No abstract

Liquefied Petroleum Gas (LPG) is one of the alternate sources of energy because of its availability and high heating value. As the interest in LPG production in Nigeria and other developing countries increases, it is imperative to study some of the flow assurance issues associated with LPG. Due to the presence of moisture in commercial LPG, hydrates can form during LPG production, transportation and storage. Hence, it is important to predict hydrate forming conditions of LPG and propose a prevention plan if the LPG production, transportation or storage system falls in the hydrate formation zone. This paper examines the mechanism of hydrate formation in LPG and how LPG hydrates can be inhibited using methanol and ethanol. The inhibitor effectiveness was evaluated by the degree of temperature depression effected by equal amounts of these inhibitors. It was discovered that Methanol performed better than Ethanol when 20wt% of methanol and 20wt% of ethanol respectively were used in inhibiting hydrates formed in LPG. The effect of dehydration on LPG hydrate formation was also examined by varying the water content of the LPG from 7wt% water to 2.5wt% water. This simple approach to hydrate inhibition will enable the Engineer reduce the risk of hydrate formation during LPG production, transportation and storage.

No abstract

In recent times, polymers and surfactants have been used to influence the kinetics of hydrate growth and coagulation. However, these Low Dosage Hydrate Inhibitors are limited in terms of water-cut and sub-cooling. This work considers using a blend of Thermodynamic Hydrate Inhibitor and Ionic Inhibitor for hydrate inhibition. A comparative study was carried out to evaluate the combined effect of Ionic and Thermodynamic Hydrate Inhibitors in preventing hydrate formation by combining their temperature depression using Hammerschdmidt and Østergaard equation. This work finds application in hydrate inhibition by reducing the dosage of inhibitors. This will be useful for deepwater reservoirs and flowlines having large subcooling temperature and (or) high water-cut. The Thermodynamic Hydrate Inhibitors used are Methanol, Monoethylene Glycol and Diethylene Glycol while the Ionic Inhibitors used are Calcium Chloride, Sodium Chloride and Potassium Chloride salts. It was observed that the dosage of Thermodynamic Hydrate Inhibitors reduced by over 14% when it was combined with Ionic Inhibitors. The best blend was the hybrid of Methanol and Sodium Chloride salt which saved about 34% of methanol. This simple approach to hydrate inhibition will enable the Engineer reduce the use of methanol or glycols based on the salinity of the formation water. The operator can also save a lot by using the hybrid instead of methanol or glycol alone.

Gas hydrate deposition is one of the major Flow Assurance problems in petroleum production in the offshore environment. Therefore, is important to accurately predict hydrate formation conditions and avoid these conditions or propose a hydrate management plan. This study compares the effectiveness of Artificial Neural Network (ANN) for predicting hydrate formation temperature to the effectiveness of other hydrate temperature prediction correlations such as: Towler and Mokhtab correlation, Hammerschmidt correlation and Bahadori and Vuthalaru correlation. The ANN was trained using 459 hydrate formation experimental data points from Katz chart and Wilcox et al chart. Pressure (P) and specific gravity (ϒ) were chosen as the inputs in the 4-layer network while temperature was the output. The data points were for gases of specific gravity of 0.5539, 0.6, 0.7, 0.8, 0.9 and 1.0. The experimental pressures considered were from 49 psia to 4000 psia. The Neural Network was built using an excel add-in tool, NEUROXL. ANN accurately predicted the experimental hydrate formation temperature with the regression coefficient greater than 0.98 for the different specific gravities considered. Moreso, the error analysis shows ANN performed better than Towler and Mokhtab correlation, Hammerschmidt correlation and Bahadori and Vuthalaru correlation because it had the least Mean Absolute percentage error, MAPE (3.5) compared to the other correlations. ANN is a viable tool for hydrate prediction and the current model can be improved upon by including more experimental data in the ANN.