Abstract
In this article the main problem of gas industry is shown. This problem if gas hydrate prevention especially in the gas flow lines. The most popular way of gas hydrate prevention is gas hydrate inhibitor supply. As usual it is methanol. To optimize methanol flow rate it is offered to diagnose the conditions of hydrate formation in the flow lines. This problem could be solved by developing the automatic system of diagnosis progressing of hydrate conditions appearance in the flow line and the beginning of this process. Sony well known methods of such system building are shown in this article. According to carried out research we can say that many factors are affected these systems. The correlation between all these factors could not been described analytically. Also there is no enough quantitative information about these factors. So that is why these systems are appropriately built based on fuzzy cognitive map. The cognitive map is made in order to make up for a deficiency in quantitative information and allows to detect the most important (key) factors. These key factors characterize the correlation between the object and environment and relate with them. Command variables in the developed system are formed based on online measured pressure and temperature conditions in the beginning and in the end of the flow line, ambient temperature and water dew point temperature. Also well flow rate, gas composition and its density are important. The selected key factor is theoretical hydrating temperature. The changing of the coefficient of heat transmission and other factors (abrasive particles and water vapors in gas, ground condition and surface relief, snow cover and its condition and others) affects on hydrating temperature. To illustrate the main idea the fuzzy cognitive model is offered in the article. This model corrects the coefficient of heat transmission, and it helps to count theoretical hydrate temperature more accurately. As a consequence it increase the accuracy of methanol supply.
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