SPECIAL AWARDS 2019

Research Summary

In the Russian Federation, the well stock is mainly represented by low- and medium-rate recover
wells, the flow rate of which does not exceed 20 cubic meters per day and which are at the last stage of field development, which is characterized by a constant increase in the water content of the reservoir, a large number of mechanical impurities, the presence of hard-to-recover reserves, as a result of which there is a constant decrease in production of producing wells.

Over the past 5 years, new and modern methods of field exploitation have become widely used in the Russian Federation and the world. The new methods include: simultaneous-separate operation, which implies the exploitation of several productive horizons by one well; drilling and operation of small lateral wells. The largest number of modern well-known schemes designed for simultaneous-separate operation and operation of the sidetracks is associated with the most energy-efficient equipment, namely, well pumping sucker-rod units and diaphragm pumps.

Analyzing information about the equipment efficiency, it can be concluded that the main direction in improving the strategy for extracting oil from small and medium production wells is to optimize the structures and increase the reliability of energy-efficient equipment that will allow the development process to be carried out systematically and most efficiently. This result can be achieved by optimizing the design and process of equipment operation by changing the basic methods of calculating energy-efficient equipment. At the same time, the use of optimization of structures and methods of calculation allows you to increase the operating time before equipment failure by more than 50%, increase the flow rate of wells, correctly select for specific operating conditions, improve the energy efficiency of the installation as a whole on the rise of 1 cubic meter of liquid.

Research Summary

A new mathematical model is proposed using machine learning techniques for estimating PVT fluids properties and reservoir rock properties. The results obtained show that the proposed model provides better estimation and higher accuracy than the published studies. The present model provides predictions with high accuracy. The main applicable results of this study are reducing manpower, making the model more robust and quantifying uncertainties. The model for estimation reservoir fluid and rock properties was based on artificial neural networks and developed using published data sets from the Middle East. This improvement in PVT and reservoir characterization calculation accuracy will be of invaluable support for simulations and designs applied in the Oil industry. The result of this study will help to improve plans for exploration and development.

Research Summary

Corrosion protection is an essential part of the maintenance of oil and gas transport and storage infrastructure. However, any protective coatings are prone to wear and mechanical damage. To control the integrity and functionality of the coatings, operators need to perform the meticulous and precise measurements; many of them are not easily reproducible in the field.

Raman spectrometers are particularly useful analytical devices for the non-destructive testing: they provide a lot of information about the sample, they can be employed for the spectra collection without any sample preparation or dissection, and are cheaper and cheaper as the high-quality lasers and detectors are mass produced nowadays. Handheld Raman spectrometers find extensive usage in geology, forensics, and pharmacology.

Raman spectra are good fingerprints for the pure compounds. However, the diagnostic Raman label must be present in low concentration in the protective coatings to not impair their properties. Fortunately, there is a technique for the detection of the chemicals in extremely low quantities: surface-enhanced Raman spectroscopy.

In the proposed project, we will develop the Raman labels combining aluminosilicate carrier decorated with gold or silver nanoparticles, and the Raman reporter molecules. These labels will provide good dispersibility, positive influence on the coating strength, and the excellent signal even in the low concentrations.