Molecular sieves are used upstream of turboexpander units and LNG facilities to dehydrate natural gas to <0.1 ppmv water content. In the natural gas industry, the molecular sieves employ heat to drive off the adsorbed water. Figure 1 shows a typical flow schematic for a 2 tower system; Figure 2 shows a 3 tower system.
Figure 1. Typical process flow diagram for a 2-tower adsorption dehydration system [1]The cyclical heating/cooling of the adsorbent results in a capacity (kg water/100 kg adsorbent; lbm water/100 lbm adsorbent) decline due to a gradual loss of crystalline structure and/or pore closure. A more troublesome cause of capacity decline is contamination of the molecular sieves due to liquid carryover from the upstream separation equipment.
Figure 3 shows a generic molecular sieve capacity decline curve. A few important observations can be made from Figure 3:
The life of the adsorbent is a function of the number of cycles, not the elapsed calendar time.
The capacity decline is steep at the beginning but gradually flattens out. This assumes no step-change events such as NGL, glycol, and/or liquid amine carryover, bed support failure, etc.
Shown in this figure are “Good”, “Average” and “Poor” curves that are a function of site specific factors.
Locating one data point on Figure 3 from a performance test allows you to extrapolate the decline curve of the unit in question.
If your regeneration circuit has excess capacity over the “normal design conditions”, i.e., a design factor, you have standby time. This excess capacity allows you to reduce your online adsorption time and “turn the beds around” faster by regenerating the beds in a shorter cycle time. When you are involved in the design of an adsorption unit, it is recommended to add 10 – 20% excess regeneration capacity.
Because of the capacity decline curves flatten out, available standby time may be able to extend the life of a molecular sieve unit when your unit is operating on fixed cycle times. Other operating options include: running each cycle to water breakthrough; and, reducing the cycle times in discreet steps throughout the life of the adsorbent.
Figure 2. Typical process flow diagram for a 3-tower adsorption dehydration system [1]Figure 3. A generic molecular sieve decline curves [1]To illustrate the benefits of standby time, consider the following case study. A natural gas processing plant has commissioned a new 3 tower molecular sieve dehydration unit to process 11.3 x 106 std m3/d (400 MMscfd) prior to flowing to a deep ethane recovery unit. The unit is expected to run for 3 years before needing a recharge and the plant turnaround is based on this expectation. The following assumptions are made:
3 tower system (2 towers on adsorption, 1 on regeneration)
External Insulation
Tower ID = 2.9 m (9.5ft)
Each tower contains 24630 kg [54300 lbm] of Type 4A 4×8 mesh beads
Regeneration circuit capable of handling an extra 15% of flow
Unit is operated on fixed time cycles
No step-change events such as liquid carryover, poor flow distribution, etc.
The design basis and molecular sieve design summary are shown in Tables 1 and 2. The additional 15% of flow from the regeneration gas heater is well below the point at which bed lifting will occur.
Table 1. Design basis for the case studyTable 2. Design Summary for the Case Study
The calculations presented here are valid for low pressure regeneration (less than 4100 kPaa (600 psia). Using the concepts outlined in Chapter 18 of Gas Conditioning and Processing, Volume 2 [1]: The Equipment Modules (9th Edition) we find a design life factor, FL, of 0.6 after 3 years (1 095 cycles) of operation at design conditions. This point lies slightly above the “average” life curve as seen in Figure 4.
After 12 months of operation, a Performance Test Run (PTR) is conducted. The results are shown in Table 3. The feed flow rate and temperature are slightly lower compared to the design values. A water breakthrough time of 20.9 hours is recorded. The FL is determined (using the concepts in Chapter 18) to be 0.68 after 365 cycles (one year of operation). It is important and useful to understand the equation sequence of the concepts in Chapter 18, as shown by Equations 18.5 through 18.10 to arrive at the cited value for FL. This data point is shown in Figure 5 and is seen to lie just below the generic “Average” curve. Note that the slope of the curves are starting to flatten out. Since the PTR FL is lower than the Design FL, the molecular sieves will experience water breakthrough if operated at design conditions in less than three years. Figure 6 shows the projected life factor, FL, after 3 years of service at design conditions. If the capacity decline continues to follow the same trend as seen from the PTR, water breakthrough will occur after 750 cycles or just a little over 2 years from startup if operation continues at design conditions. This is shown in Figure 7.
Figure 4. Design condition life factor [1]
Table 3. Results of Performance Test Run (PTR) after 12 months of operation
Figure 5. Performance test run (PTR) life factor [1]Figure 6. Projected life factor (red triangle) running at design conditions [1]Figure 7. Projected life factor running at design conditions [1] Because the unit has a regeneration circuit that can handle an additional 15% of flow, the complete regeneration cycle (heating, cooling, de- and re- pressurization) can be reduced to 7.0 hours. This allows the beds to turn around faster.
Using the reduced cycle time (the complete cycle time is now 21 hours vs the original 24 hours), we find an FL = 0.53. This is because less water is being adsorbed per cycle. This occurs at around the 1500 cycle mark as shown in Figure 8.
If the plant elects to take advantage of the standby time and operate at reduced cycle time immediately following the PTR, the molecular sieves should last an additional 2.7 years, resulting in a total life of 3.7 years. In this case, standby time will allow the unit to operate until the scheduled plant turnaround.
Figure 8. Projected life factor (red triangle) if standby time is used [1] We can draw the following conclusions from this case study:
The methods presented allow the user to estimate the decline of their adsorbent based on only one performance test run for molecular sieve dehydrators using low pressure regeneration. This permits early formulation of a credible action plan.
Site-specific factors will determine your unit’s decline curve. Consequently, conducting more than one performance test is highly recommended. A poorly performing inlet separator, for example, could result in a unit exhibiting a more pronounced decline than indicated by the generic curves in Figure 3.
Standby time offers a large degree of operating flexibility because the decline curves tend to level off; always try to build in standby time in any new molecular sieve design.
Adsorption capacity is a function of the number of cycles, not calendar time.
Install a good filter coalescer or filter separator upstream of your adsorption unit to keep the contaminants out of the system.
The approach discussed in this Tip of the Month should help a facility engineer plan for the inevitable replacement of molecular sieves in their natural gas dehydration facility.
Campbell, J.M., Gas Conditioning and Processing, Volume 2: The Equipment Modules, 9th Edition, 2nd Printing, Editors Hubbard, R. and Snow–McGregor, K., Campbell Petroleum Series, Norman, Oklahoma, 2014.
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. The water content of a gas depends on the system temperature, pressure and composition of thewater (containing gas). Sour gas will hold more water than sweet gas at the typical operating conditions encountered in a natural gas dehydration facility.There are several methods of calculating the water content of sour gases. The details of these methods can be found in Chapter 6 of Volume 1 of “Gas Conditioning and Processing” [1] and Chapter 20 of the GPSA (Gas Processors and Suppliers Association) data book [2].
In the November 2007 Tip of the Month(TOTM), we discussed the phase behavior of water-saturated sour gases. Using limited experimental data, we also demonstrated the accuracy of Maddox et al. shortcut method [3] and a rigorous calculation method based an equation of state.
In this TOTM, we will present a set of correlations and simplified charts for estimating sour gas water content directly without having to look up the water content of sweet gas. These correlations are based on the Wichert and Wichert chart [4] (Figure 20-9 of the GPSA data book) and Wagner and Pruss water vapor equation [5] and Bukacek correlation for estimating sweet gas water content [6]. The proposed correlations are valid for pressures up to 24 MPa (3500 psia), temperatures up to 175°C (350°F) and H2S equivalent concentrations of up to 50 mole %. The accuracy of the proposed correlations was compared against limited experimental data and a rigorous method using an equation of state.
Proposed Correlations and Charts
The vapor pressure of pure water, from 0 to 360, (32 to 680) can be calculated by Wagner-Pruss equation [5].
(1)
Where:
τ = 1 – (T/TC)
The critical temperature, TC = 647.096 K and critical pressure, PC = 22.064 MPa, T in K, and PV in MPa, and
Bukacek [6] suggested a relatively simple correlation for the water content of lean sweet gas as follows:
(2)
(3)
where T is in °F and PV and P should have the same unit (either kPaa or psia).This correlation is reported to be accurate for temperatures between 15.5 and 238°C (60 and 460°F) and for pressure from 0.10 to 70 MPa (15 to 10,000 psia).
Based on the Wichert and Wichert chart, the sour gas water content is estimated by multiplying the sweet gas water content by a sourness correction factor, F. This correction factor, F, is a function of H2S equivalent concentration (HEC), temperature and pressure. The H2S equivalent concentration is defined by:
HEC = Mole% H2S + 0.7 (Mole % CO2)
To develop the proposed correlation, we have defined an intermediate term represented by X, as a function of temperature and HEC;, i.e, X = f (T, HEC).
X = α + βT + γT2 (5)
where T is in °F and:
α = 195.262 / [1+26.162 e(-0.0957HEC)] (6)
β = -0.8374 / [1+ 27.813 e(-0.0991HEC)] (7)
γ = 0.0011 / [1+ 22.051 e(-0.0861HEC)] (8)
The sourness correction factor, F, is defined as:
F = a + bX + cX2
Where X is calculated by Eq. (5) and the a, b, and c parameters are expressed in terms of pressure(P) in psia as follows:
Equations 5-12 can be used for the range of 0 % <HEC<50 %. However, to generate more accurate results in the range of 10% the sourness correction factor (FHEC) may be interpolated between F at HEC of zero (F=1, sweet gas) and F at HEC of 10 (FHEC10%) calculated by Eqs. 5-12, as follows.
FHEC = FHEC10%(FHEC10% 1)(10 HEC)/10 (13)
Calculation Procedure
Calculate the water content of sweet natural gas by Eqs. 1, 2, and 3.
Calculate the HEC (H2S equivalent concentration) by Eq. 4.
Calculate the X and parameters by Eqs. 5 through 8.
Calculate the sourness correction factor (F) by Eqs. 9 through 13.
