Development of methods for predicting hydrate formation in gas storage facilities and measures for their prevention and elimination
Authors: Volovetskyi V.B. , Doroshenko Ya. V. , Matkivskyi S.V. , Raiter P.M. , Shchyrba O.M. , Stetsiuk S.M. , Protsiuk H.Ya. 
Published: May 2023

The purpose of this work is to study the processes of hydrate formation during the operation of wells and underground gas storage facilities. Development of a set of measures aimed at the prediction and timely prevention of hydrate formation in wells and technological equipment of gas storage facilities under different geological and technological conditions.The prediction of hydrate formation processes was carried out using a neural network that is a software product with weight factors calculated in MATLAB environment and the ability to adapt parameters of the network specified to updated and supplemented input data during its operation. So, within the MATLAB software environment, a software module of a two-layer artificial neural network with a random set of weight factors is created at the first stage. In the second stage, the neural network is trained using experimental field input/output data set, output data. In the third stage, an artificial neural network is used as a means of predicting hydrate formation with the ability to refine weight factors during its operation subject to obtaining additional updated data, as an input set, for modifying the coefficients and, accordingly, improving the algorithm for predicting of an artificial neural network. In the absence of new data for the additional training of an artificial neural network, it is used as a computing tool that, on the basis of input data about the current above-mentioned selected technological parameters of fluid in the pipeline, ensures the output values in the range from 0 to 1 (or from 0 to 100%), that indicates the probability of hydrates formation in the controlled section of the pipeline. Application of such an approach makes it possible to teach; additionally,, that is, to improve the neural network; therefore this means of predicting hydrate formations objectively increases reliability of results obtained in the process of predicting and functioning of the system.The authors of the work recommend to carry out an integrated approach to ensure clear control over the operation mode of wells and gas collection points.According to the results of experimental studies, the places of the most likely deposition of hydrates in underground gas storage facilities were identified, in particular, in the inside space of the flowline in places of accumulation of liquid contaminants (lowered pipeline sections) and an adjustable choke of the gas collection point. The available methods used to prevent and eliminate hydrate formation both in wells and at gas field equipment were analyzed. Such an analysis made it possible to put together a list of methods that are most appropriate for the conditions of gas storage facilities in Ukraine.The method of predicting hydrate formation in certain sections of pipelines based on algorithms of artificial neural networks is proposed. The developed methodology based on data on values of temperatures and pressures in certain sections of pipelines allows us to predict the beginning of the hydrate formation process at certain points with high accuracy and take appropriate measures.To increase the efficiency of solving the problem of hydrate formation in gas storage facilities, it is expedient to introduce new approaches to timely predict complications, in particular, the use of neural networks and diverse measures.Implementation of the developed predicting methodology and methods and measures to prevent and eliminate hydrate formation in wells and technological equipment in underground gas storage facilities will increase the operation efficiency of underground gas storage facilities.The use of artificial intelligence to predict hydrate formations in flowlines of wells and technological equipment of underground gas storage facilities is proposed. Using this approach to predict and functionthe system as a whole ensures high reliability of the results obtained due to adaptation of the system to the specified control conditions.