Article pubs.acs.org/EF
Determination of Hydrate Inhibitor Concentrations by Measuring Electrical Conductivity and Acoustic Velocity Jinhai Yang* and Bahman Tohidi Centre for Gas Hydrate Research and Hydrafact Limited, Institute of Petroleum Engineering, Heriot-Watt University, Edinburgh EH14 4AS, United Kingdom ABSTRACT: Deployment of hydrate inhibitors is the most common measure to prevent hydrate blockage in oil and gas transport pipelines. There is an increasing demand for simple, quick, and reliable means for monitoring hydrate inhibitor concentrations in pipelines to optimize the injection rate of hydrate inhibitors, improve the economy and reliability, and reduce the potential negative impact on the environment and product quality. This paper reports a novel method for determining salt and inhibitor concentrations in aqueous solutions. This method was developed on the basis of measuring electrical conductivity and acoustic velocity of the aqueous solutions under examination. Artificial neural network (ANN) correlations were trained, validated, and tested using the measured conductivity and velocity data for monoethylene glycol (MEG)−salt solutions, methanol (MeOH)−salt solutions, and kinetic hydrate inhibitor (KHI) Luvicap EG−salt solutions. The developed ANNs can simultaneously determine thermodynamic hydrate inhibitors (THIs) (MEG and MeOH) and salts or KHIs (Luvicap EG) and salts with good accuracy. This method can provide hydrate flow assurance operations with a simple, quick, and reliable means for monitoring hydrate inhibitor and salt concentrations in pipelines.
1. INTRODUCTION Light hydrocarbon components (e.g., methane, ethane, and propane) in natural gas or oil (crude oil and condensate) could form gas hydrates in hydrocarbon transport pipelines and processing facilities of the production plants, blocking the pipelines, damaging the processing facilities, and even temporarily suspending oil and gas production.1 Gas hydrate formation could occur in deepwater drilling operations, causing serious safety problems.2,3 As a result, a variety of chemical additives known as hydrate inhibitors were developed to avoid hydrate problems. Gas hydrates are ice-like crystalline solids, which are formed by enclathrating specific gas molecules in water lattices at certain thermodynamic conditions.4 Therefore, a straightforward way to prevent hydrate formation is removing one of the elements that is essential for hydrate formation.5 For example, temperature or pressure in a pipeline can be controlled outside the hydrate stability zone (HSZ) by thermal insulation, external heating, and lowering the operation pressure. Second, water can be removed by dehydrating the gas using glycol. However, these techniques are not economically feasible for all in situ conditions, particularly in offshore and deepwater environments because of the high cost of insulation, heating, and dehydration.6 Hydrate inhibitors are the most common option to prevent hydrate impeding problems. There are two categories of hydrate inhibitors, i.e., thermodynamic hydrate inhibitors (THIs) and low-dosage hydrate inhibitors (LDHIs). Alcohol, including methanol (MeOH) and monoethylene glycol (MEG), is used as a typical THI, which shifts the hydrate phase boundary to a lower temperature and higher pressure by reducing the water activity. For high water-cut (30−60 mass %) and severe conditions, a high concentration (up to 60 mass %) and, hence, a large volume of MeOH and MEG will be required to have adequate inhibition, which may lead to a significant © 2012 American Chemical Society
increase in CAPEX and OPEX and also cause a negative impact on the environment.7,8 Therefore, LDHIs have been increasingly employed in the past 2 decades.9 LDHIs are subdivided into kinetic hydrate inhibitors (KHIs) and anti-agglomerants (AAs).10−14 Typical doses are around 1−3 mass % in the commercial formulations, which is much smaller compared to those of THIs. KHIs control hydrate formation by either delaying hydrate nucleation or hindering hydrate growth within a certain degree of subcooling,15 while AAs allow for hydrate formation but prevent hydrate crystals from agglomeration (i.e., maintain the oil transportable). The inhibitor concentration in a pipeline needs to be monitored for optimizing hydrate inhibition, i.e., not only ensuring adequate inhibition but also avoiding over inhibition, because varying thermodynamic conditions and water-cut in a pipeline will result in changes in the degree of hydrate inhibition. Inadequate inhibition will be a risk of impeding flow in a pipeline because of possible hydrate blockage, whereas overdosage could cause unnecessary costs and a severe impact on the environment. In industrial practices, chromatography is used to analyze THI (alcohol) concentrations and the colorimetric method is used to determine KHI concentrations in produced water samples. However, these methods are complex and require skilled technicians and a long time to prepare the required samples.16,17 In this paper, a novel method is reported to measure hydrate inhibitor concentrations in aqueous solutions. It is based on the measurement of electrical conductivity and acoustic velocity of the aqueous solutions. Artificial neural network (ANN) correlations were developed to determine both hydrate Received: October 29, 2012 Revised: December 30, 2012 Published: December 31, 2012 736
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because it yielded a better resultant precision. A pure linear transfer function was used in the output layer. The following equations give the output salt and inhibitor concentrations:
