EROSION IN SLUG FLOW X-1 EROSION IN SLUG FLOW Introduction

EROSION IN SLUG FLOW X-1

September 2009

EROSION IN SLUG FLOW

Introduction Oil and natural gas providers supply a large percentage of energy that is used to fuel our civilization’s growth and prosperity. Foremost, petroleum forms the backbone of the struggle to supply the world’s growing transportation necessities. There are many other facets and regions in our daily lives that are all made possible by the efficient and cost effective energy provided by oil and natural gas producers. The impact of oil and gas production runs deep, and can be seen in the residential, commercial, and industrial sectors alike. The global economy practically mimics the state of oil and gas sales. With all of these aspects taken into consideration, it is blatantly obvious of the importance of improving the efficiency of upstream and downstream processes to ensure an equitable energy product for consumers. Through years of research, hard work, and dedication, the oil and gas industry has strived to supply such a product with only a few details left to decipher. One of the final frontiers of exploration for oil and gas producers, that is currently underway to being resolved, is the well known problem identified as erosion. Erosion, for the oil and natural gas industry, is the process of abrasion by which material is worn away from the pipe’s interior surface by impinging sand particles. Erosion occurs in the process of retrieving oil and gas from wells, when sand is produced along with as many as two other phases. The sand becomes entrained in the liquid, gas, or multiphase flow, and can travel through the entire gathering system. This means that every valve, elbow, joint of pipe, and any other fitting in the pipeline will witness particle impingements from the entrained sand in the flow. Erosion is a leading factor in pipeline failures and replacements and is therefore of the upmost importance. Pipe erosion can be severe enough to cause a complete shutdown of systems and create noteworthy economical impacts over the entire production process. The Erosion/Corrosion Research Center (E/CRC), at the University of Tulsa, is dedicated to providing experimental research and modeling regarding the effect of particle erosion in pipelines. It is the goal of E/CRC to provide technical tools which help estimate erosion in pipelines to avoid major failures and provide a life expectancy under known flow conditions and sand production rates. These tools can help maintenance scheduling and even allow for cost to benefit projections for any pipeline in question. The E/CRC has created a powerful tool that uses empirical evidence, erosion models, and developed correlations to predict pipe erosion. That tool is a computer program called Sand Production Pipe Saver (SPPS). The E/CRC and SPPS focus on

X-2 EROSION/CORROSION RESEARCH CENTER

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erosion in geometries such as elbows, sharp bends, contractions, expansions, plugged tees, and tees. These geometries are rigorously studied and straight sections are annulled due to the fact that erosion is assumed negligible in comparison in straight pipe. SPPS has the ability to predict erosion rates for numerous flow situations in every flow regime. Multiphase flow is of tremendous interest to many industries, especially to the oil and gas industry. This is due to the multiphase nature of oil and gas production. A single well can be producing oil, gas, brine, and sand. The production, of any and all the aforementioned phases, leads to many different flow conditions over the whole spectrum of flow patterns. Oil and gas production can include liquids with varying densities and viscosities, high pressure gases, and many variations in sand size and rates. All of these variables need to be accounted for and monitored to ensure proper erosion predictions. This study of multiphase flow presents experimental data gathered for varying flow conditions, fluid viscosities, and sand sizes. The study attempts to develop an accurate depiction of how erosion behaves under slug flow conditions and ultimately provide means of accounting for and predicting erosion in the slug flow regime. More particularly, this study focuses on the slug flow models in SPPS, their improvement, and their further development.

Background

Sand Production Pipe Saver The model that came to be known as Sand Production Pipe Saver (SPPS) was birthed in 1999 when McLaury and Shirazi extended a semi-empirical model for calculating erosion to multiphase flows. SPPS was formed from the ground up from data collected from notable research centers such as Texas A&M University, Harwell, DNV, and of course here at the University of Tulsa from the E/CRC. Jordan (1998), McLaury and Shirazi (1999), Mazumder (2004), and McLaury (2006), Gundameedi (2008), and Rodriguez (2008) have all been key contributors to the progress of extending the model to multiphase flow. Experimental data has been gathered and continues to grow in the effort to validate this model for multiphase flow conditions. The models that are intrinsic to SPPS make it possible to calculate a penetration rate by simply inputting the desired flow conditions. SPPS also has the flexibility to evaluate the flow conditions when the maximum allowable penetration rate is already known. The heart and soul of the SPPS model is the penetration rate, and it is shown by Equation (1) below.


