Campbell Tip of the Month

Category: Refining

What is Mentoring?
What is Mentoring?

In this Tip of the Month, we explore how process safety competency can be enhanced through mentoring programs.

This TOTM is the paper that was developed by JMC Instructor/Consultants Clyde Young and Keith Hodges presentation at the Center for Chemical Process Safety (CCPS) 8^th Global Conference on Process Safety in April, 2012. The paper will also be published in the AIChE (American Institute of Chemical Engineering) publication, “Process Safety Progress.”

Commit to Process Safety is the first pillar mentioned in “Guidelines for Risk Based Process Safety Management”, published by CCPS. This pillar is supported by five elements. One of the elements is Process Safety Competency, which is associated with efforts to maintain, improve and broaden knowledge and expertise.

In Greek mythology, Odysseus, King of Ithaca went to fight in the Trojan Wars. Before he left, he entrusted his son Telemachus to the care of his old and trusted friend MENTOR. It was some ten years before father and son were reunited and during this time the development and care of his son was with Mentor.

What is often missing from historical accounts is that it is Athene, the Goddess of Wisdom, who appears to Telemachus in the likeness of Mentor and gives advice, encouragement and spiritual insight.

Since then, the word Mentor has become synonymous with trusted advisor, friend and teacher, a wise person.

Demographic studies of the oil and gas processing industry indicate that a large number of people are retiring and being replaced by younger, less experienced personnel. This presents a challenge to the industry. A wise mountaineer once stated, “Good judgment comes from bad experiences.” With the influx of less experienced personnel, it would be shameful to have their good judgment developed from their bad experiences. Especially since these bad experiences can be catastrophic.

Organizations in the industry have spent considerable resources recruiting the best talent available and most have a competency development program that these new workers enter. The program will generally include a step to have a more experienced person provide feedback on the worker to assess competency in the job. Well-developed and resourced competency development programs will have a Mentor assigned to the worker.

What does this really mean and how can an organization insure that process safety competency is developed in all personnel, even if process safety activities are not the primary role?

This TOTM will provide some guidance and best practices for establishing Mentoring programs with an emphasis on developing process safety competency in the younger, less experienced workforce.

The role of Mentor involves teaching, helping, protecting, challenging, motivating, guiding, coaching, listening, and providing career guidance; it falls short of counseling. Counseling is the provision of professional psychological help and advice and chosen Mentors would be foolhardy to attempt such a role without extensive training.

Mentoring is usually a formal or informal relationship between two people, a Mentor (usually and preferably outside the Mentee’s area of supervision) and a Mentee. The Mentor can also be provided from an external organization. This can be preferable especially if there is any hint of competition between the Mentors and Mentees (e.g. working in the same department as peers). There are different rules of engagement if the external option is taken and this is outside the scope of this paper. Peer Mentoring can be a useful option, especially if a peer Mentor has specific skills and qualifications.

Using a Mentee’s supervisor within a discipline should be avoided as there could be a conflict of interest. The Mentor may be Mentoring one day and disciplining the next, This is not conducive to building trust, which is an important ingredient in the Mentoring process.

Mentoring should not be substituted for conventional classroom or computer-moderated training. It enhances traditional training by allowing the Mentee to learn from experienced colleagues within the working environment.

Choosing a Mentor

The choice of Mentors is an important aspect of a program and managers should first be satisfied that a Mentor not only has the required technical skills, but also has the ability to convey those to others in an efficient and effective way. Competency associated with Mentoring skills does not necessarily come naturally to everyone with highly competent technical skills. A key skill to insure effective process safety is communication with all disciplines that could have an impact on the process.

Mentor Program

It is foolhardy to think that just putting together a pool of people as Mentors and pairing them with Mentees is going to be an effective way to put a Mentor program together. It takes planning and needs structure. There has to be an organizational aim for the program with measurable objectives. The Mentor should be provided with these and a list of roles and responsibilities, which they should fully comprehend.

There should be a selection process for Mentors and organizations must recognize that a training program may have to be created for selected Mentors.

Ideally the Mentee should be able to select the Mentor from a pool of people in the organization; management, the training department or HR should not pair them. Mentors should have the option to refuse the role should they feel that it would not be appropriate.

Mentoring and Process Safety

A Mentoring program is not to be approached in a haphazard fashion if the goal is to develop competent personnel. A Mentoring program is much like a process safety management system. The Center for Chemical Process Safety (CCPS) guidelines for Risk Based Process Safety Management (RBPSM) defines a management system as, “A formally established and documented set of activities designed to produce specific results in a consistent manner on a sustainable basis.” The Mentoring program should be formalized, documented and designed to produce specific results. The specific results are competent personnel associated with process safety.

Mentees within a program may have been chosen because they are targeted to fill a key role within the organization. This role could be a technical position that requires narrow skills in a field or a supervisory position of either engineering personnel or operations personnel. The competency levels associated with process safety that are required will be highly dependent on the role in the organization. The Mentor/Mentee relationship should keep this in mind as the process progresses.