Calculate the sour gas water content by multiplying the sweet gas water content (Step 1) times the Sourness Correction Factor (F) given by: (Steps 2-4)
Based on the above proposed correlations and procedure several charts using a spreadsheet were generated. Figures 1, 2, and 3 present sourness correction factors for pressures of 140, 1400, and 21000 kPaa (20, 203, and 3045 psia), respectively. Similarly, the corresponding water content of sour gas for the same pressures are presented in Figures 4, 5, and 6, respectively.
Figure 1. Variation of sourness factor, F, with H2S equivalent concentration and temperature at 140 kPaa (20 psia)Figure 2. Variation of sourness factor, F, with H2S equivalent concentration and temperature at 1400 kPaa (203 psia)Figure 3. Variation of sourness factor, F, with H2S equivalent concentration and temperature at 21000 kPaa (3045 psia)Figure 4a. Sour gas water content as a function of H2S equivalent and temperature at 140 kPaaFigure 4b. Sour gas water content as a function of H2S equivalent and temperature at 20 psiaFigure 5a. Sour gas water content as a function of H2S equivalent and temperature at 1400 kPaaFigure 5b. Sour gas water content as a function of H2S equivalent and temperature at 203 psiaFigure 6a. Sour gas water content as a function of H2S equivalent and temperature at 21000 kPaaFigure 6b. Sour gas water content as a function of H2S equivalent and temperature at 3045 psia
Model Evaluation
To evaluate the accuracy of the proposed correlations, the water content of several sour gas mixtures were predicted and compared against the experimental data and other shortcut and rigorous methods. The compositions of 12 sour gas mixtures for two isotherms and at pressure of 1380 kPaa (200 psia) along with the corresponding measured water contents from GPSA RR 174 [7] studied in this TOTM are presented in Table 1. The predicted water contents by the proposed correlations are compared with these experimental data and the results obtained from Maddox et al. [3] and ProMax [8]. The percent deviations between predicted results and the experimental values are presented in Table 1.
The calculated water content results by these three methods are also plotted against the experimental data in Figures 7 and 8 for the two isotherms of 48. 9 and 93.3°C (120 and 200°F), respectively. Figure 7 indicates that even though there are deviations between predicted values and the experimental data, the results of the three methods are in close agreement with each other. Figure 8 indicates better agreement between the predicted values and the experimental data but the agreement among the predicted values are not as close as in Figure 7.
Table 1. Comparison of prediction water content by the proposed correlations (W-W), Maddox et al. and ProMax with the experimental values [5] of several sour gas mixtures at 1380 kPaa (200 psia)
* Percent deviation = 100(Experimental Value –Calculate Value)/(Experimental Value)Figure 7. Calculated water content by Wichert and Wichert, Maddox et al., and ProMax against experimental data at 48. 9°C (120°F) and 1380 kPaa (200 psia).Figure 8. Calculated water content by Wichert and Wichert, Maddox et al., and ProMax against experimental data at 93.3°C (200°F) and 1380 kPaa (200 psia).
Conclusions
The following conclusions can be made based on this case study:
Based on the Wichert and Wichert water content chart for sour gases a set of correlations (Equations 5 through 12) for spreadsheet calculation was developed and presented.
The proposed correlations are valid for pressures up to 24.14 MPa (3500 psia), temperatures up to 175°C (350°F) and H2S equivalent concentration of up to 50 mole %.
Based on the developed correlations, a set of easy-to-use charts (Figures 4 through 6) were prepared and presented which can be used to predict the water content of sour gas mixtures directly. Contrary to the original Wichert and Wichert chart, there is no need to look up water content of sweet gas.
Campbell, J.M., “Gas conditioning and Processing, Vol. 1: The Basic Principles”, 9th Edition, 2nd Printing, Editors Hubbard, R. and Snow–McGregor, K., Campbell Petroleum Series, Norman, Oklahoma, 2014.
GPSA Engineering Data Book, Section 20, Volume 2, 13th Edition, Gas Processors and Suppliers Association, Tulsa, Oklahoma, 2012.
Maddox, R.N., L.L. Lilly, M. Moshfeghian, and E. Elizondo, “Estimating Water Content of Sour Natural Gas Mixtures”, Laurence Reid Gas Conditioning Conference, Norman, OK, Mar., 1988.
Wichert, G. C. and E. Wichert, “Chart Estimates Water Content of Sour Natural Gas,” Oil & Gas J., p. 61, Mar. 29, 1993.
Wagner, W. and Pruss, A., J. Phys. Chem. Reference Data, 22, 783–787, 1993.
Bukacek, R.F., “Equilibrium Moisture Content of Natural Gases” Research Bulletin IGT, Chicago, vol. 8, 198-200, 1959.
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.
ProMax 3.2, Bryan Research and Engineering, Inc., Bryan, Texas, 2014.
In this Tip of the Month, we reflect back to December 2008, and get a reminder from the United States Chemical Safety Board (CSB) to remain focused on process safety and accident prevention during this time of lower oil prices.
During the economic downturn of 2008, oil prices dropped significantly. The latest drop in crude oil prices is similar. At that time, the CSB produced a video message asking companies to stay focused on process safety. That message is very relevant today.
Process Safety and Low Oil Prices
In the past, market conditions have occurred where oil prices have been low, such as we are experiencing today. Corporate cost cutting during these low oil price events have contributed to process safety incidents years later. In 2008, the United States Chemical Safety Board (CSB) Chairman John Bresland provided a reminder to oil companies that it is important to stay focused on process safety, even when prices are low. This was accomplished through a press release and a video safety message that is appropriate for this time [1].
Low oil prices, combined with striking workers at US refineries increase the challenges faced by managers to insure that process safety is a core value of the organization.
Containing overhead and operating costs during these market conditions may lead some to take shortcuts and make hasty decisions without considering all the process safety implications of these decisions. The attached press release and video safety message is as appropriate today as it was in 2008. This video message would be an excellent safety moment topic and hopefully will allow us to remain focused on process safety.
Dec 22, 2008
In First Video Safety Message, CSB Chairman John Bresland Calls for Industry to Remain Focused on Process Safety, Accident Prevention During Recession
Washington, DC, December 22, 2008 – In his first video safety message, CSB Chairman John Bresland today said that chemical companies and refineries need to continue to invest in process safety and preventive maintenance, even as the economic downturn cuts into sales and profits.
“My safety message for oil and chemical companies is clear: even during economic downturns, spending for needed process safety measures must be maintained,” Chairman Bresland stated in the message. He noted that the CSB investigation of the 2005 Texas City refinery disaster linked the accident to corporate spending decisions in the 1990s, when low oil prices triggered cutbacks in maintenance, training, and operator positions at the plant.
“Unfortunately, around the country, refinery accidents continue to be a concern,” Chairman Bresland said, pointing to three major accidents that occurred at refineries in Texas this year, including a fire at a refinery in Tyler last month that fatally burned two workers and forced the refinery to shut down for months. “Today, as gasoline prices remain low, companies should weigh each decision to make sure that the safety of plant workers, contractors, and communities is protected.”
Safety Messages are a new communication tool for the agency, consisting of short videos from the Chairman or the other board members. In the coming weeks and months, new messages will be released on a variety of current issues in chemical process safety.
“I encourage all of our stakeholders to join the discussion on YouTube.com and Blogger.com and share their thoughts about the subject of these messages,” Chairman Bresland said. Comments and ideas for future Safety Messages can also be emailed to safetymessages@csb.gov.
The CSB is an independent federal agency charged with investigating industrial chemical accidents. The agency’s board members are appointed by the president and confirmed by the Senate. CSB investigations look into all aspects of chemical accidents, including physical causes such as equipment failure as well as inadequacies in regulations, industry standards, and safety management systems.
The Board does not issue citations or fines but does make safety recommendations to plants, industry organizations, labor groups, and regulatory agencies such as OSHA and EPA. Visit our website, www.csb.gov.
For more information, contact Daniel Horowitz at (202) 261-7613 or Hillary Cohen at (202) 261-3601.
In this Tip of the Month (TOTM) we will discuss how to determine CO2 solubility and flashing issues in water at pressures and temperatures commonly associated with gathering systems and production facilities. This is mainly important for CO2 Enhanced Oil Recovery (EOR) floods as the CO2 concentration is high and the initial separation is at higher pressures than is common in non-CO2 EOR oilfields. These two conditions result in significant dissolving of CO2 into the produced water with resultant flash gas from downstream Free Water Knockouts (FWKO), treaters, and tanks. In mature CO2 EOR floods with Water-Alternating-Gas (WAG) injection schemes, it is likely that most of the flash gas in the downstream separations will be from the produced water.
While this TOTM is significant mainly for CO2 EOR floods, the following analysis is general in nature; it would apply to other situations involving CO2 solubility in water issues. This analysis assumes that there is no H2S. H2S would have somewhat higher solubility than CO2 which would force more gas to flash from the FWKO and tanks. Higher H2S than about 5% would begin to appreciably increase the solubility of H2S into water.
A hypothetical field production example will demonstrate how to calculate the CO2 solubility and flash gas volumes. The field configuration is shown in Figure 1.
A common configuration for a CO2 EOR flood is to have the field gathering system consist of three flashes. The first flash is at the Field Separators, the second flash is at the FWKO, and the third flash is at the tanks. A number of wells are gathered into Field Separators for the first flash of gas from the liquids. For the purposes of this case, all separators are assumed to operate at a single pressure and temperature. The amount of gas from the Field Separators is not able to be calculated from the analysis presented in this TOTM. Instead, the gas from this separation is the result of the complex flows within the reservoir through the downhole equipment to the separator. This analysis is applicable to the next two flashes that result from the liquid flowing to the FWKO and then to the Water Tank. Also note that the gas from the oil (which includes both hydrocarbon gas and CO2 and also will be a significant volume) is beyond the scope of this analysis.
