ABSTRACT
Kinetic hydrate inhibitor laboratory testing before field application is one of the key priorities in the oil and gas industry. The common induction-time-based technique is often used to evaluate and screen for kinetic hydrate inhibitors (KHIs). However, the main challenge relates to the stochastic nature of hydrate nucleation observed in fresh systems, which often results in scattered data on hydrate formation with unacceptable uncertainties. A much more precise KHI evaluation method, called crystal growth inhibition (CGI), provides comprehensive insights into the inhibitory behavior of a kinetic hydrate inhibitor, including both hydrate formation and decomposition. Given that industry does not require this much information, it is not feasible to expend either much time or cash on this strategy. This study aims to provide a cost-effective technique that presents maximum data accuracy and precision with relatively little time and cost expenditure. Hence, the impact of water-hydrate memory on improving the accuracy and repeatability of the results of the induction-time-based technique (IT method) was examined. First, the concept of water-hydrate memory, which contains information about how it is created, was reviewed, and then, the factors influencing it were identified and experimentally investigated, like the heating rate of hydrate dissociation and the water-hydrate memory target temperature during heating. Finally, a procedure was developed based on the background information in the earlier sections to compare the consistency of the results, originating from the conjunction of water-hydrate memory with the IT technique. The results of replications at KHI evaluation target temperatures of 12.3-12.4°C and 11.5-11.7°C showed that more repeatable data were obtained by applying water-hydrate memory, and a more conclusive decision was made in evaluating KHI performance than with an IT method. It seems that combining the IT method with water-hydrate memory, introduced as the "HME method", can lead to more definitive evaluations of KHIs. This approach is expected to gain in popularity, even surpassing the accurate but complex and time-consuming CGI method.