An effective Mentoring program that includes process safety as a key component will yield numerous benefits to the organization. A Mentor with wide professional and technical expertise should have considerable experience in areas that involve process safety. A Mentor that truly understands the concepts of risk based process safety will be invaluable to a Mentee with less experience. Consider the pillars of RBPSM and some of the elements within each pillar.

Commit to Process Safety

Elements of this pillar include:
- Process safety culture
- Compliance with standards
- Process safety competency
- Workforce involvement
- Stakeholder outreach
A simple definition of culture is, “How we do things around here.” Organizations strive to develop a learning culture that seeks hazards and solutions on a continuous basis. It is imperative that Mentees are provided awareness level training on the organization’s culture and the Mentor will be given training on how to act as the example. Two significant benefits will come from this. The Mentors will examine their own actions within the culture and insure that they are setting a good example. The Mentee will question why and how activities are accomplished and learn his/her role within the organization’s culture, which should accelerate the Mentees contribution through self-awareness.

It will be difficult for a less experienced worker to learn the things required to insure compliance with all applicable standards. An effective Mentor should always guide the Mentee toward the correct answer associated with compliance but not necessarily answer the question of compliance. The guidance and allowing the Mentee to find the answer will insure that the learning associated with compliance will be retained long after the answer is discovered.

Process safety competency of the Mentee will be enhanced significantly, but only if the Mentor insures that the Mentee is directed to the appropriate resources for this. The Mentor does not necessarily have to be considered a process safety expert. The Mentor does have to be aware that some process safety issues require a level of expertise that will be found elsewhere. And sometimes those resources may be outside the organization.

For a process safety management system to thrive, staff members at all levels of the organization must take an active role. The role taken needs to be identified and metrics established to show participation in the role. A Mentor can provide guidance and suggestions so that the Mentee is consistently working toward the goals of the process safety management system. Appropriately timed reviews of progress associated with established process safety metrics should be scheduled and conducted.

Stakeholders include outside contractors, shareholders, community members and partners in joint ventures. A Mentee may be involved with negotiations and planning activities associated with all kinds of stakeholders. A Mentor’s experience in the industry and the organization can be very useful to insure that all stakeholder interests are addressed.

Understand Hazards and Risks

Elements of this pillar include:
- Process knowledge management
- Hazard identification and risk analysis
Development of a Mentee’s competency in this pillar of RBPSM could be the Mentor’s most important role. Insuring that the correct process knowledge is developed and managed appropriately is a critical activity that the Mentee strives for. There is no need for a Mentee to learn from mistakes if a Mentor can provide clear guidance on this pillar.

It is within these two elements that mistakes can lead to catastrophic events. Having an incorrectly sized relief valve installed in a process or not anticipating the consequences of failure of controls is not acceptable. The Mentor and Mentee should routinely conduct discussions about these elements.

Contract services are utilized a great deal for design of new and modified facilities. A Mentor who has significant experience in this area can provide the Mentee advice and guidance for overseeing these projects. Oversight by a qualified company representative will insure that all issues associated with a project have been addressed.

Providing resources during the conduct of Process Hazard Analysis (PHA) studies is a challenge for many organizations. This is especially true considering the demographics of the industry at this time. More experienced personnel have moved on. PHA team members with significant experience are critical to the success of a PHA. A Mentee who is assigned to a PHA team may or may not work side by side with their Mentor. If the assigned Mentor is also a member of the PHA team, this may prove advantageous. As the role of Mentor is to provide guidance and direction to new and developing staff, the PHA is an excellent environment to do just that. The structure of the PHA provides an opportunity to guide the Mentee in the proper way to identify hazards, develop measures to mitigate those hazards and work as a team member in a formalized setting.

Manage Risk

Within this pillar, a Mentee will benefit from the guidance of an experienced Mentor to become proficient at what might be considered the day-to-day activities associated with their job. Elements are:
- Procedures
- Safe work practices
- Asset integrity
- Contractors
- Training and performance
- Management of change
- Operational readiness
- Conduct of operations
- Emergency management
Sometimes organizations will assign a younger, less experienced person to a supervisory position in operations to “season” them. Studies have shown that a great number of incidents occur during normal operations. Having a Mentor with significant operations experience will accelerate the “seasoning” process and insure that the problems associated with day-to-day activities do not lead to a catastrophic incident.

Working in operations supervision will certainly expose the Mentee to many issues associated with personal interaction. Dealing with people may be one of the most difficult tasks undertaken. Having the ear of a Mentor can be helpful as the Mentee develops his/her skills in this area.

Learn From Experience

There is no reason that a young professional cannot learn from the experience of others. To pass along the experience and knowledge that has been gained over the years is the focus of a Mentoring program. Hopefully, the Mentee will not have to experience a catastrophic incident to learn from experience.

