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.

The presented research is focused on two issues. The first one is finding additional parameters that characterize the technical condition of an electric machine according to the current consumption. The second one is increasing of the informativeness of diagnostic methods in conditions of limited access to equipment and incomplete information. The paper discusses a method for diagnosing low and medium power asynchronous electric motors by electrical parameters. It is shown that asynchronous electric motors powered from a self-commutated voltage inverter have contained higher harmonic components in addition to the main harmonic. The non-sinusoidal voltage leads to the emerging of the highest current components in the stator winding of the motor. This is the reason for occurring of a pulsating component of the torque on the motor shaft. Therefore, there are two types of ripple: the one caused by the power supply from the self-commutated inverter and the one caused by defects. The moments’ coincidence of these ripples leads to increasing in the amplitude of oscillations. However, significant noise due to the presence of an autonomous inverter does not allow using the current spectrum to diagnose damage. Analysis of the hodographs of the Park’s vector of the stator current and voltage allows one to exclude harmonic components that were formed by the carrier frequency (and multiple of it). At the same time, analyzing the modulus of the Park’s vector and its spectrum, one can note pulsations of the amplitude values of the vector modulus corresponding to certain damage.

This paper presents a methodology for creating a soft sensor for predicting the bearing wear of electrical machines. The technique is based on a combination of Park vector methods and a classifier based on an artificial neural network (ANN‐classifier). Experiments are carried out in la-boratory conditions on an asynchronous motor of AIR132M4 brand. For the experiment, the inner rings of the bearing are artificially degraded. The filtered and processed data obtained from the installation are passed through the ANN‐classifier. A method of providing the data into the classi-fier is shown. The result is a convergence of 99% and an accuracy of 98% on the test data.