inhibitor (THIs and KHIs) and salt concentrations using the measured conductivity and velocity. By comparison to the conventional chromatography and colorimetric methods, this method is simple, fast, and also reliable, with potential for online application.18
f (n) =
e n − e −n e n + e −n
(1)
2. METHODOLOGY
n = W iI + bi
(2)
There is not much literature dealing with test methods to determine hydrate inhibitor concentrations. Henning et al.19 accurately measured concentrations of the chemicals, such as MeOH and MEG, in the absence of any salts using an acoustic multisensor system. Other researchers determined the hydrate suppression temperature by measuring the electrical conductivity of aqueous solutions containing salts only.20 However, typical produced water from hydrocarbon transport pipelines normally contains salts and one chemical inhibitor at least. An empirical correlation was developed to determine both MEG and NaCl concentrations based on measurements of the electrical conductivity and density of the water samples under examination.21 However, it can be anticipated that this density-based empirical correlation is hardly applicable to real produced water samples because produced water usually contains solid particles (sands and clays) and oil droplets that will significantly affect the accuracy of the density measurement. 2.1. Conductivity−Velocity Method. Two physical properties at least need to be measured to be able to determine both inhibitor and salt concentrations simultaneously. In this work, a new method based on the measurement of the electrical conductivity and acoustic velocity was developed. Electrical conductivity of aqueous solutions depends upon the concentration and mobility of ions.22 Aqueous fluids in a pipeline are usually electrolyte solutions; therefore, its conductivity is proportional to the salt concentration in a certain concentration range of the salts. The mobility of the ions will be affected by the temperature and impurities, such as nonconductive chemical additives. Therefore, the measured electrical conductivity can be directly correlated to the concentrations of chemicals, such as salts and hydrate inhibitors. Acoustic velocity was selected as the second parameter (acoustic property). It is independent of the electrical conductivity (electrical property), i.e., not correlated with the electrical conductivity in any way. Clay and Medwin23 developed empirical correlations that related acoustic velocity to the salinity of seawater. Acoustic velocity was also applied to investigate a variety of solutions, even composition of binary gas mixtures.24−27 2.2. ANN. ANN correlations were developed to determine the inhibitor and salt concentrations simultaneously using the measured electrical conductivity, acoustic velocity, and temperature. An ANN is trained by trial and error and requires neither an analytical formula nor an understanding of the physical relationships behind it. Therefore, ANN is especially useful for some applications where multiparameter correlations are needed, while the interaction and the relations between the measured parameters are not well-known.28−30 In this specific application, simple feed-forward ANNs were constructed with three layers, including an input layer, a hidden layer, and an output layer. The input layer had three neurons corresponding to the measured acoustic velocity, electrical conductivity, and temperature, while the output layer had two neurons corresponding to the inhibitor and salt concentrations. The number of neurons in the hidden layer was decided on the basis of a trial and error method. In general, the smaller the number of neurons, the better the ANN, as long as a desired output precision can be achieved. A large number of neurons may result in overfitting, which means that the ANN is not capable of “thinking” (i.e., poor precision in interpolation and extrapolation), even though the training precision may be high. There were five neurons in the hidden layer of the KHI (Luvicap EG) ANN and nine neurons in the hidden layer of the MeOH and MEG ANNs. The Levenberg−Marquardt optimization algorithm was used for adjusting the weights.31,32 Two typical transfer functions (i.e., exponential sigmoid and tangent sigmoid) were tested, and the tangent sigmoid transfer function was used in the hidden layer
O = W on + bo
(3)
where f(n) is the transfer function, I and O are the input matrix (3 × 1) and output matrix (2 × 1), respectively, n is the net input matrix (S × 1), Wi and Wo are the input weight matrix (S × 3) and output weight matrix (2 × S), respectively, and bi and bo are the input bias matrix (S × 1) and output bias matrix (2 × 1), respectively. Here, S is the number of neurons in the hidden layer; i.e., S = 9 for the MEG and MeOH ANNs, while S = 5 for the Luvicap EG ANN. 2.3. Methodological Chart. Figure 1 shows a methodological chart of the conductivity-velocity-based method for determining
Figure 1. Methodological chart. concentrations of the hydrate inhibitor and salts in aqueous solutions. Both the acoustic velocity V and electrical conductivity σ are functions of the salt concentration Csalt, the inhibitor concentration CHI, and temperature T. The measured velocity, conductivity, and temperature of the aqueous sample are fed to a pretrained ANN correlation. The ANN correlation determines the salt and inhibitor concentrations in the sample under test.