Elements within this pillar are:
- Incident investigation
- Measurement
- Audits
- Management review and continuous improvement
Having a Mentor available to help review near miss reports, incident investigations, audit findings and metrics associated with process safety can provide the Mentee with a “cold eye” review of issues that are the Mentee’s responsibility to address. Often a wiser, more experienced Mentor will have experienced some of the same things that are being discovered under the Mentee’s watch. In this case, issues should be able to be addressed quickly and more efficiently.

Troubleshooting

All processes within the industries we work have been designed to operate in a specified manner. This manner includes specific temperatures, pressures, flow rates and levels. Defining these specific parameters establishes “normal” for these processes. Operating processes in a “normal” manner reduces the likelihood of a catastrophic incident. Deviation from “normal” is not acceptable and identifying this deviation and taking the steps required to return to normal requires experience and knowledge. This is known as troubleshooting. Process safety management is a system that establishes “normal” and provides directions on maintaining “normal”. Personnel with effective troubleshooting skills will also work efficiently within an organization’s process safety management system.

A formalized, well established Mentoring program for younger, less experienced personnel entering the business enhances everyone’s troubleshooting skills. The Mentee has someone (the Mentor) available to query about issues seen and the Mentor is challenged to insure the advice and guidance provided is correct and useful.

Attaining high-level competency in a job requires training and then performing the job for a period of time. Accelerating the path to high-level competency is a significant goal of a formalized Mentoring program.

Conclusion

At the beginning of this TOTM, it was stated that the word Mentor has become synonymous with trusted advisor, friend and teacher, a wise person. Process safety management has become synonymous for reducing the risk associated with the activities performed in our industries.

Risk is often viewed differently from individual to individual. A person’s perception of risk may change with familiarity. Having a trusted advisor for younger, less experienced personnel, to help identify and provide suggestions for mitigation of hazards, in all their forms, is a strong competency development tool for any organization. Personnel will be developed quicker and more efficiently. Experienced personnel are one of a company’s most valuable resources. Acting as a Mentor can be the best use of this resource and will provide a challenge that some people thrive on.

Any organization that truly strives for a generative safety culture should do whatever it takes to implement a process safety-Mentoring program. The benefits will be seen and reaped for years to come.

To learn more about managing process safety systems, we suggest attending our PetroSkills HSE course, HS 45- Risk Based Process Safety Management.

To enhance process safety engineering skills we suggest any of the JMC foundation courses or our, PS 4 – Process Safety Engineering course.

John M. Campbell Consulting (JMCC) offers consulting expertise on this subject and many others. For more information about the services JMCC provides, visit our website at www.jmcampbellconsulting.com, or email us at consulting@jmcampbell.com.

By: Clyde Young and Keith Hodges
February 1, 2013
How good are the detailed methods for sour gas density calculations?
Gas density estimates are of fundamental importance for process simulation, equipment design, and process safety engineering. In the previous Tip of the Month (TOTM), two shortcut methods for predicting sour and acid gas density were evaluated. We showed that Katz correlation gives accurate results for lean sweet gases and it is the most accurate in comparison to Wichert-Aziz method or the SRK EOS. For binary mixtures of CH₄ and CO₂, Wichert-Aziz method gives the most accurate result for CO₂ content of between 10 and 90 mole percent. As H₂S and CO₂ content increased, the accuracy of the Katz correlation decreased, but its accuracy increased as the mixture approached a single component. The percentage difference between the Katz and Wichert-Aziz [1] methods for gas mixtures containing acid gases was greater for H₂S than CO₂.

Process simulation software often use the Benedict-Webb-Rubin-Starling (BWRS), Soave-Redlich-Kwong (SRK) and/or Peng-Robinson (PR) equations of state for gas density calculations. Other sources of gas density calculation are NIST REFPROP (Reference Fluid Thermodynamic and Transport Properties) program and GERG-2004 [2, 3], a reference equation of state for natural gases.

Due to the importance of CO₂ injection for enhanced oil recovery and the increasing interest in CO₂ capture and sequestration, this study was undertaken to evaluate the accuracy of density calculations for gases containing nil to 100% CO₂. An experimental data base was used for the basis of comparison. The study reviews all of the above mentioned methods and will report their accuracies. Table 1 presents the summary of the temperature, pressure, and CO₂ mole percent ranges for the data used in this study. The sources of experimental data were reference [4, 5]. This table also presents the average absolute average percent error and the overall average percent error.

Table1 – Summary of error analysis and comparison of accuracy of sour gas and acid gas density prediction by several methods:

Table 1 provides the overall accuracy of the various methods. It should be noted that the relative accuracy of each method varies depending on the CO₂-CH₄ proportion, the temperature and pressure. The AGA 8 did not return values for many of the low temperature cases where two phases were present. These points were ignored in the analysis.

Next, we plotted the experimental data reported in the GPA RR-138 [3] and GPA RR 68 [4] to evaluate the accuracy of Katz, Wichert-Aziz and the best four of the detailed methods. The results of this evaluation for the T=350°K and 320°K cases are shown in Figures 1 through 5, for CO2 content of 9.83 to 100 mole percent. In Figure 1, Katz method is the most accurate and the accuracy of the other methods are almost the same.