Figure 1. A simplified production flow diagram
With the above figure and discussion in mind, the problem statement is as follows:
A CO2 EOR flood is in operation. The Field Separators operate 15.6 °C (60 °F) and 1483 kPag (215 psig) and the gathering system gas is 87.5% CO2. The liquids flow from the Field Separators to a central tank battery where the unheated FWKO is operating 15.6 °C ( 60 °F) and 207 kPag (30 psig) and the gas from it is 89.7% CO2. The tanks operate at 3.45 kPag (0.5 psig.) The atmospheric pressure is 93.1 kPaa (13.5 psia). The water from the field is 3180 STm3/d (20,000 bbl/day) and has 4 wt% associated salts.
Determine the amount of gas flashed from the produced water at the FWKO and water tanks.
The analysis begins by determining the CO2 in solution within the Field Separators. The solution gas is determined from Figure 2 below.
Figure 2 (SI). Solubility of CO2 in fresh water as a function of its partial pressureFigure 2 (FPS). Solubility of CO2 in fresh water as a function of its partial pressure
Step 1 – Find the amount of CO2 in solution in the water in the Field Separator from Figure 1. Determine the CO2 partial pressure in the gas phase of the Field Separator which is operating at 1380 kPaa (200 psia). Then read the gas saturation at the separation temperature which is 15.6 °C (60 °F). The solution gas is determined to be 11.23 Sm3/STm3 (63 scf/bbl). The key is to measure the gas composition at the outlet of the Field Separator.
Note that the curves for 0 °C (32 °F) stops at 2069 kPaa (300 psia), 4.4 °C (40 °F) stops at 2759 kPaa (400 psia), and 15.6 °C (60 °F) stops at 4828 kPaa (700 psia) CO2 partial pressure. That is because the CO2 becomes liquid as it increases much past these pressures. No design should contemplate separation where the CO2 is still liquid. Its density would be too close to oil so separation would not be feasible. The curves themselves will not extrapolate successfully from these endpoints; the character of the curve changes as the CO2 moves into the liquid phase.
It is not easy to read the saturation at the FWKO and even more difficult to read the tanks so a close-up of the graph has been prepared as Figure 3. The “X” axis is now zero to 690 kPaa (100 psia) rather than zero to 5517 kPaa (800 psig). The “Y” axis is now zero to 8.92 Sm3/STm3 (50 scf/bbl) rather than zero to 35.66 Sm3/STm3 (200 scf/bbl). The various curves now are reasonably approximated as straight lines which intercept the “Y” axis at zero. The equations are included.
Figure 3 (SI). Close-up of solubility of CO2 in fresh water as a function of its partial pressureFigure 3 (FPS). Close-up of solubility of CO2 in fresh water as a function of pressure
Step 2 – The CO2 solubility of the water in the FWKO is determined in the same fashion as for the Field Separator. The composition of the gas is the gas exiting the FWKO is the composition to use to determine the CO2 partial pressure. In the FWKO 2.23 Sm3/STm3 (12.5 scf/bbl) remains in solution.
Step 3 – This shows the gas in solution in the storage tanks. An assumption is made that the gas is liberated from the tankage that the tanks are 100% CO2. It will be slightly less than 100% but should be close enough for design purposes. This assumption will slightly overstate the amount of CO2 remaining in solution. Observe that in the storage tanks the water still has 0.8 Sm3/STm3 (4.5 scf/bbl) of CO2. This is enough CO2 to make the water substantially more corrosive than water from non-CO2 EOR floods.
The determination of CO2 solubility has thus far been made for fresh water but the produced water contains salts (Na, Mg, Ca). An adjustment is to be made for each of the three saturations based on the 15.6 °C (60 °F) temperature and the salts percentage by weight of the produced water. This is accomplished with Figure 4 which calculates the appropriate reduction in solubility.
Figure 4. Brine Correction for CO2 Solubility in Water
Step 4 – Make the adjustments for salt in the water by applying the Salinity Reduction Factor (SRF). The temperature impact is small enough that only three curves are shown. It would be too difficult to read the graph if all curves were shown. To read this point it may be easier to use the corresponding Table 1 which contains more temperature data.
Table 1. Salt Reduction Factor (SRF) [1]
The data obtained from the graphs and data from the problem statement are summarized in Table 2. With some calculations the appropriate gas volumes can be determined.
Table 2. Example of CO2 Flashing for 3180 STm3/d (20,000 bbl/day) of Water, 4% Salts
Step 5 – Column (D) – Input the data from Figure 2 and Figure 3 into the appropriate row
Step 6 – Column (C) – Input the SRF from Figure 4 into the last row
Step 7 – Column (E) – Subtract 2.23 Sm3/STm3 (12.54 scf/bbl) from 11.23 Sm3/STm3 (63 scf/bbl) = 9 Sm3/STm3 (50.46 scf/bbl) to calculate the amount of gas that evolves from solution into the gas phase from the Field Separators to the FWKO.
Step 8 – Column (E) – Subtract 0.8 Sm3/STm3 (4.5 scf/bbl) from 2.23 Sm3/STm3 (12.54 scf/bbl) = 1.43 Sm3/STm3 (8.04 scf/bbl) to calculate the amount of gas that evolves from solution into the gas phase from the FWKO to the tankage.
Step 9 – Column (F) – Multiply 9.00 Sm3/STm3 (50.46 scf/bbl) times 3180 STm3/d (20,000 bbl/d) = 28.58×103 Sm3/d (1009 MSCFD) to calculate the amount of gas to compress from the FWKO
Step 10 – Column (F) – Multiply 1.43 Sm3/STm3 (8.04 scf/bbl) times 3180 3180 STm3/d (20,000 bbl/d) = 4.56×103 Sm3/d (161 MSCFD) to calculate the amount of gas to compress from the tankage
Step 11 – Column (G) – Multiply the quantities of Column (F) times the SRF to determine the gas as adjusted for salinity. The total gas is 28.29×103 Sm3/d (999 MSCFD).
Supplemental Data:
A 2nd order polynomial for each of the curves in Figure/s 2 has been prepared. These equations will allow the user to determine the solution gas by calculation rather than by reading the graphs directly.
Figure 2 can be presented by Equation 1 [2].
SCO2 = A (PCO2)2 + B (PCO2) (1)
Where:
Conclusions:
The combination of relatively high Field Separator pressures and high CO2 content in gas from CO2 EOR floods results in substantial flashing of CO2 from water in the FWKO and tanks. This has the potential of overwhelming the piping, Vapor Recovery Units, and Flash Gas compressors.
To learn more about similar cases and how to determine the impact of CO2 on field operational problems, we suggest attending our PF81 (CO2 Surface Facilities) 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 http://petroskills.com/consulting, or email us at consulting@PetroSkills.com.
By: Paul Carmody
Reference:
Chang, Y.-B., Coats, B. K., & Nolen, J. S. (1998, April 1). A Compositional Model for CO2 Floods Including CO2 Solubility in Water. Society of Petroleum Engineers. doi:10.2118/35164-PA.
VMG Sim v8.0 (Build 85) August 2014, Thermo = “APR for Natural Gas”.
In the October and November 2014 Tips of the Month (TOTM), we demonstrated that Gas-Oil-Ratio (GOR) has a large impact on the capacity of crude oil gathering lines. If GOR is less than the saturation solution gas, the increase in GOR reduces the viscosity and density of crude oil which causes the pressure drop to decrease. However, if the GOR exceeds the saturation solution gas the system becomes two phase and pressure drop increases.
The solution gas is a function of temperature, pressure, gas and liquid compositions. In this TOTM, we will study the impact of temperature on the crude oil properties in the gathering systems for the case presented in the November 2014 TOTM. Specifically, the variation of the crude oil relative density and viscosity with the temperature will be studied. Finally, the impact of temperature on the oil and gas velocity and pressure drop along a gathering line for nominal pressure of 6900 kPag (1000 psig) and nominal pipe size of 101.6 mm (4 inches) will be demonstrated using a multiphase rigorous method from a commercial simulator. The calculated properties, oil and gas velocities and pressure drops are presented in graphical format as a function of the oil stock tank volume flow rate, solution gas, RS, and temperature.
Case Study
For the purpose of illustration, we considered a case study for transporting a crude oil of relative density of 0.852 (°API = 34.6) at stock tank conditions combined with a gas with relative density of 0.751. The selected GORs were 0 (dead oil), 17.8, 35.6, and 89 Sm3 of gas/STm3 of oil (0, 100, 200, 500 scf/STB). The compositions of oil and gas are presented in Table 1. The oil C6+ was characterized as 30 hypothetical single carbon number (SCN) [1] ranging from SCN6 to SCN35 while the gas C6+ was characterized by 10 hypothetical SCN ranging from SCN6 to SCN15. For details of the SCN components, see Table 3.2 on page 64 of reference [1]. The mole fraction of SCN components were determined by an exponential decay algorithm [2]. The feed enters the line at 15.6 ˚C (60 ˚F) for case 1 and 43.3 ˚C (110 ˚F) for case 2.
Table 1. Feed composition at stock condition
The following assumptions were made:
Steady state conditions
The line is 1.601 km (1 mile) long with nominal size of 101.6 (4 inches), onshore buried line.
Segment lengths and elevation changes are presented in Table 2 and Figure 1. This elevation profile is considered to be approximately equivalent to “rolling” terrain.
Pipeline inside surface roughness of 46 microns (0.046 mm, 0.0018 inch)
Line nominal pressure 6900 kPag (1000 psig)
The feed enters the line at 15.6 ˚C (60 ˚F) for case 1 and 43.3 ˚C (110 ˚F) for case 2.
The ground/ambient temperature, is 15.6 ˚C (60 ˚F)
Water cut is 0 (no water in the feed).
Overall heat transfer coefficients of 2.839 W/m2-˚C (0.5 Btu/hr-ft2-˚F), for onshore buried line.
Simulation software ProMax [3] and using the Soave-Redlich-Kwong (SRK) Equation of State [4] for vapor-liquid equilibrium and Beggs-Brill method for two-phase pressure drop calculation [5].