3. RESULTS AND DISCUSSION 3.1. Measurements of Conductivity and Velocity. The electrical conductivity was measured using a CDM230 conductivity meter (Meterlab Radiometer Analytical, accuracy of 0.2%). The conductivity meter was equipped with a 4-pole conductivity probe. Use of the 4-pole probe was to minimize the effect of polarization on electrical conductivity measurements, which was particularly useful for the solutions with high salinity. The temperature was measured by a built-in temperature sensor (resolution of 0.1 °C) in the conductivity probe. The acoustic velocity was measured using two Panametrics ultrasonic transducers (1 MHz) and a Panametrics pulser/ receiver (model 0577PR). The errors of the measured velocity were assessed less than 1 m/s by comparison to the literature data.23 Electrical conductivity and acoustic velocity were measured for MEG−NaCl solutions, MeOH−NaCl solutions, and Luvicap EG-NaCl solutions. The measurements were conducted at 273.15, 277.15, 288.15, and 298.15 K. In this work, MEG (>99%), MeOH (>99.5%), and NaCl (>99.5%) were supplied by Fisher Scientific, while Luvicap EG was supplied by BASF and composed of 40 mass % of the active polymer polyN-vinylcaprolactam (PVCap) and 60 mass % MEG. In hydrate flow assurance practice, MEG and MeOH are commonly used as THIs and Luvicap EG is a typical PVCap-based KHI, which 737
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Figure 2. Dependence of the measured acoustic velocity upon salts (NaCl) and MEG concentrations.
Figure 3. Dependence of the measured electrical conductivity upon salt (NaCl) and MEG concentrations.
Figure 4. MEG and salt concentrations determined by the MEG ANN versus the experimental concentrations.
is widely used by the oil and gas industry as a base polymer in commercial formulations. Figure 2 shows typical dependence of the measured acoustic velocity (at 277.15 K) upon the salt (NaCl) and MEG concentrations in aqueous MEG−salt solutions. In this work, the salt concentration was defined in mass percent of the salt in the aqueous phase, which was similar to the salinity of seawater or formation water. The inhibitor concentration was defined as a mass percentage of the inhibitor in the total solution. As shown in Figure 2a, the velocity linearly increases as the salt
concentration increases from 0 to 10 mass %. Figure 2b shows that velocity increases monotonically as the MEG concentration increases from 0 to 50 mass %. This increase in the velocity infers that the addition of MEG and salt enhances the bulk modulus of the water.32 In Figure 3, monotonic trends of the measured conductivity versus the MEG concentration can be seen. The conductivity increases with the salt (NaCl) concentration (i.e., ion concentration) and decreases with the MEG concentration as the addition of MEG reduces the mobility of the salt ions. It was also found that both the 738
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Figure 5. MeOH and salt concentrations determined by the MeOH ANN versus the experimental concentrations.