In Figure 2, Katz method has the least accuracy and even though the accuracy of the other methods look the same, GERG 2004 is slightly better than the others.

In Figure 3, Katz method again has the least accuracy and even though the accuracy of the other methods look the same, AGA8 provided slightly better estimates than the others.

In Figure 4, Wichert-Aziz method has the least accuracy and even though the accuracies of the other methods look the same, AGA8, GERG-2004 and REFPROP are slightly more accurate than the PR EOS.

In Figure 5, Wichert-Aziz method has the least accuracy and REFPROP, GERG 2004 and AGA 8 equally have the best accuracy.

Based on the work done in this study and in the previous TOTM, the following can be concluded:
1. Katz correlation gives accurate results for pipeline quality gases (lean sweet gases)
2. For pure CO₂, AGA 8, REFPROP, and GERG 2004 methods equally are the most accurate method
3. For binary mixtures of CH₄ and CO₂, REFPROP and GERG 2004 methods equally give the most accurate result for CO₂ content of between 10 and 90 mole percent.
4. As CO₂ content increases, the accuracy of the Katz correlation decreases, but its accuracy increases as the mixture approaches a single (pure) component.
5. The Peng-Robinson EOS provides a better density estimate than the SRK EOS.
6. Results from either the PR or the SRK EOS in ProMax are slightly more accurate than the comparable results from HYSYS.
7. Binary interaction parameters which have been optimized to predict VLE behavior may not provide the best density prediction.
8. At several low temperatures, AGA8 did not provide density estimates. The average errors reported here ignored these missing data. Note that AGA8 is not valid for liquid nor for the extended region near the critical point.
9. Table 1 indicates that REFPROP and GERG 2004 give equally the best results.
To learn more about similar cases and how to minimize operational problems, we suggest attending our G4 (Gas Conditioning and Processing), G5 (Gas Conditioning and Processing – Special), G6 (Gas Treating and Sulfur Recovery), RF61 (RefineryGas Treating, Sour Water, Sulfur and Tail Gas), PF-81 (CO2 Surface Facilities), and G40 (Process/Facility Fundamentals) courses.

By: Wes Wright and Dr. Mahmood Moshfeghian

References:
1. Wichert, E. and Aziz, K., Hydr. Proc., p. 119 (May 1972).
2. Lemmon, E.W., Huber, M.L., McLinden, M.O. NIST Standard Reference Database 23: Reference Fluid Thermodynamic and Transport Properties-REFPROP, Version 8.0, National Institute of Standards and Technology, Standard Reference Data Program, Gaithersburg, 2007.
3. Kunz, O., Klimeck, R., Wagner, W., and Jaeschke, M. “The GERG-2004 Wide-Range Equation of State for Natural Gases and Other Mixtures,” GERG Technical Monograph 15 (2007)
4. Hwang, C-A., Duarte-Garza, H., Eubank, P. T., Holste, J. C. Hall, K. R., Gammon, B. E., March, K. N., “Thermodynamic Properties of CO₂ + CH₄ Mixtures,” GPA RR-138, Gas Processors Association, Tulsa, OK, June 1995
5. Hall, K. R., Eubank, P. T., Holste, J., Marsh, K.N., “Properties of C02-Rich Mixtures Literature Search and Pure C02 Data, Phase I,” GPA RR-68, A Joint Research Report by Gas Processor Association and the Gas Research Institute, Gas Processors Association, Tulsa, OK, June 1985
October 1, 2008
How good are the shortcut methods for sour gas density calculations?
Gas density is needed for process simulation and equipment design. For example, accurate predictions of gas density are needed for calculation of pressure drop in piping/pipeline and for vessel sizing. Accurate gas density is also essential for custody transfer metering. Gas density, , is calculated by:

(1)

Where:

&Gas density, kg/m³ (lb_m/ft³)
Absolute temperature, K (ºR)
Pressure, kPa (psia)
MW Molecular weight kg/kmole (lb_m/lbmole)
Gas compressibility factor
Universal gas constant, 8.314 (kPa)(m³)/(kmole)(K) or 10.73 (psia)(ft³)/(lbmole)(ºR)

In equation 1, “z” represents gas compressibility factor. For ideal gases, “z” is equal to 1. Gas densities are sometime expressed in terms of relative density (specific gravity), , and is defined as:

(2)

Substituting Equation 1 for gas and air into Equation 2 and assuming ideal gas behavior at standard conditions, Equation 2 will be transformed to:

At the standard condition and for simplicity, Equation 3 can be written as

In Equation 1, the key parameter is the compressibility factor “z”, which is a function of pressure, temperature and gas composition. Compressibility factor is a dimensionless surrogate of non-ideal gas density. In general, equations of state are probably the most widely used for calculation of z. They are not necessarily the most accurate. Empirical correlations developed for a specific mixture or a narrow range of mixtures provide better accuracy, but may be less general. An example would be the Katz chart which is quite good when applied to “sweet” pipeline quality gases, but less reliable for gases containing H₂S, CO₂ and/or N₂. Figure 3.2 in Chapter 3 of Gas Conditioning and Processing [1] shows the Katz chart for sweet natural gases as prepared by Standing and Katz [2]. The chart was developed by using experimental data on methane binary mixtures with ethane, propane, butane and other natural gases over a wide range of composition with a maximum molecular weight of 40.