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o

LDrPSM DD

WVFFFFh/

73.1

/= (1)

where: h = penetration rate, [mm/yr] FM = empirical constant related to material hardness FS = empirical sand sharpness factor FP = penetration factor for steel based on 1” pipe diameter, [m/kg] Fr/D = penetration factor for long radius elbows W = sand production rate, [kg/s] VL = characteristic particle impact velocity, [m/s] D = pipe diameter, [any length unit] D0 = reference 1 inch pipe diameter, [same length unit used for D]

All of the factors in the penetration rate calculation are known, either through assumptions or empirical methods, except for the characteristic particle impact velocity VL. The characteristic particle impact velocity must be modeled for each flow regime to properly predict the penetration rate or amount of erosion taking place. Presently the model uses a one dimensional approach to the calculation of VL. A much more detailed explanation of the model is discussed in previous E/CRC Advisory board reports. It is proposed that a two dimensional approach would be more beneficial to the model as a whole, but especially for small particles. Electrical Resistance Probes For this study, Electrical Resistance (ER) probes were employed for metal loss measurements. The probes are manufactured by Cormon Ltd., a British company, and Corrocean, a Norwegian company. The E/CRC has been conducting research with these probes for some time now. Therefore a great deal of the work conducted by the E/CRC relies heavily on the use, accuracy, and precision of ER probes to continue the battle of erosion prediction. The ER probes have substantial compensation to other methods of determining metal loss. Unlike metal coupons or samples, ER probes are much more advantageous. ER probes do not have to be installed and removed previously to and following the experiment to determine mass loss. ER probes can be left in the experimental apparatus to ensure that correct placement and time are not issues.

The ER probes output real-time metal loss and temperature readings to allow measurements and calculations to be performed in a much timelier manner, unlike metal samples that need to be weighed and installed, then retrieved and weighed again to


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determine a mass loss. The ER probes exhibit extremely high sensitivity that allows for metal loss measurements on the order of nanometers. Studies have been conducted that formally evaluate the performance of the ER probes. The studies were carried out by Hedges and Bodington (2004) and Evans et al. (2004). There has been ER probe evaluations performed by the University of Tulsa Sand Management Projects (TUSMP), by Madhu (2006) and Antezana (2004). The most recent studies conducted by the research centers here at the University of Tulsa were performed by Gundameedi (2008) and Rodriguez (2008). Projects have been sponsored by many different companies throughout the inception of the E/CRC and TUSMP. Of those companies, British Petroleum (BP) sponsored studies that included the testing of hardware and software to determine the proper candidate for accurately measuring erosion and corrosion in flow lines. Hedges and Bodington (2004) performed sponsored studies that concentrated efforts to establish what particular type of equipment could sufficiently acquire data under given test conditions. Needless to say, there was a large quantity of tools available to attain the data, but all methods needed to be evaluated and compared. The methods that were tested and compared in the study include the following: an acoustic sand detector, an ultrasonic sand detector, a standard ultrasonic probe, a high sensitivity ultrasonic probe, a flexible ultrasonic mat, and last but not least ER probes. Hedges and Bodington performed multiphase flow experiments that involved high pressures with varying sand size. The study concluded that out of all the previously mentioned equipment that was investigated, the acoustic sand detector and the ER probe were the only finalists in the race to provide acceptable results for erosion measurements. As with most things in the world around us, there are drawbacks to nearly any process or mode of measurement. Hedges and Bodington report that the ER probe’s primary disadvantage lies solely on the fact that there is no clear distinction that the output measurements are caused by erosion, corrosion, or the synergistic effect.

