Artificial Neural Network-based detection of gas hydrate formation

Abstract

In the production process of natural gas one of the major problems is the formation of hydrate crystals creating hydrate plugs in the pipeline. The hydrate plugs increase production losses, because the removal of the plugs is a high cost, time consuming procedure. One of the solutions used to prevent hydrate formation is the injection of modern compositions to the gas flow, helping to dehydrate the gas. Dehydratation obviously means that the size of hydrate crystals does not increase. The substances used in low concentrations, have to be locally injected at the gas well sites. Inhibitor dosing depends on the amount of gas hydrate present. In the article two Artificial Neural Network (ANN)-based predictive detection solutions are presented. In both cases the goal is to predict hydrate formation. Data used come from two solutions. In the first one measurements were performed by a self-developed and -produced equipment in this case, differential pressure was used as input. In the second solution data are used from the measurement system of a motorised chemical-injector device, in this case pressure, temperature, quantity and type of inhibitor were used as inputs. Both systems are presented in the article.