For fiscal metering of natural gas, an accurate experimental database has been developed and compressibility factor correlations, with uncertainties generally within ±0.2%, have been published in the industry standards, AGA Report No. 8 and ISO 12213. A summary of some common “z” correlations and their effect on gas measurement accuracy can be found in reference [3]. Since many people use the Katz compressibility factor chart, the question is often asked how it may be extended to gases containing H₂S and CO₂. There are two methods available for this application.
1. The approach proposed by Robinson et al. [4]
2. The approach proposed by Wichert and Aziz [5]
In this Tip of the Month (TOTM) we will demonstrate the accuracy of the second approach. The details of this method are presented in Chapter 3 of Gas Conditioning and Processing [1].
Let’s consider the gas mixture shown in Table 1 with total acid gas (H₂S and CO₂) of 14.68 mole percent. At 13.94 MPa (2021 psia) and 58 ºC (136 ºF), the compressibility factors are 0.797 (120.1 kg/m3) and 0.832 (114.8 kg/m³), using Katz chart and Wichert-Aziz method respectively. The percent deviation between two answers from each other is 4.4%.

In order to show the effect of acid gas on compressibility factor determined from Katz chart and Wichert-Aziz methods, we varied the acid gas content of the gas in Table 1 from 0 to 37 mole percent. This was accomplished by diluting the non-acid gas components with a 50:50 mixture of CO₂ and H₂S. Figure 1 presents the percentage difference between the two methods as a function of acid gas content. The graph shows that as the H₂S and CO₂ content increases, the deviation of Katz chart from Wichert-Aziz method increases almost linearly. This graph also indicates that the percentage difference between the two methods is greater for the case of diluting gas with only H₂S than only CO₂.

Next, we used the experimental data reported in the GPA RR-138 [6] and GPA RR 68 [7] to evaluate the accuracy of Katz, Wichert-Aziz and SRK equation of state (EOS) for binary mixtures of CO₂ and CH₄. The results of this evaluation are shown in Figures 2 through 6, for CO₂ content of 9.83 to 100 mole percent. The figures indicate that the Katz correlation accuracy decreases as the mole percent of CO₂ increases. However; Figure 5 indicates that as the gas becomes very rich in CO₂, the accuracy of the Katz correlation and the Wichert-Aziz method are practically identical. Figure 6 shows that the Katz correlation best predicts the density of pure CO₂, and also when the gas approaches pure CH₄. The experimental data for pure CO₂ in Figure 6 is from GPA RR 68 [7]. Figure 2 through 6 also indicate that the SRK EOS has low accuracy. In this study, a binary interaction parameter of 0.12 between CH₄ and CO₂ which had been determined from experimental vapor-liquid-equilibrium (VLE) data was used.

Based on the work done in this study, the following can be concluded:
1. Katz correlation gives accurate results for pipeline quality gases (lean sweet gases)
2. For pure CO₂, Katz correlation is the most accurate in comparison to Wichert-Aziz method or the SRK EOS.
3. For binary mixture of CH₄ and CO₂, Wichert-Aziz method gives the most accurate result for CO2 content of between 10 and 90 mole percent.
4. As H₂S and CO₂ content increases, the accuracy of the Katz correlation decreases, but its accuracy increases as the mixture approaches a single (pure) component.
5. The percentage difference between the Katz and Wichert-Aziz methods for gas mixtures containing acid gases is greater for H₂S than CO₂.
6. Binary interaction parameters which have been optimized to predict VLE behavior, may not provide the best density prediction.
To learn more about similar cases and how to minimize operational problems, we suggest attending our G4 (Gas Conditioning and Processing), G5 (Gas Conditioning and Processing – Special), G6 (Gas Treating and Sulfur Recovery), RF61 (RefineryGas Treating, Sour Water, Sulfur and Tail Gas), PF-81 (CO₂ Surface Facilities), and G40 (Process/Facility Fundamentals) courses.