Antezana (2004) conducted studies for the E/CRC in multiphase annular flows that focused on the effects of probe location relative to the metal loss measured by the probe. ER probes were the primary means of data acquisition, which led to another key objective involved in the study. Assess the sensitivity of the ER probes. This, coupled with the effect of probe location, proved to be an interesting study. The test matrix for the experiments encompassed varying sand rates from 1 to 60 grams per minute of sand that possessed a mean particle diameter of 150 microns. A key discovery to determining the sensitivity was observed. It was detected that there is an inherent noise output range for every ER probe. Several conclusions were drawn from the study. It was found that the effect on probe


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location was noticeable. The resulting consequence was that the probes downstream of a vertical section in the system have a greater erosion ratio than probes downstream of a horizontal section in the system. Antezana (2004) argued that this is a result of dissimilarities in the distribution of the sand inside the pipe between the different flow patterns. Finally, Antezana (2004) estimated a range of ±25 nanometers for the noise output range of the ER probes. The evaluation of the ER probes as effective tools for measuring metal loss was proven successful yet again.

The next scientific exploration that was reviewed is yet another BP sponsored study accomplished by Evans et al. (2004), which travels further into the use of ER probes for measuring erosion and corrosion. The work focuses on the evaluation of three different erosion-corrosion inhibitors while imitating high gas production conditions. ER probes were the primary mode of data acquisition. Three different types of probe materials were tested. Those three materials included: regular carbon steel, 13Cr steel, and a 25Cr duplex stainless steel. The study subsequently revealed properties of the different ER probe materials in addition to the inhibitor efficiency. Of the findings concerning the ER Probes, two conclusions were met. The data collected for the 25Cr duplex stainless steel ER probe yielded no suggestion of the combined effect of erosion-corrosion, but revealed only metal loss caused by erosion. The metal loss rates sampled from the regular carbon steel and 13Cr steel probes were higher than the 25Cr duplex stainless steel ER probe. In fact, the regular carbon steel and 13Cr steel produced a larger metal loss rate than the pure erosion and pure corrosion components combined. The probes showed a greater loss rate when both the erosion and corrosion components are combined in the flow than if the loss rates are summed from both the pure erosion and pure corrosion. The phenomena found to be occuring is known as the synergistic effect. Yet another study was performed which relied on the use of ER Probes. Pyboyina (2006) used ER probes to predict sand rates in primarily single-phase gas flows while still including multiphase annular flows. Not only did Pyboyina run real world experiments, but Computational Fluid Dynamics (CFD) was used to compare the prediction of sand rate. The conclusions gathered from the results include peculiar findings in both single phase-gas flows and in multiphase annular flows. For the tee geometry, under single-phase gas only experiments, it was observed that lower sand concentrations produced more erosion than higher sand concentrations. The mechanism behind this phenomenon is the particle interactions with the gas flow, which actually creates a “sand shield” on the face of the probe protecting it from any high energy impacts and therefore decreasing measured erosion. The multiphase annular flow experiments gave way to another conclusion, which states that


