The presence of paraffin in crude oil often leads to wax deposition in pipelines when temperatures reach below Wax appearance temperatures. It is possible to simulate the wax build up using thermodynamic and kinetic/deposition models for estimating wax deposition with varied degree of accuracy. The most popular remediation technique for wax removal is direct mechanical cleaning using pigs as these are generally cheaper than chemical inhibitors. The pigging frequency is mostly determined using thumb rules and operating experience. Correct pigging frequency is essential to avoid significant wax build-up in pipeline that may lead to plugging or stuck pig. This becomes more acute in subsea lines where insufficient well head pressures driving the pig may not scrap out the wax plug ahead of pig. This can increase to downtime and can lead to expensive pipeline repair. In Mumbai offshore pigging is most common method of wax removal in well fluid lines. In this paper a simulation model using dynamic simulator for predicting daily wax build-up and pigging shall be discussed. The model is based on the actual pipeline condition, fluid parameters and pigging data. Extensive lab studies on actual comingled well fluid samples from platform have been carried out for capturing the Fluid & Wax properties. The model has been used to run what if scenarios by varying the pigging frequency for the pipelines. The impact of ambient temperature or increase in gas (lift gas) in well fluid on pigging has been studied. Sensitivity studies have been carried out for various parameters like wax plug friction, bypass opening, change in well fluid parameters, etc.

Natural gas hydrate formation is a costly and challenging problem for the oil and gas industry. Prediction of hydrates have been carried out through rigorous and laborious solving of mathematical equations called equations of state (EOS) which give accurate results but require appropriate setup and time. Few examples of such equations of state currently used by industry benchmarked software tools include Peng-Robinson (PR), Cubic-Plus-Association (CPA), Soave-Redlich-Kwong (SRK) etc. which more or less provide us with an accurate hydrate stability curve i.e. a pressure-temperature profile for a given composition, which allows us to keep the pressures and temperatures (operating conditions) out of the hydrate stability zone. Hydrate stability curves are a function of the composition of the fluid (gas) being produced. Compositional changes in the percentage of C1 to C7+ components of gas, would not only affect the specific gravity, but would also change the hydrate stability curve of the gas significantly. Previous studies have been aimed at finding a quick and precise prediction method for hydrate formation, so as to make swift arrangements to counter any chance of flow assurance issue. Different empirical correlations have been developed on the basis of the composition of the gas being produced that take into consideration the pressure and predict the temperature of hydrate formation. Multiple data points, i.e. fluid compositions from different areas/fields are considered and correlations have been developed to fit the hydrate stability zones of these data points which were found through a more accurate equation of state. As the initial data sets for each correlation are different, the possibility of any two correlations giving the correct and same prediction is very low. This paper gives an insight into how different empirical correlations like Hammershmidt, Motiee, Makogon, Towler and Mokhtab etc., that have already been derived can be used with better accuracy for a set of different fluid compositions and specific gravities. A sensitivity analysis is done on the performance of each correlation against the accurate hydrate curves found out through the software tool, using different available equations of state. The data points picked here are random and were not included in any data sets adopted for derivation of the correlation. Furthermore, the mimicked hydrate curve from this new method is cast against the software simulated hydrate curve for a flow assurance steady state simulation study with two deepwater gas wells with different gas compositions. The results of the study suggest that the use of the imitated hydrate curve through analytical approach works well in predicting the hydrate stability zone. It would also not require any software proficiency, would give quick results and would cost a fraction compared to the state of the art simulators.

Subsea flow lines in deep water are typically exposed to high pressure and low temperature conditions which can create problems due to formation of gas hydrate. The gas hydrate formed can plug the flow lines causing not only loss of production, but may also create severe safety and environmental hazard. Moreover, dissociation of these plugs may take weeks or even months. Assessment of the hydrate formation potential during both steady is therefore an essential part of field development studies. The paper presents a case study of a gas field located in KG basin of India which was brought on production in 2018. The objective of the study was to assist the on-site team on issues related to hydrate inhibition during ongoing initial start-up operation and assess the arrival time of rich MEG in the onshore plant in view of turn down flow conditions during commissioning. The study also demonstrates how the transient simulations helped to monitor progress, identify and respond quickly to address the challenges during initial start-up operation of the deepwater gas field in Indian east coast. It emphasizes the need for accurate estimation of rich MEG arrival time and the minimum required gas flow rate from the subsea wells to ensure timely return of rich MEG to the onshore plant in order to avoid disruption in hydrate inhibition in the subsea system.