The hydrotransport system of Mikhailovsky GOK PJSC used for iron ore processing tailings was studied with the aim of establishing its performance in slurry pipelines with and without polyurethane coatings of their internal surfaces under mass concentration variations of 10 to 70 %, as well as for preparing related recommendations for the enterprise. Theoretical and laboratory studies were completed with the development of a methodology for recalculating the experimental laboratory data for the specific head loss in industrial pipelines. It has been established that the use of internal polyurethane coatings significantly reduces the specific head losses in hydraulic transportation of thickened tailings slurry for iron ore processing tailings. The respective feasibility calculations confirm the economic efficiency of using steel pipes with internal polyurethane coating in hydrotransport slurry pipelines. The calculated specific head losses for the pipelines with a polyurethane coating in the hydraulic transportation of processing tailings slurry with the mass concentration of the solid phase of 6.95 % are almost 1.8 times lower than in steel pipelines without such coating. It is proposed to use the results obtained in the reconstruction of the tailings slurry hydrotransport system at Mikhailovsky GOK PJSC with a conversion to hydraulic transportation of bulk concentration tailings thickened to 35–40 % in slurry pipelines with an internal polyurethane coating, which will ensure energy savings in the hydrotransport process.

The research considers conducting orthogonal experiment (OT) as one of the stages in developing a new discrete element method (DEM) parameters calibration approach. The measured responses in experiment are the parameters obtained by DEM animation processing using machine vision system (MVS). The variable factors in experiment are DEM parameters. A brief overview of an existing calibration approaches given in the article. The choice of OT as a design of experiment tool among other mathematical tools discussed. Experiments conducted using specially developed rig where bulk material's flow captured as DEM animation. DEM animation converted to video and then processed using MVS that allow register the values of such parameters as angle of repose or expiration time (measured responses). The results of the OT show that it is possible to identify four measured responses with the most valuable correlation coefficient. DEM parameters with the biggest influence on the measured responses identified for each of the obtained regression. Obtained results are useful in learning or iterative algorithms development for DEM parameters calibration.

Improving the tools and mathematical methods to diversify the portfolio of oil and gas assets in the face of limited investment, high market volatility, increasing risks and uncertainties at the current level of technology development is a very urgent task. In order to form an effective investment portfolio, the authors proposed asset diversification using cluster analysis, which implies grouping sample objects according to a set of specific features. The method under consideration involves five stages of asset valuation in order to consider those assets in a three-dimensional space, taking into account the specifics of the oil and gas business, including determination of individual asset trajectory, performing spatial approximation, calculating the clustering coefficient, ranking the resulting pairs, and directly solving the portfolio formation optimization problem. This paper provides a reasonable set of metrics for diversification of the investment portfolio based on cluster analysis: main criterion - ∆PV, additional criteria - ∆Production, ∆OPEX/toe, ∆CAPEX/toe, characterized geological, environmental, social and economic aspects. Thus, the proposed methodology provides an opportunity to identify the most attractive investment projects, thereby allowing large oil and gas companies to diversify their business with minimal risk and maximum return on invested capital.