Table 2. Line segment length and elevation change
Figure 1. Gathering line elevation profile
Results and Discussions:
The two phase (oil and gas) flow through the gathering line was simulated by ProMax with SRK EOS for vapor-liquid equilibria and Beggs-Brill for two phase pressure drop calculations. Figure 2 presents the variation of temperature along the pipeline for the case of 0 GOR for an oil rate of 636 STm3/d (4000 STB/day). Since the ambient temperature was assumed to be 15.6°C (60°F), for case 1 the crude oil temperature in the line remained constant. However, for the second case the temperature dropped from 43.3°C (110°F) to about 40°C (104°F) within 1.6 km (1 mile) of the line. For the second case the average line temperature is about 41.7°C (107°F).
Figure 2. Variation of line temperature along gathering line for two feed temperatures of 15.6 and 43.3 C (60 and 110) and an oil rate of 636 STm3/d (4000 STB/day)
Figure 3 presents the bubble point pressure of the feed to the gathering line at the average line temperatures of 15.6 and 41.7 (60 and 107) as a function of solution gas. This figure indicates that for the nominal line pressure of 6900 kPa (1000 psig), the crude oil is under saturated up to GOR of 51.8 Sm3/STm3 (290.5 scf/STB) for the lower average line temperature. Similarly it shows oil is under saturated up to GOR of 39.4 Sm3/STm3 (221.3 scf/STB) for the higher average line temperature. For GOR greater than these values, the oil becomes saturated with gas and gas breaks out. The system becomes two phase gas and liquid flow.
Figure 4 presents the line pressure drop per unit length as a function of oil stock tank volume rate, GOR, and feed temperature. In this figure and subsequent figures, the solid lines are for the feed temperature of 15.6°C (60°F) and symbols are for feed temperature of 43.3°C (107°F). Figure 4 indicates that as the GOR increases from 0 to 35.7 Sm3/STm3 (0 to 200 scf/STB), the pressure drop decreases but increases with further increase in GOR of 89 Sm3/STm3 (500 scf/STB) and higher. The dividing point is at a saturation solution gas of 39.4 Sm3/STm3 (221.3 scf/STB) and 51.8 Sm3/STm3 (290.5 scf/STB) for temperatures of 41.7°C (104°F) and 15.6°C (60°F), respectively. At higher temperature the increase of GOR reduces the pressure drop when solution gas is under saturated but increases the pressure drop for GOR greater than the saturated solution gas.
Figure 3. Bubble point pressure of the feed to the gathering line as a function of solution gas at nominal line temperature of 15.6 and 41.7 (60 and 107)
Figure 4. Variation of pressure drop per unit length with oil stock tank volume rate, GOR, and temperature – Solid curves for 15.6°C (60°F) and symbols for 43.3°C (107°F)
Figure 5 presents the variation of oil relative density along the line as a function of solution gas (RS) and temperature. This figure indicates that as the RS increases, the oil relative density decreases. Note as the temperature increases the solution gas (RS) decreases.
Figure 5. Variation of oil relative density with solution gas and temperature along the gathering line at 6900 kPag for 101.6 mm pipe diameter, oil rate of 636 STm3/d (4000 STB/day). Lines are for feed temperatures of 15.6 and symbols are for 43.3 C (60 and 110)
Figure 6 shows that as the RS increases, the oil viscosity decreases considerably. The reduction of viscosity causes pressure drop to decrease. The simulation results (Figure 3) indicated that for GOR less than 51.8 Sm3/STm3 (290.5 scf/STB) at 15.6°C (60°F) or 39.4 Sm3/STm3 (221.3 scf/STB) at 41.7°C (104°F), the flow is under saturated single liquid phase; however, for higher GOR the flow becomes saturated two phase (gas and liquid) which causes the pressure drop to increase. The increase in pressure drop due to higher GOR (and higher total flow rate) is more than the decrease in pressure drop due to reduction of oil viscosity as a result of solution gas. The net effect is higher pressure drop compared to dead oil (GOR = 0) pressure drop. This figure also indicates that at zero and low GOR, in which the system is single liquid phase, the temperature has a larger impact on the crude oil viscosity.
Figure 6. Variation of oil viscosity with solution gas, along the gathering line at 6900 kPag for 101.6 mm pipe diameter, oil rate of 636 STm3/d (4000 STB/day). Lines are for feed temperatures of 15.6 and symbols are for 43.3 (60 and 110)
Figure 7 presents the variation of oil and gas velocity for two stock tank oil rates and two feed temperatures along the gathering line at 6900 kPag for 101.6 mm pipe diameter and GOR of 89.1 Sm3/STm3 (500 scf/STB). The lines are for temperature of 15.6 and symbols are for temperature of 43.3 C (60 and 110). Figure 7 indicates that the oil velocity remains constant along the line but the gas velocity increases due to pressure drop in the line and more gas coming out of the solution. This figure also indicates that the impact of temperature on gas velocity is more than on the liquid phase velocity.
Figure 8 presents the impact of GOR and temperature on pressure drop along the gathering line at 6900 kPag for 101.6 mm pipe diameter and an oil rate of 636 STm3/d (4000 STB/day).
Figure 7. Variation of oil and gas velocity for two stock tank oil rate along the gathering line at 6900 kPag for 101.6 mm pipe diameter and GOR of 89 Sm3/STm3 (500 scf/STB). Lines are for feed temperatures of 15.6 and symbols are for 43.3 C (60 and 110)
Figure 8. Impact of GOR, Sm3/STm3 (scf/STB) on pressure drop along the gathering line at 6900 kPag for 101.6 mm pipe diameter, oil rate of 636 STm3/d (4000 STB/day). Lines are for feed temperatures of 15.6 and symbols are for 43.3 C (60 and 110)
As can be seen in this figure, for GOR less than saturation GOR, the pressure drop decreases as GOR increases but for GOR greater than saturation GOR, due to presence of two phase flow, the pressure drop increases. It also shows that for GOR less than saturation, increase in temperature reduces the pressure drop but for GOR greater than saturation, the increase in temperature increases the pressure drop.
Conclusions
The following conclusions can be made based on this case study:
For under saturated oil, the increase in temperature reduces oil density, viscosity and pressure drop. For saturated oil, the increase in temperature increases pressure drop due to increased free gas volumes.
Even though increasing temperature reduces the solution gas, it reduces the viscosity of under saturated crude oil but its impact on saturated liquid viscosity diminishes. Increasing temperature decreases the viscosity of crude oil but it also decreases the gas in solution. The two effects can diminish the effect of temperature on viscosity.
The impact of temperature on gas velocity is higher than its impact on liquid velocity.
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: Mahmood Moshfeghian
Reference:
Campbell, J.M., Gas Conditioning and Processing, Volume 1: The Basic Principles, 9th Edition, 2nd Printing, Editors Hubbard, R. and Snow–McGregor, K., Campbell Petroleum Series, Norman, Oklahoma, 2014.
Moshfeghian, M., Maddox, R.N., and A.H. Johannes, “Application of Exponential Decay Distribution of C6+ Cut for Lean Natural Gas Phase Envelope,” J. of Chem. Engr. Japan, Vol 39, No 4, pp.375-382 (2006)
ProMax 3.2, Bryan Research and Engineering, Inc., Bryan, Texas, 2014.
One of the most common problems in Oil and Gas Processing facilities is underperforming vapor / liquid separators. The most common types of gas-liquid separators are:
Slug catchers
Vessel / Finger-type
“Conventional” separators
Vertical / Horizontal
Scrubbers (i.e. Compressor Suction Scrubbers)
Gas “polishers”
Coalescing Filters / Filter Separators
Underperforming separators generally result from either: 1. the wrong type of equipment was selected for the application, or 2. the correct type of equipment was selected, but the sizing methodology was inadequate. The type of separator required for an application depends largely on the gas-liquid ratio of the stream to be treated, and the flow variability of the process, as shown in Figure 1. As the flow variability increases with low to moderate gas-liquid ratios, the separator selection will move from a conventional separator to a slug catcher. For applications where there is a high gas-liquid ratio (i.e. very low liquid content), and the flow variability is moderately low, scrubbers and gas polishers would be the appropriate equipment selection depending upon the gas quality requirement for the treated stream.
Figure 1. Gas-Liquid Separation Equipment Selection Map [1]Figure 2. Troubleshooting Methodology [1]Unfortunately, once the equipment has been selected and installed, it is very costly to replace if the separator was not specified properly. Common separator performance issues are: too much liquid in the separated gas, inadequate slug/surge capacity, and too much gas in the separated liquid.
This paper is focused on troubleshooting inadequate liquid removal from the gas for conventional separators (moderate to high liquid loads) and scrubbers (very low liquid loads).
A troubleshooting methodology is provided in Figure 2 [Reference No]. The problem, in this case, is too much liquid in the separated gas stream. In order to effectively troubleshoot separator performance, it is required to understand the metrics of good performance, and the functions and analysis of the various components of the separation equipment.
Typical performance metrics for separators are provided in Table 1. The specific performance requirements for a given separator are set by the sensitivity of the downstream process / equipment to the presence of liquids. For example, the product gas (sales gas) off of the cold separator in an NGL Extraction facility is sensitive to the presence of entrained liquids. The product gas can go off specification if there is too much carryover of liquids from the cold separator. On the other hand, the sensitivity of the downstream equipment from the facility inlet separator is much less, and the amount of liquids entrained in the gas is more tolerable.Table 1. Example separator performance metrics [1]
Separator Components
The main components of a separator, shown in Figure 3, are the feed pipe, inlet device, gas gravity separation section, mist extractor and the liquid gravity separation section. The gas/liquid separator components will be briefly discussed in regards to their effects on gas/liquid separation performance. These effects need to be understood and quantified in order to troubleshoot separator operations, and to identify modifications that can be made to improve performance. The liquid gravity separation section will not be discussed.
Figure 3. Parts of a Conventional Separator [2]Inlet Feed Pipe
The inlet feed pipe sizing and geometry is important as it is desired to keep the multiphase flow pattern “stabilized” in the piping to minimize the production of small liquid droplets, and liquid entrainment into the gas phase. Figure 4 [2] shows the effect of feed pipe velocity on liquid entrainment. Figure 5 [2] demonstrates how quickly the liquid entrainment increases once the entrainment inception point is reached.
