Estimating gas hydrate inhibitor loss: â€¨a case study
A predictive tool is used to understand and predict the loss of methanol during natural gas hydrate inhibition
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The amount of methanol to be injected as a hydrate inhibitor must not only be sufficient to prevent freezing of the inhibitor water phase, but also to provide for the equilibrium vapour-phase content of the inhibitor and the amount that is soluble in the condensate liquid phase. In this article, a case study is presented showing how the information gained from a predictive tool can be used to understand and predict the loss of methanol during natural gas hydrate inhibition. According to this study, more than $3700/day is the cost of lost methanol for 3 x 106 m3/d of natural gas.
Gas hydrate formation in natural gas and natural gas liquids (NGL) systems can block pipelines, equipment and instruments, restricting or interrupting the flow, which leads to safety hazards and substantial economic risks.1,2
Methanol is the most commonly used hydrate inhibitor in subsea petroleum industries, gas treatment and processing, pipelines and wells, with worldwide usage worth several million dollars per year.1 Due to its high volatility, methanol is lost in the vapour phase. Often, when applying methanol as an inhibitor, there is a significant expense associated with the cost of lost methanol.
The amount of methanol injected to treat the water phase, including the amount of inhibitor lost to the vapour phase and the amount that is soluble in the hydrocarbon liquid phase, equals the total amount of required methanol.
In addition, one of the primary factors in the selection process is related to the possibilities for recovery, regeneration and reinjection of the spent material. Usually, methanol is not regenerated because of its intermittent application (mainly during start-up or shutdown). However, when it is injected continuously, as is often observed in gas systems, it is sometimes regenerated.1,2 The losses to the vapour phase can be prohibitive, in which case operators select monoethylene glycol.
Considering all of these issues, there is a significant need for the development of an accurate and simple-to-use predictive tool to represent methanol loss during gas hydrate inhibition. Predictive tools to minimise the complex and time-consuming calculation steps are also an essential requirement. It â€¨is apparent that mathematically compact, simple and reasonably accurate equations, which contain fewer tuned coefficients, would be preferable for computationally intensive simulations. The present study discusses the formulation of a novel and simple predictive tool that could be of significant importance to natural gas engineers.
Methanol vapourisation loss during gas hydrate inhibition
Equation 1 shows a definition of methanol vapourisation loss: methanol vapour composition to methanol liquid composition. Equation 2 is an Arrhenius-type function to correlate methanol vapourisation loss as a function of temperature (°K), wherein the relevant coefficients (see Table 1) are correlated as a function of pressure in kPa(abs) (Equations 3–6).
kg methanol (1)
LM = Million standard m3 gas
Mass% methanol in water phase
ln(LM) = a + b + c + d
T T2 T3 (2)
a = A1 + B1 + C1 + D1
P P2 P3 (3)
b = A2 + B2 + C2 + D2
P P2 P3 (4)
c = A3 + B3 + C3 + D3
P P2 P3 (5)
d = A4 + B4 + C4 + D4
P P2 P3 (6)
The novel tools proposed in the present work are simple formulations. Furthermore, the selected exponential function to develop the tool leads to well-behaved (that is, smooth and non-oscillatory) equations, enabling fast and more accurate predictions.
Methanol loss in condensate liquid phase during gas hydrate inhibition
Equation 7 represents the proposed governing equation in which four coefficients are used (Equations 8–11) to correlate methanol solubilities in the liquid hydrocarbon phase (ω) in mole fraction as a function of temperature (T) for a given methanol mass fraction in the aqueous phase, where the relevant coefficients are shown in Table 2:
ln(ω) = a1 + b + c + d
T T2 T3 (7)
a = A1 + B1ψ + C1ψ2 + D1ψ3 (8)
b = A2 + B2ψ + C2ψ2 + D2ψ3 (9)
c = A3 + B3ψ + C3ψ2 + D3ψ3 (10)
d = A4 + B4ψ + C4ψ2 + D4ψ3 (11)
These optimum tuned coefficients help to cover temperatures in the range â€¨240–320°K and methanol concentrations up to 0.70 mass â€¨fraction in the aqueous phase. â€¨The optimum tuned coefficients shown in Table 2 can be further retuned quickly according to â€¨the proposed approach1,2 if â€¨more data become available in the future.
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