multiple salt components on this method, produced water from a hydrocarbon transport pipeline was also used to test the ANN. It contained an overall salt concentration of 4.97 mass %, including NaCl (3.11 mass %), CaCl2 (0.16 mass %), KCl (0.34 mass %), MgCl2 (0.33 mass %), and others (1.03 mass %). A total of 16 sample−temperature points were generated using the produced water with 0 and 25 mass % MEG. In Figure 4, DW denotes the solutions that were made of deionized water, while PW denotes the solutions that were made of produced water. It can be seen that, for both salt and MEG concentrations, the DW testing data and the PW testing data are in good agreement with the experimental data. 3.2.2. MeOH−Salt Solutions. It was found that the addition of MeOH results in an increase in the acoustic velocity for aqueous solutions with less than 20 mass % MeOH but a decrease in the acoustic velocity for solutions with obviously more than 20 mass % MeOH. This non-monotonic relationship between the acoustic velocity and MeOH concentration sets a MeOH concentration limit of 20 mass % for the MeOH ANN development. As a result, for ANN training and validation, the conductivity and velocity were measured for aqueous solutions with 0, 1, 3, 5, 7, and 10 mass % NaCl and 0, 10, and 20 mass % MeOH. After temperature interpolation, there were 301 sample−temperature points generated for training and 135 sample−temperature points generated for validation. As shown in Figure 5, the salt (NaCl) and MeOH concentrations determined by the MeOH ANN are in good agreement with the experimental data; therefore, most of the points are overlapped together and distribute with small deviations from the 0-error line. Testing data were generated for synthetic solutions containing 0, 1, 2.5, 3, 5, 7, 8, and 10 mass % NaCl and 0, 5, 15, and 18 mass % MeOH, i.e., 26 sample− temperature data in total. Figure 5 also illustrates that all of the testing points are in good agreement with the experimental data. 3.2.3. Luvicap EG−Salt Solutions. Luvicap EG was used as a typical KHI for developing the conductivity−velocity-based technique. The measured aqueous solutions were made with deionized water, 0, 1, 3, 5, and 7 mass % NaCl, and 0, 0.2, 0.5, 1.0, and 2.0 mass % PVCap. A 0−2 mass % PVCap (i.e., 0−5.0 mass % Luvicap EG) covers a typical concentration range of Luvicap EG commercially deployed, and the salt concentration was limited up to 7 mass % because it was observed that the PVCap polymer started agglomerating out of the aqueous solutions in the presence of 7 mass % NaCl and 2 mass %
conductivity and velocity monotonically change with the solution temperature, which is not illustrated. Similar monotonic relations between the measured conductivity and velocity versus the salt concentration, inhibitor concentration, and temperature were also found for MeOH− salt solutions and Luvicap EG−salt solutions. These monotonic relations are the foundation of the development of the conductivity−velocity-based method for determining hydrate inhibitor and salt concentrations in aqueous solutions. 3.2. Determination of Hydrate Inhibitor and Salt Concentrations. 3.2.1. MEG−Salt Solutions. An ANN with nine neurons in its hidden layer was developed for determining MEG and salt concentrations in aqueous solutions. The conductivity and velocity were measured at four different temperatures for synthetic solutions that contained 0, 1, 3, 5, 7, and 10 mass % NaCl and 0, 10, 20, 30, 40, and 50 mass % MEG in deionized water. A 10 mass % NaCl covers typical concentrations of salts in produced water. The maximum MEG concentration was set to 50 mass % because of the fact that, as shown in Figure 2b, the relationship between the acoustic velocity and the MEG concentration gradually became non-monotonic for aqueous solutions with more than 50 mass %, which could be interpreted in terms of the packing propensity of alcohol in aqueous solutions.33 It is worth mentioning that a dilution option can be introduced in the system to overcome this problem in any practical application of the techniques for systems with alcohol or glycol concentrations higher than 50 mass %. The measured conductivity and velocity data were then interpolated at a temperature interval of 1 K. There were 936 sample−temperature points in total. About 2/3 of the generated sample−temperature points were used for training the ANN, and the rest were used for validating the ANN. Figure 4 shows the results of the ANN training and validation. It can be seen that the ANN-determined salt and MEG concentrations evenly distribute around the 0-error line (diagonal line). The ANN-determined salt and MEG concentrations are so close to the 0-error line that the 936 points are heavily overlapped together at each experimental concentration. The developed MEG ANN was then tested using independent data to prove its ability to generate for new data. A total of 46 sample−temperature data were generated by measuring the conductivity and velocity in synthetic aqueous solutions with 0, 3, 3.5, and 6 mass % NaCl and 0, 25, and 35 mass % MEG in deionized water. To investigate the effect of 739
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Figure 6. PVCap and salt concentrations determined by the Luvicap EG ANN versus the experimental concentrations.