Figure 4. Effect of feed pipe velocity on liquid entrainment [2]Figure 5. Example of liquid entrainment behavior in a gas-liquid system [2]Some general guidelines for inlet piping to minimize liquid entrainment are:
Provide 10 diameters of straight pipe upstream of the inlet nozzle without valves, expansions/contractions or elbows.
If a valve is required, only use full port gate or ball valves.
Inlet Device
The main purpose of an inlet device is to improve separation performance. This is achieved by maximizing the amount of gas-liquid separation occurring in the feed pipe, minimizing droplet shearing, and optimizing the downstream velocity distributions of the separated phases into the separator. Schematics for inlet devices are shown in Figure 6. In large capacity, more critical separation applications, the vane-type and cyclonic inlet devices are commonly used. The simpler, and less expensive, impact (or diverter plates) are often used where the separation performance is less critical.
Figure 6. Various separation equipment inlet devices [2]Table 2 provides a comparison of the performance of various inlet devices.
Table 2. Comparison of inlet devices [2]
The inlet momentum (ρmV2m – density*velocity2 of the mixture) of the feed stream is typically used to select and size inlet devices. Table 3 provides the suggested upper limits of inlet momentum values. For conditions where it is not practical to avoid higher feed pipe velocities, it must be recognized that failure to do so will result in higher entrainment loads, smaller droplet sizes, and more difficult separation conditions.
Table 3. Inlet device ρV2 upper limits [3]
The quality of the flow distribution downstream of the inlet device is critical to mist extractor performance. Historically, tracer surveys have been used to provide an approximate indication of the continuous phase velocity within separators. In more recent years, the use of CFD (Computational Fluid Dynamics) has provided insight into the flow behavior of fluids, and has resulted in significant improvement in separator internals design. Separator performance is to a large degree dependent on the removal of droplets/ bubbles from the continuous phase. The efficiency of this removal is a function of the continuous phase velocity, thus the importance of understanding the factors that affect velocity profiles. Figure 7 provides an example of ideal versus actual gas velocity profiles within a separator.
Figure 7. Ideal and actual gas velocity profiles [3]Gas Gravity Separation Section
The gas gravity separation section of a separator has two main functions: 1) reduction of entrained liquid load not removed by the inlet device, 2) improvement / straightening of the gas velocity profile.
Most mist extractors have limitations on the amount of entrained liquid droplets that can be efficiently removed from the gas, thus the importance of the gas gravity section to remove the liquids to an acceptable level upstream of the mist extractor. This is particularly important for separators handling higher liquid loads. For scrubber applications with low liquid loadings, the Ks value will be primarily dependent on the mist extractor type, and the gas gravity separation section becomes less important.
For the higher liquid load applications, there are two approaches for sizing the gravity separation section to remove liquid droplets from the gas: 1) Ks method, 2) Droplet settling theory.
Historically the Ks method has been employed as it can provide reasonable results and is easy to use, but has shortcomings in terms of quantifying separator performance. References 3-5 provide the details on the droplet settling theory methods which can be used to more accurately quantify separator performance. The Ks approach is limited in that it only informs of the average droplet size, but cannot quantify the amount of liquid droplets exiting the gas gravity section.
The Ks method (Equation 1) is an empirical approach to estimate the maximum allowable gas velocity to achieve a desired droplet separation.
Where:
ρL = liquid density kg/m3 (lbm/ft3)
ρg = gas density kg/m3 (lbm/ft3)
Vgmax = maximum allowable gas velocity m/s (ft/sec)
KS = an empirical constant m/s (ft/sec)
Figure 8 provides the relationship of Ks values for various droplet sizes and separator operating pressures for the gas gravity section. Typically, a Ks value is selected that will achieve removal of all entrained droplets larger than a specified target droplet diameter in the original design of the separator. For conventional separators the target droplet diameter is typically 150 microns, and for scrubbers the target droplet size should not exceed ~500 microns. This correlation can also be used to determine the performance of the gas gravity section based upon current operating conditions. The separator Ks value can be estimated from the actual velocity and fluid conditions, and the droplet size removed in the gas gravity section can be estimated from Figure 8.
Figure 8. Ks vs. pressure and droplet size for empty vessels [2]Mist Extraction Section
The mist extractor is the final gas cleaning device in a conventional separator. The selection, and design to a large degree, determine the amount of liquid carryover remaining in the gas phase. The most common types include wire mesh pads (“mesh pads”), vane-type (vane “packs”) and axial flow demisting cyclones. Figure 9 shows the location and function of a typical mist extractor in a vertical separator.
Figure 9. Typical mist extractor in a vertical separator [2]Mist extractor capacity is defined by the gas velocity at which re-entrainment of the liquid collected in the device becomes appreciable. This is typically characterized by a Ks value, as shown in Equation 1. Mesh pads are the most common type of mist extractors used in vertical separator applications. The primary separation mechanism is liquid impingement onto the wires, followed by coalescence into droplets large enough to disengage from the mesh pad. Figure 10 provides some mesh pad examples. Table 4 provides a summary of mesh pad characteristics and performance parameters.
Figure 10. Mesh pad examples [1]
Table 4. Mesh pads summary of characteristics and performance parameters [1,4]
Notes:
Flow direction is vertical (upflow).
Assume mesh pad Ks values decline with pressure as shown in Table 5. Table 5 was originally developed for mesh pads, but is used as an approximation for other mist extractor types. [6].
If liquid loads reaching the mesh pad exceed the values given in Table 4, assume capacity (Ks) decreases by 10% per 42 L/min/m2 (10% per gal/min/ft2). [3-5].
These parameters are approximate.
Table 5. Mesh pad Ks deration factors as a function of pressure [2]
Vane packs, like mesh pads, capture droplets primarily by inertial impaction. The vane bend angles force the gas to change direction while the higher density liquid droplets tend to travel in a straight-line path, and impact the surface of the vane where they are collected and removed from the gas flow. Figure 11 shows a schematic of a single-pocket vane mist extractor. Table 6 provides vane pack performance characteristics.
Figure 11. Single-pocket vane schematic [2]
Table 6. Typical vane-pack characteristics [1,4]
Notes:
Assume vane-pack Ks values decline with pressure as shown in Table 5.
If liquid loads reaching the vane pack exceed the values given in Table 4, assume capacity Ks decreases by 10% per 42 L/min/m2 (10% per gal/min/ft2). [3-5].
These parameters are approximate only. The vane-pack manufacturer should be contacted for specific information.
In the case of demisting cyclones, the vendor should be consulted in regards to performance for the current operations of interest.
Troubleshooting
When troubleshooting a separator, one needs to quantify the acceptable performance of the separator in terms of the amount of liquids in the separated gas. The separator physical condition and design is then assessed to determine the liquid removal capability of the separation equipment installed. Each separator component should be analyzed in terms of the current operating conditions versus the original design specifications.
Table 7 provides a few common causes of inadequate liquid removal performance of a separator. The separator components that need to be reviewed are identified to determine the specific limitation. This table can serve as a road map for the calculations to work through when doing this type of analysis.
Table 7. Common conditions resulting in inadequate separated gas quality [1]
There are numerous options available to improve the performance of a separator depending upon what the cause for the poor performance is. Depending upon the size and construction of the separator, it may be possible to retro-fit the separator internals. Another option may be modification of the inlet feed piping geometry and number of fittings upstream of the vessel if this is found to be less than ideal. The inlet device may be damaged, or in the bottom of the vessel. Higher efficiency inlet devices may be an option for consideration. Frequently, different mist extraction equipment can be selected to improve performance. For example, if the mist extractor Ks value is greater than the original design, a different mist extraction device could improve performance. The separator internals modifications may or may not be possible without welding on the vessel (which adds additional complications and cost to the project).
The operating liquid levels should also be reviewed in terms of the distance of the normal operating liquid level in relation to the inlet feed device. If the liquid level is too high, the gas velocity from the inlet could be re-entraining liquids that were previously separated in the feed piping / inlet device. Unfortunately, in some cases the only way to improve performance is to cut rate (i.e. gas velocity) through the separator.
Campbell, J.M., Gas Conditioning and Processing, Volume 2: The Equipment Modules, 9th Edition, 2nd Printing, Editors Hubbard, R. and Snow–McGregor, K., Campbell Petroleum Series, Norman, Oklahoma, 2014.
Bothamley, M. 2013. Gas-Liquid Separators – Quantifying Separation Performance Part 1. SPE Oil and Gas Facilities, Aug. (22 – 29).
Bothamley, M. 2013. Gas-Liquid Separators – Quantifying Separation Performance Part 2. SPE Oil and Gas Facilities, Oct. (35 – 47)
Bothamley, M. 2013. Gas-Liquid Separators – Quantifying Separation Performance Part 2. SPE Oil and Gas Facilities, Dec. (34 – 47)
Fabian, P., Cusack, R., Hennessey, P., Neuman, M. 1993. Demystifying the Selection of Mist Eliminators, Part 1: The Basics.Chem Eng 11 (11): 148 – 156.
In the October 2014 Tip of the Month (TOTM), we demonstrated that Gas-Oil-Ratio (GOR) has a large impact on the capacity of crude oil gathering lines. In general as GOR increased the pressure drop increased which lowered the line capacity. In addition, at high pressures and low GOR, pressure drop was lower than the pressure drop for dead oil (solution gas is zero) because the viscosity of live oil is lower than viscosity of dead oil. This effect was bigger for the smaller line diameter.
In this TOTM, we will study the impact of solution gas (RS) on the crude oil properties in the gathering systems for one of the cases presented in the October 2014 TOTM. Specifically, the variation of the crude oil relative density and viscosity with the solution gas (RS) will be studied. Finally, the impact of solution gas (RS) on the oil and gas velocity and pressure drop along a gathering line for nominal pressure of 6900 kPag (1000 psig) and nominal pipe size of 101.6 mm (4 inches) will be demonstrated using multiphase rigorous method from a commercial simulator. The calculated properties, oil and gas velocities and pressure drops are presented in graphical format as a function of the oil stock tank volume flow rate and solution gas, RS.