By: Dr. Mahmood Moshfeghian

References:
1. Campbell, J. M., and Hubbard, R. A., Gas Conditioning and Processing, Vol. 1 (8^th Edition, 2^nd Printing), Campbell Petroleum Series, Norman, Oklahoma, (2001).
2. Standing, M.B. and Katz, D.L.; “Density of Natural gas gases,” AIME Trans., 146, 140-49 (1942)
3. Hannisdal, N.E., “Gas Compression Equations Evaluated,” Oil and Gas J., p. 38-41 (May 4, 1987)
4. Robinson, D. F. et al. Trans. AIME, Vol 219, P. 54, (1960).
5. Wichert, E. and Aziz, K., Hydr. Proc., p. 119 (May 1972).
6. Hwang, C-A., Duarte-Garza, H., Eubank, P. T., Holste, J. C. Hall, K. R., Gammon, B. E., March, K. N., “Thermodynamic Properties of CO₂ + CH₄ Mixtures,” GPA RR-138, Gas Processors Association, Tulsa, OK, June 1995
7. Hall, K. R., Eubank, P. T., Holste, J., Marsh, K.N., “Properties of C0₂-Rich Mixtures Literature Search and Pure C0₂ Data, Phase I,” GPA RR-68, A Joint Research Report by Gas Processor Association and the Gas Research Institute, Gas Processors Association, Tulsa, OK, June 1985
September 1, 2008
Acid Gas-Water Phase Behavior
In the last Tips of the Month, we discussed the phase behavior of water-sweet natural gas and water-sour natural gas mixtures. In this tip, we will demonstrate the acid gas–water phase behavior.

The water content of a gas depends on the system temperature, pressure and composition of the gas. The phase equilibria in the system H₂S + water and CO₂ + water are key to the discussion of the water content of an acid gas system. Figure 1 presents the water content of hydrogen sulfide predicted by ProMax [1] as a function of pressure and temperature. A limited number of experimental data points at 100°F [37.8°C] by Gillespie and Wilson [2] are also shown on this diagram. The behavior shown on this plot is quite complicated and explained by Carroll [3] thoroughly: “At low pressure the hydrogen sulfide + water mixture is in the gas phase. At low pressure the water content tends to decrease with increasing pressure, which is as expected. Eventually a pressure is reached where the H₂S is liquefied. On this plot this is represented by the discontinuity in the curve and a broken line joins the phase transition. There is a step change in the water content when there is a transition from vapor to liquid. In the case of hydrogen sulfide the water content of the H₂S liquid is greater than the coexisting vapor. This is contrary to the behavior for light hydrocarbons where the water content in the hydrocarbon liquid is less than the coexisting vapor.”

Figure 1. Water content of pure H₂S predicted by ProMax, experimental data [2]

In general the phase behavior of the system CO₂ + water is as complex as that of the H₂S + water system. The CO₂-rich liquid phase only occurs for temperatures less than about 90°F [32.2°C]. As shown in Figure 2 reported by Maddox and Lilly [4], the water content of CO₂ exhibits a minimum.

Figure 2. Predicted saturated water contents at 100°F [38°C] for CO₂, CH₄ and mixture of both [4]

There are several methods available that can be used to predict the water content of acid gases. All of these methods are based on equation of state and rigorous thermodynamic models. As described above, the phase behavior is complicated and extra care should be taken to assure a correct prediction. In the remaining section of this tip, we will demonstrate the capabilities of some of these methods.

Figure 3 compares the water content calculation results for an acid gas stream by several methods in HYSYS [5] and ProMax [1]. The composition of the acid gas stream is shown in the inset of diagram. Even though at low pressures, all methods give close results, as can be seen from this figure, there are large differences at higher pressures.

Figure 3. Comparison of water content prediction by different methods at 59 °F [15°C]

Table 1 gives another comparison of available methods for prediction of acid gas water content.

Table 1. Comparison of ProMax and modified SRK EOS results with the experimental water content [6] of several acid gas mixtures:

The experimental composition and predicted water content by HYSYS, ProMax and the modified SRK for eight acid gas mixtures are presented in Table 2. The upper part of this table reports the measured mole percent of the feed stream and the lower part shows the experimental vapor stream compositions in mole percent. Based on the feed compositions, three-phase flash calculations were performed and the resulting vapor stream water content (mole %) are shown in the last three columns (upper part).

For each vapor stream, the saturated water content was predicted by the above methods and is presented in the lower portion of this table. As can be seen from this table, ProMax predict saturated water content reasonably well. The red figures in Table 2 indicate that the methods predict a non-aqueous liquid phase instead of the vapor phase. Based on a dry basis phase envelope, the conditions for these mixtures were dense phase/compressed liquid.

Table 2. Conditions and compositions of 8 acid gases and their saturated water contents

Figure 4 also compares the accuracy of the above methods graphically. This figure clearly indicates that ProMax gives the most accurate results. The Erbar et al. [7] SRK method also gives reasonable results.

To learn more about similar cases and how to minimize operational problems, we suggest attending

To learn more about similar cases and how to minimize operational problems, we suggest attending our G4 (Gas Conditioning and Processing), G5 (Gas Conditioning and Processing – Special), G-6 Gas Treating and Sulfur Recovery and RF-61 Refinery Gas Treating, Sour Water, Sulfur and Tail Gas courses.