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erosion in annular flow decreases as the liquid flow rate increases. The liquid film on the outer diameter of the pipe acts like a protective barrier by slowing down impinging particles. The two most current works performed here at The University of Tulsa’s research centers include Gundameedi (2008) and Rodriguez (2008). Both researchers utilized the Boom Loop at North Campus and concentrated their studies in slug and annular flow regimes in the 3” test section. This study is a continuation of their research in slug flow and the further development of the slug flow models in SPPS. Gundameedi (2008) set out to analyze the performance of ER probes and acoustic monitors in slug flow. The study was funded by Petrobras, a member company of E/CRC. The range of flow conditions that were used by Gundameedi (2008) is the same for this study. Gundameedi (2008) focused on data acquisition for the angle-head probe in a straight section of pipe in the test section, as well as a Clamp-On sand monitor located on the first elbow of the test section. The procedure used varies slightly from the procedure of this study. The sand concentration was incrementally increased to a final desired value, while the response of both the angle-head probe and the acoustic sand monitor was observed during the tests. The objective was to understand the behavior of the ER probe and acoustic monitor under the varying sand rates, and improve the slug flow models from the results. Contribution to the angle-head and elbow models were made. The surface area, in the erosion calculation, was modified for the elbow model which improved the accuracy of its predictions. It was concluded that the results from the acoustic monitor supported the results from the ER probe. Rodriguez (2008) had goals similar to the objectives of this study. Angle-head and flat-head probes were used to measure the erosion in a straight section of pipe and in an elbow respectively. Unlike Gundameedi (2008), who focused on erosion measured by an angle-head probe and an acoustic sand monitor, Rodriguez (2008) collected data in the elbow with a flat-head probe oriented at 45 degrees from parallel to the incoming flow in addition to the angle-head probe in a straight section of pipe. The flow conditions that were used in the study fall into the slug flow region and cascade over onto the transition to the annular flow regime. The liquid viscosity was varied from 1 cP to 40 cP, and particle sizes used range from 20 microns to 300 microns. Rodriguez (2008) developed the concept of stagnation length distribution for the slug flow models. It was determined that the slug body should be divided into 500 increments to accurately depict the impacting velocity of particles in the slug flow models. Further explanation can be found in the results section. It was concluded that the models do not accurately predict small particle erosion. The results of the study serve as a preliminary mode of evaluation for the content of this research. Data from Rodriguez (2008) is to be built on by this research to improve the database of erosion results at E/CRC.


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The review of all the above mentioned studies reveals that ER probes were a crucial element to each and every one of the experiments. Not only were the ER probes involved in the rigorous testing schemes associated with the studies, but they where also the focal point of data collection. This forced the ER probes into the lime light, and open to scrutiny from many researchers. Some of the studies evaluated the probes themselves while other studies evaluated metal loss rates using the ER probe’s output. The ER probes fared well in comparison to the other modes of erosion measurement though. The successful progression of ER probes is apparent through each of the trials put forth by fellow scientists that have been curious with regard to the abilities possessed by the probes. The previous work conducted in this area has time and time again proven that ER probes collect relatively accurate and precise data, and they are suitable components to use as the basis of erosion monitoring.

Objectives and Approach

Objectives of Work

SPPS is a very powerful tool provided by the E/CRC that attempts to predict the very nature of physically occurring processes involving fluid flow. The universe is guided by the fundamental laws of physics, and the SPPS model is merely trying to mimic those interactions to predict erosion. SPPS has the ability to be tweaked and improved to capture the complete spectrum of phenomena that is transpiring in all of the flow regimes. The task is easier said than done, but the model has already traversed many obstacles and continues to become better equipped to handle any flow condition. The models in SPPS have been extended to predict erosion in multiphase flow conditions in addition to single-phase flows. The E/CRC has collected some degree of data for slug flow, but more is required for further development of the models. Through the years the E/CRC has compiled a data bank for erosion from previous studies including Bourgoyne (1989), Salama (1998), Mazumder (2004), Antezana (2004), Madhu (2006), Nuguri (2006), Gundameedi (2008), and Rodriguez (2008). The cache of data is intended to inspect previous erosion experiments to determine specific areas that require additional research for proper investigation.

The compilation of data suggests that high liquid flow rates and large diameter pipes necessitate further experimentation. Therefore, the large outdoor boom loop at the University of Tulsa’s North Campus will be utilized. The data bank also lacks some information taking place in the slug flow regime, in particular, small particle erosion in slug


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flow. Therefore more tests will be conducted of this nature to ensure a robust foundation that the model can further build upon.

The E/CRC is dedicated to the satisfaction of the oil and gas companies who support them and their efforts in the quest for a deeper understanding of erosion in pipelines. SPPS is the tool that is rewarded to the member companies for their support. The E/CRC regularly searches for possible improvements that can be made to SPPS in any and all aspects of its operation. The culmination of this study’s research, testing, and data analysis will contribute to the improvement of SPPS and its extension to erosion prediction in multiphase flow. The primary objectives of the research entail two components. Both of which are constituents for the other’s completion. The first objective is the enhancement of the general slug flow erosion predictions for the model. The second objective is to increase the accuracy of the predictions for small particles in slug flow. This study will attempt to complete both of the stated goals, while also conducting a sensitivity analysis as to the effects of changing the model. The investigation will determine the precision and accuracy of the penetration rate when the model is tweaked, and compare that to previous results obtained by an earlier version of the model. The computational expenditure needed to run the model will be assessed, so the appropriate meeting grounds between accuracy and the amount of time required to perform the calculations of the model can both be satisfied to a reasonable degree.