For clarity, gas-oil-ratio (GOR) is defined as the total volume of gas which comes out of oil at standard conditions divided by the total volume oil at the stock tank conditions. The solution gas (RS) is defined as the volume of gas dissolved in oil divided by the volume of oil (with the same unis as GOR) but at the flowing temperature and pressure.
Case Study
For the purpose of illustration, we considered a case study for transporting a crude oil of relative density of 0.852 (°API = 34.6) at stock tank conditions combined with a gas with relative density of 0.751. The selected GORs were 0 (dead oil), 17.8, 35.6, and 89 Sm3 of gas/STm3 of oil (0, 100, 200, 500 scf/STB). The compositions of oil and gas are presented in Table 1. The oil C6+ was characterized as 30 hypothetical single carbon number (SCN) [1] ranging from SCN6 to SCN35 while the gas C6+ was characterized by 10 hypothetical SCN ranging from SCN6 to SCN15. For details of the SCN components, see Table 3.2 on page 64 of reference [1]. The mole fraction of SCN components were determined by an exponential decay algorithm [2].
Table 1. Feed composition at stock condition
The following assumptions were made:
Steady state conditions
The line is 1.601 km (1 mile) long with nominal size of 101.6 (4 inches), onshore buried line.
Segment lengths and elevation changes are presented in Table 2 and Figure 1. This elevation profile is considered to be approximately equivalent to “rolling” terrain.
Pipeline inside surface roughness of 46 microns (0.046 mm, 0.0018 inch)
Line nominal pressure 6900 kPag (1000 psig)
The feed enters the line at 15.6 ̊C (60 ̊F)
The ground/ambient temperature, is 15.6 ̊C (60 ̊F)
Water cut is 0 (no water in the feed).
Overall heat transfer coefficients of 2.839 W/m2- ̊C (0.5 Btu/hr-ft2- ̊F), for onshore
buried line (minor effect as inlet temperature = ambient ground temperature).
Simulation software ProMax [3] and using the Soave-Redlich-Kwong (SRK) Equation of State [4] for vapor-liquid equilibrium and Beggs-Brill method for two-phase pressure drop calculation [5].
Table 2. Line segment length and elevation change
Figure 1. Gathering line elevation profile
Results and Discussions:
The two phase (oil and gas) flow through the gathering line was simulated by ProMax with SRK EOS for vapor-liquid equilibria and Beggs-Brill for two phase pressure drop calculations. Figures 2A and 2B present the calculated pressure drop per unit length as a function of oil stock tank volume rate and GOR for nominal line diameter of 101.6 mm (4 inches) at nominal line pressure of 6900 kPag (1000 psig) in SI (international) and FPS (Engineering) system of units, respectively. Figures 2A and 2B indicate that as the GOR increases from 0 to 35.7 Sm3/STm3 (0 to 200 scf/STB), the pressure drop decreases but increases with further increase in GOR of 89 Sm3/STm3 (500 scf/STB) and higher.
The impact of RS on the properties of oil is demonstrated in the next section which will explain the causes of pressure drop.
Figure 2A (SI). Variation of pressure drop per unit length with oil stock tank volume rate and GOR at 6900 kPag for 101.6 mm pipe diameter
Figure 2B (FPS). Variation of pressure drop per unit length with oil stock tank volume rate and GOR at 1000 psig for 4 in pipe diameter
Figure 3 presents the bubble point pressure of the feed to the gathering line at 15.6 (60) as a function of solution gas. Figure 3 indicates that for the nominal line pressure of 6900 kPa (1000 psig), the feed is under saturated up to GOR of 51.8 Sm3/STm3 (290.5 scf/STB). For GOR greater than this value, the oil becomes saturated with gas and gas breaks out.
Figure 3. Bubble point pressure of the feed to the gathering line as a function of solution gas at 15.6 (60)
The variation of oil relative density along the line as a function of solution gas (RS), is presented in Figure 4. This figure indicates that as the RS increases, the oil relative density decreases. Figure 5 shows that as the RS increases, the oil viscosity decreases considerably. The reduction of viscosity causes pressure drop to decrease. The simulation results (Figure 3) indicated that for GOR less than 51.8 Sm3/STm3 (290.5 scf/STB), the flow is under saturated single liquid phase; however, for higher GOR the flow becomes saturated two phase (gas and liquid) which causes the pressure drop to increase. The increase in pressure drop due to higher GOR (and higher total flow rate) is more than the decrease in pressure drop due to reduction of oil viscosity as a result of solution gas. The net effect is higher pressure drop compared to dead oil (GOR = 0) pressure drop.
Figure 6 presents the variation of oil and gas velocity for two stock tank oil rate along the gathering line at 6900 kPag for 101.6 mm pipe diameter and GOR of 89.1 Sm3/STm3 (500 scf/STB). Figure 6 indicates that the oil velocity remains constant along the line but the gas velocity increases due to pressure drop in the line.
Figure 7 presents the impact of GOR on pressure drop along the gathering line at 6900 kPag for 101.6 mm pipe diameter. As it can be seen in this figure, for GOR less than 51.8 Sm3/STm3 (290.5 scf/STB) the pressure drop decreases as GOR increases but at higher GOR due to presence of two phase flow, the pressure drop increases. As the GOR increases further, the effect of elevation change diminishes compared to rise of pressure drop due to friction.
Conclusions
The following conclusions can be made based on this case study:
If the oil is under saturated the increase in solution gas (RS), reduces the oil viscosity and causes the pressure drop to decrease. For saturated oil, the increase in GOR changes the single phase liquid flow to two phase gas-liquid flow and causes the pressure drop to increase and overcome the pressure drop reduction due to lower liquid viscosity.
While increasing solution gas (RS) reduces the oil viscosity and relative density they remain almost constant along the line.
While at higher GOR the flow becomes two phase, the pressure drop due to friction becomes dominant and overcomes the elevation changes. This is more pronounced in the longer lines.
While oil velocity remains constant along the line, the gas velocity increases along the line.
Figure 4. Variation of oil relative density with solution gas (RS), Sm3/STm3 (scf/STB), along the gathering line at 6900 kPag for 101.6 mm pipe diameter
Figure 5. Variation of oil viscosity with solution gas (RS), Sm3/STm3 (scf/STB), along the gathering line at 6900 kPag for 101.6 mm pipe diameter
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: Mahmood Moshfeghian
Reference:
Campbell, J.M., Gas Conditioning and Processing, Volume 1: The Basic Principles, 9th Edition, 2nd Printing, Editors Hubbard, R. and Snow–McGregor, K., Campbell Petroleum Series, Norman, Oklahoma, 2014.
Moshfeghian, M., Maddox, R.N., and A.H. Johannes, “Application of Exponential Decay Distribution of C6+ Cut for Lean Natural Gas Phase Envelope,” J. of Chem. Engr. Japan, Vol 39, No 4, pp.375-382 (2006)
ProMax 3.2, Bryan Research and Engineering, Inc., Bryan, Texas, 2014.
Brill, J. P., et al., “Analysis of Two-Phase Tests in Large-Diameter Flow Lines in Prudhoe Bay Field,” SPE Jour, p. 363-78, June 1981.
Figure 6. Variation of oil and gas velocity for two stock tank oil rate along the gathering line at 6900 kPag for 101.6 mm pipe diameter and GOR of 89 Sm3/STm3 (500 scf/STB)
Figure 7. Impact of GOR, Sm3/STm3 (scf/STB) on pressure drop along the gathering line at 6900 kPag for 101.6 mm pipe diameter
The use of multiphase flow systems is common practice in the oil and gas industry. Multiphase flow is often encountered in the well tubing, flow lines and gathering systems. For transport of oil and gas (and water) to downstream processing facilities the preference is normally a single pipeline in which both phases are transported simultaneously for economic reasons. Even in gas pipelines where the gas enters the line as a single phase fluid, condensation of liquids can occur due to pressure and temperature changes along the line.
Modeling and simulation of a multiphase systems, even under steady-state conditions, is complex. There are a few tools designed specifically for modeling and analysis of complex multiphase systems such as PipePhase, PipeSim, OLGA, etc. [1].
In the June 2008 Tip of the Month (TOTM), we demonstrated how general-purpose process simulation programs can be used to simulate gas dominated two-phase pipelines. In the August 2008 TOTM, we discussed the value of the simple Flanigan correlation and how it can be used to model and analyze the behavior of a wet gas transmission pipeline. The results of the Flanigan correlation were compared with more rigorous calculation methods for multiphase pipelines.
In this TOTM, we will study the impact of gas-oil ratio (GOR) on pressure drop in crude oil gathering systems. Specifically, pressure drop along a gathering line for nominal pressures of 690, 3450, and 6900 kPag (100, 500, and 1000 psig) and nominal pipe size of 101.6 and 152.4 mm (4 and 6 inches) was calculated using multiphase rigorous method from commercial simulator. The calculated pressure drops are presented in graphical format as a function of the oil stock tank volume flow rate and GOR. Variation of thermo physical properties was considered.
Case Study
For the purpose of illustration, we considered a case study for transporting a crude oil of relative density of 0.852 (°API = 34.6) at stock tank condition combined with a gas with relative density of 0.751. The selected GORs were 0 (dead oil), 17.8, 356.5, and 891.3 Sm3 of gas/STm3 of oil (0, 100, 2000, and 5000 scf/STB). The compositions of oil and gas are presented in Table 1. The oil C6+ was characterized as 30 single carbon number (SCN) [2] ranging from SCN6 to SCN35 while the gas C6+ was characterized by 10 SCN ranging from SCN6 to SCN15. For details of the SCN components, see Table 3.2 on page 64 of reference [2]. The mole fraction of SCN components were determined by an exponential decay algorithm [3].
Table 1. Feed composition at stock condition
The following assumptions were made:
Steady state conditions
The line is 1.601 km (1 mile) long with nominal size of 101.6 and 152.4mm (4 and 6 inches), onshore buried line.
Segment lengths and elevation changes are presented in Table 2. This elevation profile is considered to be approximately equivalent to “rolling” terrain.
