INTELLIGENT EXPERT HYDRATE INHIBITOR FLOW CONTROL SYSTEMS IN FLOWLINES

The article presents the development of an intelligent system for determining the optimal volume of hydrate formation inhibitors to be injected into field gas gathering systems. It describes the system's operating principles, methods of analyzing and forecasting hydrate formation, and the results of testing the developed model. The relevance of this work is driven by the fact that existing methodological approaches are insufficient for adequately assessing the risks of hydrate formation in gas gathering systems, which can lead to significant material and financial losses. To justify the most accurate method for calculating the hydrate equilibrium curve to predict the onset of crystalline structure formation in the gas flow, the study presents the results of modeling the equilibrium curve for a multicomponent gas mixture from one of the Cenomanian gas reservoirs. The analysis was conducted using widely applied methodologies available in software such as Pipesim and Aspen HYSYS, which are based on different equations of state.To enhance the accuracy of simulation modeling, the proposed risk analysis methodology includes a supplementary algorithm developed and integrated into the Python environment. This algorithm, based on basic mathematical balance principles, allows for the recombination of the component composition of the gas flow for each individual segment of the field pipeline. The algorithm takes into account the inlet composition based on the production rates of wells entering a specific network section, the component composition and mass of each element, and the presence of water in the flow. In combination, the proposed methodology for risk analysis and mitigation in hydrate formation processes not only enables the use of the resulting composition to accurately calculate the hydrate formation temperature by integrating an algorithm similar in function to the hydrate equilibrium curve module from Pipesim, but also determines the optimal volume of inhibitor to mitigate the risk of hydrate plugs in pipeline sections, significantly improving the model's calculation accuracy by considering the multiphase flow composition for each specific gathering system segment. The scientific novelty of this work lies in the development of a new comprehensive approach to hydrate formation risk analysis for each segment of the well product gathering system. This approach is partially based on existing methodologies and incorporates machine learning technologies to promptly detect hydrate formation risks and determine the necessary inhibitor volume to mitigate these risks. The obtained results of calculations of the risk of hydrate formation and inhibitor for cycles allow us to conclude that the reliability of the operation of field gas pipelines due to the premature identification of potentially dangerous places in the operation of the system and the timely supply of a hydrate formation inhibitor to compensate for them, which together reduces material losses from excessive consumption of the inhibitor and from stopping the pipeline due to the formation of a hydrate plug, using the developed intelligent methodology.