PVCap at ambient temperature. An ANN with five neurons in its hidden layer was trained using 296 sample−temperature data and validated using 225 data. As illustrated in Figure 6, the PVCap and salt concentrations determined by the developed ANN are in good agreement with the experimental concentrations. The developed Luvicap EG ANN was tested using synthetic aqueous solutions and produced water solutions. The synthetic solutions were made of deionized water with 0, 4, and 4.5 mass % NaCl and 0, 0.8, and 1.5 mass % PVCap. The produced water solutions were made of the produced water with 1.2 mass % PVCap (i.e., 3.0 mass % Luvicap EG) and the produced water with 1.1 mass % PVCap (i.e., 2.75 mass % Luvicap EG) and 500 ppm of a corrosion inhibitor (CI) and 550 ppm of a scale inhibitor (SI). Testing the Luvicap EG ANN using the produced water solutions was to demonstrate the reliability of this conductivity−velocity-based method for aqueous samples from real pipelines where multiple components of salts and other chemical additives, such as corrosion inhibitors and scale inhibitors, were present. This was particularly important for KHI−salt solutions because the determination of a KHI concentration will be vulnerable to the presence of other chemicals and multiple components of salts because of its low dose used in pipelines. Figure 6 shows good agreement between the ANN determined and the experimental concentrations for both salts and PVCap. In particular, for the produced water solutions in the presence and absence of the corrosion inhibitor and scale inhibitor, the ANN deviations were less than 0.25 and 0.04 mass % in the salt and PVCap concentrations, respectively. 3.3. Reliability Assessment. Table 1 outlines the overall results of training, validation, and testing of the developed MEG ANN, MeOH ANN, and Luvicap EG ANN. In general, the ANNs are capable of determining MEG, MeOH, Luvicap EG, and salt concentrations with good accuracy in terms of flow assurance. The errors of the testing results are comparable to those of training and validation, which indicates that the developed ANNs are able to determine the concentration of salts and hydrate inhibitors in independent solutions. In Table 1, it can also be seen that the maximum errors are several times larger than the averages. However, statistical analysis of the ANN errors shows that these large errors appeared at very low probability. In panels a and b of Figure 7, given an appearance probability of 0.99, the maximum errors are 0.3 mass % in salts
Table 1. Results of Training, Validation, and Testing, in Mass % deviation in training
deviation in validation
deviation in testing
hydrate inhibitors
average
maximum
average
maximum
average
maximum
MEG NaCl MeOH NaCl PVCap NaCl
0.08 0.08 0.06 0.04 0.04 0.04
0.42 0.45 0.32 0.23 0.18 0.21
0.09 0.10 0.06 0.04 0.03 0.04
0.39 0.48 0.42 0.17 0.20 0.24
0.18 0.12 0.10 0.15 0.03 0.08
0.61 0.27 0.66 0.24 0.17 0.27
and 0.35 mass % in MEG for MEG−salt solutions, 0.15 mass % in salts and 0.25 mass % in MeOH for MeOH−salt solutions, 0.18 mass % in salts and 0.14 mass % in PVCap for Luvicap EG−salt solutions. It should be noted that chromatography cannot be used to analyze MEG and MeOH in the samples containing salts or other solid particles, although it can have a measurement accuracy of parts per million (ppm) levels. To analyze KHIs, Lavallie et al.17 reported that the average measurement errors of the colorimetric method were similar to this conductivity− velocity method but the colorimetric method required skilled people and hours of time for the analysis.
4. CONCLUSION A novel method has been developed for determining the hydrate inhibitor and salt concentrations in aqueous solutions. It is based on the measurement of the electrical conductivity and acoustic velocity of the aqueous solutions. Three ANN correlations were trained, validated, and tested using the measured conductivity and velocity data for MEG−salt solutions, MeOH−salt solutions, and Luvicap EG−salt solutions. The results show that, in comparison to the conventional chromatography and colorimetric methods, the developed ANNs provide a simple, quick, and reliable means for determining hydrate inhibitor and salt concentrations for the aqueous solutions containing THIs (MEG and MeOH) and salts or KHIs (Luvicap EG) and salts. 740
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Figure 7. Statistical distributions of the ANN errors for MEG−salt solutions, MeOH−salt solutions, and Luvicap EG−salt solutions.
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AUTHOR INFORMATION
Corresponding Author
*Telephone: +44-(0)131-451-3564. Fax: +44-(0)131-4513127. E-mail:
[email protected]. Notes
The authors declare no competing financial interest.
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ACKNOWLEDGMENTS This work was conducted through a joint industry project that was financially supported by the sponsors, including BP, Chevron, NIGC, Petronas, Statoil, and TOTAL, which is gratefully acknowledged.
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