Pipeline inside surface roughness of 46 microns (0.046 mm, 0.0018 inch)
Line nominal pressure 690, 3450, and 6900 kPag (100, 500, and 1000 psig)
The feed enters the line at 15.6 ˚C and (60 ˚F)
The ground/ambient temperature, is 15.6 ˚C and (60 ˚F)
Water cut is 0 (no water in the feed).
Overall heat transfer coefficients of 2.839 W/m2-˚C (0.5 Btu/hr-ft2-˚F), for onshore buried line (minor effect as inlet temperature = ambient ground temperature).
Simulation software ProMax [4] and using the Soave-Redlich-Kwong (SRK) Equation of State [5] for vapor-liquid equilibrium and Beggs-Brill method for two-phase pressure drop calculation [6].
Table 2. Line segment length and elevation change
Results and Discussions:
The two phase (oil and gas) flow through the gathering line was simulated by ProMax with SRK EOS for vapor-liquid equilibria and Beggs-Brill for two phase pressure drop calculations. Figures 1A and 1B present the calculated pressure drop per unit length as a function of oil stock tank volume rate and GOR for nominal line diameter of 101.6 mm (4 inches) at nominal line pressure of 690 kPag (100 psig) in SI (international) and FPS (Engineering) system of units, respectively. Figures 1A and 1B indicate that as the GOR increases from 0 to 891 Sm3/STm3 (0 to 5000 scf/STB), the pressure drop increases considerably. Consequently, as the GOR increases, the line capacity decreases.
Figures 2A, 2B, 3A, and 3B present the results for the same line size but at nominal pressures of 3445 and 6900 kPag (500 and 1000 psig), respectively. Contrary to Figure 1, Figures 2 and 3 indicate that at these higher pressures as the GOR increases, the pressure drop decreases for low GOR value. However, for further increase of GOR the pressure drop increases considerably.
Similar calculations were repeated for another line with nominal pipe size of 152.4 mm (6 inches) and the simulation results are presented in Figures 4 through 6. Figures 4 through 6 also demonstrate the same impact of GOR on the pressure drop, at higher pressures and low GOR, the pressure drop decreases. However, the impact of low GOR at higher pressures is less compared to the smaller line diameter.
Figure 1A (SI). Variation of pressure drop per unit length with oil stock tank volume rate and GOR at 690 kPag for 101.6 mm pipe diameter
Figure 1B (FPS). Variation of pressure drop per unit length with oil stock tank volume rate and GOR at 100 psig for 4 in pipe diameter
Figure 2A (SI). Variation of pressure drop per unit length with oil stock tank volume rate and GOR at 3445 kPag for 101.6 mm pipe diameter
Figure 2B (FPS). Variation of pressure drop per unit length with oil stock tank volume rate and GOR at 500 psig for 4 in pipe diameter
Figure 3A (SI). Variation of pressure drop per unit length with oil stock tank volume rate and GOR at 6900 kPag for 101.6 mm pipe diameter
Figure 3B (FPS). Variation of pressure drop per unit length with oil stock tank volume rate and GOR at 1000 psig for 4 in pipe diameter
Conclusions
The following conclusions can be made based on this case study:
The GOR has a large impact on the capacity of crude oil gathering lines. In general as GOR increases the pressure drop increases which lowers the line capacity.
At high pressures and low GOR, pressure drop is lower than the pressure drop for dead oil (solution gas is zero) because the viscosity of live oil is lower than viscosity of dead oil. This effect is bigger for the smaller line diameter.
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: Mahmood Moshfeghian
Reference:
Ellul, I. R., Saether, G. and Shippen, M. E., “The Modeling of Multiphase Systems under Steady-State and Transient Conditions – A Tutorial,” The Proceeding of Pipeline Simulation Interest Group, Paper PSIG 0403, Palm Spring, California, 2004.
Campbell, J.M., Gas Conditioning and Processing, Volume 1: The Basic Principles, 9th Edition, 2nd Printing, Editors Hubbard, R. and Snow–McGregor, K., Campbell Petroleum Series, Norman, Oklahoma, 2014.
Moshfeghian, M., Maddox, R.N., and A.H. Johannes, “Application of Exponential Decay Distribution of C6+ Cut for Lean Natural Gas Phase Envelope,” J. of Chem. Engr. Japan, Vol 39, No 4, pp.375-382 (2006)
ProMax 3.2, Bryan Research and Engineering, Inc., Bryan, Texas, 2014.
In the October, November, December 2007 and February 2014 Tips of the Month (TOTM), we studied in detail the water phase behaviors of sweet and sour natural gases and acid gas systems. We also evaluated the accuracy of different methods for estimating the water content of sour natural gas and acid gas systems.
The water vapor content of natural gases in equilibrium with water is commonly estimated from Figure 6.1 of Campbell book [1] or Figure 20.4 of Gas Processors and Suppliers Association, including corrections for the molecular weight (relative density) of gas and salinity of water [2].
In this TOTM, we will present two new correlations for estimating the water content of lean and sweet natural gases. The performance of the proposed correlations will be compared with the rigorous simulation and shortcut method software and other correlations.
Low Pressure System
At low pressure conditions, less than 700 kPa (100 psia), the mole fraction of water in the gas phase can be estimated by dividing water vapor pressure, PV, at the specified temperature, T, by the system pressure, P. The vapor pressure of pure water, from 0 to 360, (32 to 680) can be calculated by the following relation [3].
Where:
The critical temperature, TC = 647.096 K and critical pressure, PC = 22.064 MPa, T in K, and PV in MPa, and
Knowing one kmole of water = 18 kg (lbmole=18 lbm) and one kmole of gases occupy 23.64 Sm3 at standard condition of 15 and 101.3 kPa (one lbmole of gases occupy 379.5 SCF at standard condition of 60 and 14.7 psia), the water content is calculated by
Moderate to High Pressure System
For pressures higher than 700 kPa (100 psia), we propose a correlation similar to equation 6-276 in Chapter 6 of Standard Petroleum Handbook [4] as follows:
Reference [4] presents the tabular values of A and B as a function of temperature. In this work, the temperature dependency of A and B in equation 3 is presented in the form of Gaussian Model.
Where:
A temperature range of -40 to 100 (-40 to 212) and pressure range of 6.8 to 680 atm have been considered. For the purpose of higher accuracy, the parameters in equations 4G are regressed and presented in Table 1 for 5 different temperature intervals, for SI (System International) and FPS (Foot-pound-Second) system of units. The temperature range in the 5th row in each system of units is used more frequently. The proposed correlations are suitable for spreadsheet calculations.
Table 1. The parameters for equation 4G (Gaussian Model).
Alternatively, the temperature dependency of parameters A and B in equation 3 can also be presented in the form of Polynomial Model.
Similarly, the correlation parameters of equations 4P are presented in Table 2.
The performance of the proposed correlation was evaluated against the water content of a lean sweet natural gas with a relative density (specific gravity) of 0.6 calculated by SRK equation of state of ProMax software [5]. The water saturator tool of ProMax was utilized. Figure 1A (SI) and Figure 1B (FPS) present the comparison between the proposed correlation (Eqs. 3 and 4G) results (solid curves) and the corresponding results from ProMax designated by geometric symbols. Figure 1 indicates there is relatively good agreement between the proposed correlation and ProMax. Large deviations are observed for temperatures below -20 (-4). Almost in all conditions, except at very high temperature the proposed correlation is more conservative (over predicting) than the ProMax.
Table 2. The parameters for equation 4P (Polynomial Model).
Evaluation of the Proposed Correlation
The performance of the proposed correlation was also evaluated against the water content of a lean sweet natural gas predicted by GCAP software [6]. The water content of GCAP is based on Figure 6.1 of Campbell book [1]. Figure 2A (SI) and Figure 2B (FPS) present the comparison between the proposed correlation (Eqs. 3 and 4G) results (solid curves) and the corresponding results from GCAP designated by geometric symbols. Figure 2 indicates there is better agreement between the proposed correlation and GCAP compared to ProMax. Large deviations are observed for temperatures below -20 (-4). Almost in all conditions, except at very high temperature, the proposed correlation is slightly more conservative (over predicting) than the GCAP.
Figures 1 and 2 indicate that in the temperature range of 0 to 70 (32 to 158 ) excellent agreement is observed between results of equation 3 and both software. Figure 2 also indicates that the GCAP results for 170 atm at very low temperature is inconsistent with other pressure results of GCAP.
When equation 4P and the parameters presented in Table 2 were used instead of equation 4G, similar quality of results was obtained.
Figure 1A (SI). Comparison of results between the proposed correlation (Eq 3) and ProMax
Figure 1B (FPS). Comparison of results between the proposed correlation (Eq 3) and ProMax
Figure 2A (SI). Comparison of results between the proposed correlation (Eq 3) and GCAP
Figure 2B (FPS). Comparison of results between the proposed correlation (Eq 3) and GCAP
Bukacek Correlation
Bukacek [7] suggested a relatively simple correlation for the water content of lean sweet gas as follows:
where T is in °F.
This correlation is reported to be accurate for temperatures between 15.5 and 238°C (60 and 460°F) and for pressure from 0.105 to 69.97 MPa (15 to 10,000 psia). The pair of equations in this correlation is simple in appearance. The added complexity that is missing is that it requires an accurate estimate of the vapor pressure of pure water. In this study, we have used equation 1 for water vapor pressure.
The performance of the proposed correlation was also evaluated against the Bukacek’s Correlation coupled with equation 1 for water vapor pressure. Figure 3A (SI) and Figure 3B (FPS) present the comparison between the proposed correlation (Eqs. 3 and 4G) results (solid curves) and the corresponding results from Bukacek’s correlation designated by geometric symbols. Figure 3 indicates there is an excellent agreement between the proposed correlation and Bukacek’s correlation.
Conclusions
The following conclusions can be made regarding the proposed correlation:
A relatively simple correlation for predicting the water content of lean sweet natural gas is presented that can be used for spreadsheet calculation.