Approach

The approach that is best suited for this study, to achieve the mentioned objectives, relies on the use of the large scale multiphase flow loop located at the University of Tulsa’s North Campus that is known as the Boom Loop. The flow loop was developed and constructed by the E/CRC and TUSMP. This loop is the largest and most powerful flow loop available for use with either program. It was designed to produce multiphase flows under many flow conditions. The loop can generate many different flow patterns for different areas of research. This study will more specifically make use of its ability to spawn slug flow.

Electrical Resistance (ER) probes will be the means of detecting, quantifying, and recording the solid particle erosion that is taking place in the system. ER probes have been used in previous studies with commendable results. ER probes have the ability to measure extremely small rates of erosion and corrosion on the order of nanometers. For this reason, ER probes have been selected as the primary mode of data acquisition. ER probes are used extensively at E/CRC for determining metal loss. There are two types of ER probes, which are the angle-head and flat-head probes. Angle-head probes are employed in straight


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sections of pipe and protrude into the flow. Flat-head probes are utilized in bends and are placed flush with the wall. Figure 1 below shows pictures of the two types of probes. The picture of the angle-head probe is located on the left and the picture of the flat-head probe is located on the right.

Figure 1. Angle-Head and Flat-Head ER Probes

The guiding principal that gives the ER probes their abilities is, as their name infers,

electrical resistance. Metal loss, by erosion and/or corrosion, is detected by the change in resistance of an electrode. There is an exposed electrode, known as the working electrode, that is placed in the flow and a reference electrode that is protected from exterior elements inside the probe. The resistance of the working electrode changes when it is eroded or corroded. The resistance is then compared to that of the reference electrode that has not been affected by either erosion or corrosion. The amount of metal loss that has occurred on the working electrode in the flow can then be calculated by the comparison to the reference electrode. This metal loss, combined with known sand rates, provides the penetration rate for a particular flow condition. The penetration rate from the ER probe is then compared to the penetration rate calculated by SPPS to validate the model for the particular case.

In the oil field, wellbores produce entrained sand that can take the form of almost any shape, size, and sharpness. The resulting effect, for this study, gives way to the variation of sand type and size in the experimental modus operandi. Several sand types will be used over the entire test matrix to provide insight as to the effect of particle size on erosion. The different sand types have varying physical characteristics which do not stop with only the size. Particle shape, sharpness, and hardness are big contributors to the overall erosion that can be caused by an impingement. Two sand particles could have the same mean diameter


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with varying sharpness, and cause a completely different amount of erosion under the same flow conditions. Research at the E/CRC has been conducted to further determine sand properties that affect erosion, and among them is the sharpness of a particle. Erosion is known to increase as the sharpness of the particle increases. As seen in Equation (1), SPPS takes this aspect into consideration by the use of a sharpness factor in the erosion calculations. Therefore these variables need to be assessed and accounted for experimentally. As a result, this particular study will involve several different sand types.

There are three sand types that are explored in this study. Each type has been properly examined to determine the average particle size and overall range of particle diameters. This was accomplished by the implementation of sand sieves of varying screen size. The smallest particle size that is investigated in this study is Silica Flour. The Silica Flour has an average sand size of 20 microns or micrometers, and is classified as sharp. The mid-sized particle in the study is Oklahoma #1. The average particle size for this sand is 150 microns, and is classified as semi-rounded. The largest particle size that will be used is California Silica sand mesh 60. The average particle size for this sand is 300 microns, and is classified as sharp. The use of these three different sand types provides an all encompassing view into the effect of particle properties on erosion.

