cited by 29 publications

References 38 publications

Technique for calculating technological parameters of non-Newtonian liquids injection into oil well during workover

Technique for calculating technological parameters of non-Newtonian liquids injection into oil well during workover

Application of machine learning methods for predicting well disturbances

Investigation of the ytterbium reduction process in the synthesis of Al – Yb master alloys for the modification of aluminum alloys

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