Based on Figures 1 and 2, the agreement between the predicted water content by the proposed correlation (Eqs. 3 and 4G or Eqs. 3 and 4P) and those predicted by ProMax and GCAP software is relatively good. The agreement deteriorates at temperatures below -20 (-4).
In general, the estimated water content by the proposed correlation is conservative compared to ProMax and GCAP.
A better agreement between the proposed correlation and GCAP compared to ProMax is observed.
Excellent agreement is observed between the proposed and the Bukacek’s correlations.
The GCAP results for pressure of 170 atm at low temperatures are inconsistent with respect to GCAP results at other pressure.
The proposed correlation is easy and suitable for hand or spreadsheet calculations.
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: Dr. Mahmood Moshfeghian
Reference:
Campbell, J.M., Gas Conditioning and Processing, Volume 1: The Basic Principles, 9th Edition, 2nd Printing, Editors Hubbard, R. and Snow–McGregor, K., Campbell Petroleum Series, Norman, Oklahoma, 2014.
GPSA Engineering Data Book, Section 20, Volume 2, 13th Edition, Gas Processors and Suppliers Association, Tulsa, Oklahoma, 2012.
Wagner, W. and Pruss, A., J. Phys. Chem. Reference Data, 22, 783–787, 1993.
Standard Handbook of Petroleum, Natural Gas Engineering volume 2, Lyons, W. C., Editor, Gulf Professional Publishing, Houston, Texas, 1996
ProMax 3.2, Bryan Research and Engineering, Inc., Bryan, Texas, 2014.
GCAP 9.1, Gas Conditioning and Processing, PetroSkills/Campbell, Norman, Oklahoma, 2014
Bukacek, R.F., “Equilibrium Moisture Content of Natural Gases” Research Bulletin IGT, Chicago, vol 8, 198-200, 1959.
Figure 3A (SI). Comparison of results between the proposed correlation (Eq 3) and Bukacek’s Correlation
Figure 3A (FPS). Comparison of results between the proposed correlation (Eq 3) and Bukacek’s Correlation
The first pillar of Risk Based Process Safety Management is “Commitment to Process Safety.” A formalized mentoring system can ensure workforce involvement, compliance with company and regulatory requirements, increase the competency of personnel and enhance the process safety culture of the entire organization. Within this element there are several essential features that lead to a more effective process safety culture.
Providing strong leadership is critical for any organization that strives to manage the risk associated with the activities associated with process safety. Leadership is a skill that is not necessarily intuitive to managers and mentors. Leadership is a skill that can be learned.
In this Tip of the Month (TOTM), we explore process safety leadership.
This TOTM is part of a paper that was developed by John M. Campbell (JMC) Instructor/Consultants Clyde Young and John Kanengieter for presentation at the Center for Chemical Process Safety (CCPS) 9th Global Conference on Process Safety [1].
Over the last several years, significant resources have been devoted to examining the issue of process safety culture, and strong leadership has been cited as a key element to enhance a process safety culture. Study of major accidents within the oil, gas, chemical and allied industries have found that the safety culture of organizations is often proposed as a contributing factor, and development of a culture of process safety as the solution. Presentations at symposia and conferences point to enhancing culture and providing leadership as necessary to address breakdowns in process safety management systems.
The first pillar of the Center for Process Safety (CCPS) Guidelines for Risk Based Process Safety (RBPS) is “Commit to Process Safety.” Supporting this pillar is the element “Process Safety Culture”, which is defined as, “ the combination of group values and behaviors that determine the manner in which process safety is managed.” One of the four essential features of process safety culture is “strong leadership.”
Leadership
What is “leadership”? It has been described as “organizing or influencing a group to achieve a common goal”. This would intimate that the leader is a boss or manager, but is a manager necessarily an effective leader? There is considerable literature about leadership. This literature includes quotes about leadership, how to find “natural” leaders and how to develop leadership skills. There are workshops about leadership and even university degrees in leadership. If there are so many resources dedicated toward understanding and teaching leadership, why is leadership listed as something that needs to be enhanced in symposia, papers and reports that deal with managing process safety in high hazard activities? It may be because leadership and culture are considered human factors. When associated with process safety, they are known as factors that can lead to loss of the standards of consistently reliable human performance. These standards are relied on as part of an organization’s defenses against process safety incidents.
Every person working in the oil, gas, chemical and allied industries should perform their jobs under the guidance of a process safety management system. CCPS 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.” Producing specific results in a consistent manner all the time requires that all personnel perform at a high level. If culture is defined simply as “the way we do things around here”, this is influenced greatly by leadership. But leadership doesn’t reside in the role of one person. Leadership needs to be imbedded within the organization with every person. This is a skill that can be learned by all and dependence on one individual with authority or one person who might be considered a “natural” leader can lead to failure of the system.
When teams cease to function effectively and breakdowns are discovered in the system to manage process safety, it is highly likely that there is a breakdown in goals, roles and expectations in the team.
Every person working in or supporting the operation of a high hazard process must be able to recite and explain the goal of every team they work with. There should never be in any doubt what every team’s goal is.
Because we may and probably do work on several teams, it is vital that we are clear of our role on each team. What is my primary function to support achieving the goal? There should never be in any doubt what every person’s role is on that team.
Does each person on the team have a concisely developed set of expectations for individual and team behavior? Is there some way for the team to check that the expectations are being met? What is the procedure for addressing deviation from expectations?
A PetroSkills client recently asked for a one-day Overview of Risk Based Process Safety Management for Upper Level Management. Four sessions of this overview have been delivered around the world to the business unit managers and their direct (team members) reports?. Leadership and working as effective teams are two elements of the session that address the issue of process safety culture in this client’s operations.
A key learning point offered by participants is that a clear understanding of goals, roles and expectations comes from leadership and exhibiting the appropriate leadership role. Many leave the session with an action item to conduct team work sessions to establish/reaffirm goals, roles and expectations.
If you would like a copy of the paper presented at the CCPS 9th Global Congress, contact PetroSkills.
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
PetroSkills Instructor/Consultant
Reference:
1. Clyde Young and John Kanengieter, “Process Safety Management Mentoring: Developing Leaders”, The (CCPS) 9th Global Conference on Process Safety, the Center for Chemical Process Safety , April, 2013.
![Figure 1. Typical process flow diagram for a 2-tower adsorption dehydration system [1]](https://i0.wp.com/www.jmcampbell.com/tip-of-the-month/wp-content/uploads/2015/04/Fig1.png?resize=600%2C375)
![Figure 2. Typical process flow diagram for a 3-tower adsorption dehydration system [1]](https://i0.wp.com/www.jmcampbell.com/tip-of-the-month/wp-content/uploads/2015/04/Fig2.png?resize=600%2C368)
![Figure 3. A generic molecular sieve decline curves [1]](https://i0.wp.com/www.jmcampbell.com/tip-of-the-month/wp-content/uploads/2015/04/Fig3.png?resize=671%2C473)
![Figure 4. Design condition life factor [1]](https://i0.wp.com/www.jmcampbell.com/tip-of-the-month/wp-content/uploads/2015/04/fig41.png?resize=660%2C462)
![Figure 5. Performance test run (PTR) life factor [1]](https://i0.wp.com/www.jmcampbell.com/tip-of-the-month/wp-content/uploads/2015/04/fig5.png?resize=611%2C422)
![Figure 6. Projected life factor (red triangle) running at design conditions [1]](https://i0.wp.com/www.jmcampbell.com/tip-of-the-month/wp-content/uploads/2015/04/fig6.png?resize=631%2C430)
![Figure 7. Projected life factor running at design conditions [1]](https://i0.wp.com/www.jmcampbell.com/tip-of-the-month/wp-content/uploads/2015/04/fig71.png?resize=658%2C461)
![Figure 8. Projected life factor (red triangle) if standby time is used [1]](https://i0.wp.com/www.jmcampbell.com/tip-of-the-month/wp-content/uploads/2015/04/fig81.png?resize=651%2C429)
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![Figure 1. Gas-Liquid Separation Equipment Selection Map [1]](https://i0.wp.com/www.jmcampbell.com/tip-of-the-month/wp-content/uploads/2014/11/fig11.png?resize=554%2C339)
![Figure 2. Troubleshooting Methodology [1]](https://i0.wp.com/www.jmcampbell.com/tip-of-the-month/wp-content/uploads/2014/11/fig2.png?resize=321%2C526)
![Figure 3. Parts of a Conventional Separator [2]](https://i0.wp.com/www.jmcampbell.com/tip-of-the-month/wp-content/uploads/2014/11/fig31.png?resize=604%2C276)
![Figure 4. Effect of feed pipe velocity on liquid entrainment [2]](https://i0.wp.com/www.jmcampbell.com/tip-of-the-month/wp-content/uploads/2014/11/fig41.png?resize=455%2C346)
![Figure 5. Example of liquid entrainment behavior in a gas-liquid system [2]](https://i0.wp.com/www.jmcampbell.com/tip-of-the-month/wp-content/uploads/2014/11/fig51.png?resize=384%2C280)
![Figure 6. Various separation equipment inlet devices [2]](https://i0.wp.com/www.jmcampbell.com/tip-of-the-month/wp-content/uploads/2014/11/fig61.png?resize=593%2C371)
![Figure 8. Ks vs. pressure and droplet size for empty vessels [2]](https://i0.wp.com/www.jmcampbell.com/tip-of-the-month/wp-content/uploads/2014/12/fig8.png?resize=618%2C481)
![Figure 9. Typical mist extractor in a vertical separator [2]](https://i0.wp.com/www.jmcampbell.com/tip-of-the-month/wp-content/uploads/2014/12/fig9.png?resize=447%2C451)
![Figure 10. Mesh pad examples [1]](https://i0.wp.com/www.jmcampbell.com/tip-of-the-month/wp-content/uploads/2014/12/fig10.png?resize=599%2C228)
![Figure 11. Single-pocket vane schematic [2]](https://i0.wp.com/www.jmcampbell.com/tip-of-the-month/wp-content/uploads/2014/12/fig11.png?resize=488%2C201)