Fluid viscosity is another chief aspect of this study. Viscosity is important to many fluid flow calculations, and it can even render the physical effects on the flow itself. Oil and gas production generate both liquids and gases over a broad range of viscosities. The liquids and gases extracted from producing wells are not homogeneous, but are mixtures that are comprised of many elements with different viscosities. In any type of flow, varying the viscosity of the fluid can produce dramatic consequences throughout the entire system. The viscous effects of fluids contribute to such things as the amount of frictional losses experienced, the flow pattern observed, all the way to the erosion pattern and penetration intensity taking place. Therefore a firm understanding is needed regarding the effect of viscosity on erosion.

There are three different liquid viscosities that are examined in this study. The three viscosities used are as follows. The lowest viscosity in the experimental range is arguably the most important viscosity in fluid flow. That viscosity is 1 cP, which is the viscosity of water. Processes in every realm of industry rely on fluids of this viscosity. Therefore it is essential to have a firm understanding of the effects of fluids with a viscosity of 1 cP. This work will further explore increasing viscosities much greater than that of water. In fact, 20 to 40 times greater. The other viscosities include 20 cP and 40 cP. Oil and gas providers are no strangers to high viscosity flows. Experimentation requiring higher liquid viscosities is achieved by using long chain polymers known as thickeners. The thickener used is


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Carboxymethylcellulose (CMC). The CMC is added to water to increase its viscosity without encouraging gelling. The CMC achieves an increased homogeneous viscosity throughout the liquid. Proper results on the effect of viscosity can then be obtained for the study.

Description of the Facility

Description of experimental facility

The Boom Loop at the University of Tulsa’s North Campus was crucial to the completion of the experiments for this project. The key components of the flow loop itself are the two test sections, the mixing tank (or slurry tank), the collecting tank, the liquid side injection point, and the gas side injection point. Further detail of the flow loop is explained later on, and a schematic of the flow loop is included below in Figure 2.

Figure 2. Experimental Schematic of the Boom Loop

V21

Gas

Liquid

Slurry Tank

Stirrer

Collecting Tank

Gas

Gas & Liquid

Pump1a)

Compressors

V11 Gas & Liquid

Flow meter

V12

V1

V22

Pump1b)

2

Boom

Test Sections

Sand Monitor

Angle Head ER Probe (used 3 in diameter pipes or higher)

1 3

Pump2a)

Pump2b)

ER Probes


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The flow loop possesses the ability to either re-circulate the liquid and entrained sand particles or provide a once-through method for the tests. The recirculation method is better suited for high liquid velocities, while the once-through method is better apt for lower liquid velocities and gas only flows. For this particular study, the recirculation method was used due to the relatively higher liquid velocities involved in slug flow. The boom is also adjustable for any degree of inclination between horizontal and vertical. This work only focuses on horizontal flow, and does not place emphasis on any other degree of inclination. The Boom Loop is powered primarily by pneumatics. There are two independent Ingersol Rand diesel compressors mounted on trailers that each yields a maximum gas flow rate of 400 standard cubic feet per minute. The two compressors serve a single purpose. They are the solitary mode of providing the gas flow rates to the actual test section. The compressors are configured in parallel so their maximum flow rate is effectively doubled; as opposed to being configured in series where their pressure would be doubled. There is yet another compressor that is a Caterpillar Sullair that yields a maximum gas flow rate of 375 cubic feet per minute. The primary function of this compressor is to provide a sufficient amount of pressurized gas which drives the principal set of pumps for the flow loop. The Boom Loop is comprised of four pumps. There are two sets of Ingersol Rand ARO pneumatic diaphragm pumps in two different sizes. The smaller size pumps are 2” outlet non-metallic pumps. These pumps are powered by an electric compressor that provides a 100 standard cubic feet per minute flow rate. This compressor is located behind the Boom Loop’s shed and is not shown in the schematic in Figure 1. The larger size pumps are 3” outlet metallic pumps. These pumps are powered by the Caterpillar Sullair diesel compressor mentioned earlier. The correct size of pump to use for experimentation is dependant on application and desired flow conditions. As one would assume, the 3” pumps provide a higher liquid flow rate than that of the 2” pumps. For this study, the 3” pumps were used to reach the desired liquid flow rates that are involved in the promotion of slug flow. The Boom Loop also employs a mixing tank known as the slurry tank. The slurry tank has a maximum capacity of 230 gallons of liquid. The slurry tank houses both the liquid and sand particles that are injected in the flow. The desired sand concentration in the liquid is achieved by adding the proper proportion of sand to the liquid in the tank. The sand particles are suspended in the liquid through the use of a ½ horsepower electric motor with an attached mixing rod that is submerged in the slurry tank. This countermeasure discourages the sand from settling on the bottom of the tank, so the mixture remains homogeneous for the duration of the experiments. The slurry tank is key to the determination of the superficial liquid velocities for the system. Flow meters can not be used