By Wes Wright and M .Moshfeghian

Reference:
1. ProMax 2.0, Bryan Research and Engineering, Inc., Bryan, Texas, U.S.A., 2007
2. Gillespie, P.C. and G.M. Wilson, “Vapor-Liquid Equilibrium Data on Water-Substitute Gas Components: N₂-H₂O, H₂-H₂O, CO-H₂O, H₂-CO-H₂O, and H₂S-H₂O” Research Report RR-41, GPA, Tulsa, OK, 1980.
3. Carroll, J.J., “The water content of acid gas and sour gas from 100 to 220 °F and pressures to 10,000 Psia,” Presented at the 81st Annual GPA Convention, Dallas, Texas, USA, March 11-13, 2002.
4. Maddox, R.N., L.L. Lilly, “Gas conditioning and Processing, Vol 3: Computer Applications and Production/Processing Facilities”, John M. Campbell & Company, Norman, USA, 1982.
5. ASPENone, Engineering Suite, HYSYS Version 2006, Aspen Technology, Inc., Cambridge, Massachusetts U.S.A., 2006.
6. Huang, S.S.-S., A.-D. Leu, H.-J. Ng, and D.B. Robinson, “The Phase Behavior of Two Mixtures of Methane, Carbon Dioxide, Hydrogen Sulfide, and Water” Fluid Phase Equil. 19, 21-32, 1985.
7. Erbar, J.H., A.K. Jagota, S. Muthswamy, and M. Moshfeghian, “Predicting Synthetic Gas and Natural Gas Thermodynamic Properties Using a Modified Soave Redlich-Kwong Equation of State,” Gas Processor Research Report, GPA RR-42, Tulsa, USA, 1980.
8. Ng, H.-J., C.-J. Chen, and H. Schroeder, “Water Content of Natural Gas Systems Containing Acid Gas”,Research Report RR-174, Gas Processors Association, Tulsa, OK, 2001.
December 1, 2007
Water-Sour Natural Gas Phase Behavior
In the last Tip of the Month, we discussed the phase behavior of water-sweet natural gas mixtures. In this tip, we will demonstrate the water-sour natural gas phase behavior. In a future tip, we will address water content of acid gases.

Water is produced with oil and gas. A question that comes to mind is: “Why is water important?” The presence of water may cause corrosion, freezing and hydrate formation. All of these problems are enhanced by the presence of acid gases such as H₂S and CO₂.

A phase envelope with hydrate and water dew point curves is an excellent tool to find out what form/phase water is in at operating conditions, during start-up, during shut-down and during upsets. The water content of a gas depends on the system temperature, pressure and composition of the water containing gas. There are several methods of calculating of water content of sour gases. The details of these methods can be found in Chapter 6 of Volume 1 [1] and Chapter 9 of Volume 3 [2] of “Gas Conditioning and Processing”. In this work we will use Maddox et al. [3] (Figures 6.1, 2, 3 and Equation 6.2 of Volume 1) and the modified Soave-Redlich-Kwong (SRK EoS) reported in GPA RR-42 by Erbar et al. [4]. This version of SRK is tailor-fitted to handle water-hydrocarbon systems containing hydrogen sulfide and carbon dioxide.

The compositions of several sour gases studied in this study along with their measured and predicted water contents are shown in Table 1. The Maddox et al. (referred to as Chart) results were generated using GCAP software and the modified SRK EOS results were generated by performing rigorous three-phase (gas-liquid hydrocarbons-aqueous) flash calculations. A trial version of GCAP can be downloaded here, at the bottom of the page.

Table 1 indicates that as long as the total acid gas concentrations is less than 60 mole percent, Maddox et al. and the modified SRK methods produce results within the accuracy of experimental data. However, for higher concentrations of acid gases, the modified SRK provides a better prediction. The water content of acid gas systems will be discussed further in the next Tip of the Month.

The composition, experimental, and predicted water content by Maddox et al. and the modified SRK for two natural gas mixtures are presented in Table 2. The upper part of columns 1, 2, 4, 5 report the measured mole percent. Based on the feed compositions shown in columns 1 and 4, three-phase flash calculations using the modified SRK were performed and the resulting vapor stream compositions are shown in columns 3 and 6, respectively. Notice that the measured and predicted vapor compositions are not identical. Inaccuracies in predicting the vapor composition can result in errors in predicting the water saturation.

For each vapor stream, the saturated water content was predicted by both methods and is presented in the lower portion of this table. As can be seen from this table, both methods predict saturated water content reasonably well. Not surprisingly, the accuracy of both methods is improved slightly when the experimental vapor composition is used rather than the predicted vapor composition.

Figure 1 represents the phase behavior for mixture A. This figure includes from right to left: the water dew point, hydrate formation, 25 weight percent methanol (MeOH) inhibited hydrate formation, hydrocarbon dew point, retrograde, and the bubble point curves, respectively. The blue-triangular-symbol water dew point curve is predicted by use of Figures 6.1, 6.22 and 6.3 with Equation 6.2 of Volume 1 (Maddox et al. method). The red curve represents the water dew point predicted by rigorous calculations using the modified SRK. It is interesting to see that both methods agree quite well with each other. The region to the right of the water dew point curve is gas phase and to the left, liquid water is present.

Figure 2 presents the phase behavior of sour gas mixture B. With exception of low pressure region, both methods agree quite well.

Figure 3 demonstrates the effect of acid gases on phase behavior of mixture B. As shown in this figure, the presence of acid gases shifts all of the curves to the right. In other words, the presence of acid gases increases the hydrate formation temperature considerably. It also increases the water dew point temperature. It should be noted that the water dew point curves have been generated for a fixed amount of water content predicted at specified separator condition. In this case the separator temperature and pressure were 120 °F [48.9 °C] and 1500 psia [10,342 kPa], respectively.

To learn more about similar cases and how to minimize operational problems, we suggest attending our G4 (Gas Conditioning and Processing), G5 (Gas Conditioning and Processing – Special) and RF-61 Refinery Gas Treating, Sour Water, Sulfur and Tail Gas courses.

Dr. Mahmood Moshfeghian

Reference:
1. Campbell, J.M., “Gas Conditioning and Processing, Vol 1: The Basic Principles”, 8^th Edition, Edited by R.A. Hubbard, John M. Campbell & Company, Norman, USA, 2001.
2. Maddox, R.N., L.L. Lilly, “Gas conditioning and Processing, Vol 3: Computer Applications and Production/Processing Facilities”, John M. Campbell & Company, Norman, USA, 1982.
3. Maddox, R.N., L.L. Lilly, M. Moshfeghian, and E. Elizondo, “Estimating Water Content of Sour Natural Gas Mixtures”, Laurance Reid Gas Conditioning Conference, Norman, OK, Mar., 1988.
4. Erbar, J.H., A.K. Jagota, S. Muthswamy, and M. Moshfeghian, “Predicting Synthetic Gas and Natural Gas Thermodynamic Properties Using a Modified Soave Redlich-Kwong Equation of State,” Gas Processor Research Report, GPA RR-42, Tulsa, USA, 1980.
5. Huang, S.S.-S., A.-D. Leu, H.-J. Ng, and D.B. Robinson, “The Phase Behavior of Two Mixtures of Methane, Carbon Dioxide, Hydrogen Sulfide, and Water” luid Phase Equil. 19, 21-32, 1985.
6. Ng, H.-J., C.-J. Chen, and H. Schroeder, “Water Content of Natural Gas Systems Containing Acid Gas”, Research Report RR-174, Gas Processors Association, Tulsa, OK, 2001.
November 1, 2007
Better Alternative for Natural Gas Sweetening
In this Tip of the Month, we will study two alternatives for removal of H₂S and CO₂ from natural gas. The process objective is to produce a sweet gas with less than 4 PPM H₂S and the near total removal of CO₂ due to the presence of a downstream nitrogen rejection unit (NRU). Each alternative consist of two stages of acid gas removal. The schematic diagrams for both alternatives are shown in Figure 1. The detail of gas sweetening may be found in the Gas Conditioning and Processing, Vol. 4, “Gas and Liquid Sweetening”.

In option A, sour natural gas is treated in the MDEA unit to selectively remove H₂S and part of the CO₂. The partially treated gas is then sent to the DEA unit for removal of the remaining CO₂. The acid gas from the MDEA unit is sent to the sulfur recovery unit (SRU). The acid gas from the DEA unit, mainly CO₂, and tail gas from the SRU are sent to the incinerator.

In the second alternative (Option B), all of the H₂S and CO₂ are removed in the DEA unit and sent to the MDEA unit where H₂S is selectively removed at low pressure. The H₂S enriched acid gas from the MDEA unit is sent to the SRU. The CO₂ from the MDEA contactor is routed directly to the incinerator unit.

We performed rigorous computer simulation for both alternatives with typical process specifications. The simulation results showed that arrangement of the MDEA and DEA units and the order of their utilization make a difference in the concentration of H₂S in the acid gases fed to the sulfur recovery unit. Our study indicated that option B produces richer H₂S in the acid gases stream, producing a more suitable feed for sulfur recovery unit. A drastic reduction of pumping was obtained for option B. Smaller diameter and fewer number of trays for the contactor of the MDEA unit in Option B are needed. An additional benefit of option B is that less pick up of heavy hydrocarbons from the feed gas was obtained. This will benefit both the operation of the amine units and the SRU. The preliminary evaluation indicates that the overall energy consumption is lower for option B which has more favorable performance. However, for decision making process, a detail economic analysis for both options is highly recommended.

Similar cases in gas sweetening are discussed in our GAS TREATING AND SULFUR RECOVERY (G-6) and REFINERY GAS TREATING, SOUR WATER, SULFUR AND TAIL GAS (RF-61) courses.

By: Dr. Mahmood Moshfeghian and Mark E. Bothamley

References:
1. Maddox, R.N., and D.J. Morgan, “Gas Conditioning and Processing, Vol. 4, Gas and Liquid Sweetening,” 4th Ed., John M. Campbell & Company, Norman Oklahoma, (1998)
May 1, 2006