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to find the flow rates of the liquid under slug flow conditions. This is directly caused by the intermittent nature of the slugs. Flow meters will show false or extremely varying rates rendering them useless. This is caused when a liquid slug passes through the meter producing a relatively high flow rate. Afterwards, the gas pocket or Taylor bubble passes through the meter producing a relatively low flow rate. The slug frequency and nature of the meter make it impossible to get an accurate reading. The slurry tank makes it possible to average the liquid flow rate by measuring the time and depth of liquid in the tank. The Boom Loop employs another tank that is crucial to its operation and ability to produce multiphase flows. The tank has three separate orifices in which flow can pass through. There is an inlet to the tank that receives all phases from the test section. There are two outlets on the tank. They are located at the very top, and at the very bottom. The tank separates the phases by allowing the gas to escape from the top of the tank to the atmosphere, while the solid/liquid mixture remains in the bottom of the tank where it can be pumped back into the slurry tank. The collecting tank makes possible the abilities of either using a recirculation method or a once-through method. The Boom Loop tower is where the test sections are located. The boom and two test sections are 18 meters long. The length is necessary to ensure a fully developed flow by the time the flow reaches the ER probes in the elbow and the angle-head probe in the straight section. There are several different sizes of test sections that can be outfitted on the boom, but only two can be situated on it at once. The arrangement of the boom can allow for experiments to be run on different size test sections simultaneously under the same flow rates (not velocities). There are 2”, 3”, and 4” test sections available to the system. This study focuses on the use of the 3” test section only. The arrangement of the boom can be seen in Figure 3.

Figure 3. Test Sections Mounted on the Boom

Upper Test Section

Lower Test Section


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The test sections that are mounted on the boom have strategically located ER probes. There are a combined total of three ER probes available for data acquisition on a single test section. There are two regions of each test section where the three probes can be located. The configuration of the two regions was chosen to best suit the study of erosion on the system. The flow travels through 18 meters of straight pipe before coming to possibly the most commonly used geometry in a pipe line, an elbow. As mentioned earlier, it is crucial to have this amount of uninterrupted length in the test section so the flow can be assumed fully developed. Two probes are located in the elbow that monitor erosion. The first probe, seen by the flow, is oriented at 45 degrees. The second is oriented at 90 degrees. Figure 4 offers a diagram of the probe locations in the elbow geometry for the test section.

Figure 4. Probe Locations for the Elbow Geometry in the Test Section.

The second region, where a single angle-head probe is located, is downstream of the elbow. The flow will pass through the first elbow and then through another elbow. The flow path is then directed towards the tower. Before the flow exits the test section, it will pass through the length of straight section to the angle-head probe. The angle-head probe is placed at the far end of the straight length to promote fully developed flow by the time the flow reaches it. The angle-head probe operates under the same guiding principals as the flat-head probe. The only difference being that the angle-head probe is used to measure erosion in straight pipe sections as opposed to other geometries like the elbow. The angle-head probes are of much significance to industry, particularly in the field where it is common that the only accessible region to place an erosion measuring device is in a straight section of pipe. Readings from the probes located in the elbow section and straight section can be

45

90

ER Probes


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correlated to obtain valid erosion measurements. Figure 5, found below, visually describes the orientation of the angle-head probe in the straight test section.

Figure 5. Angle-Head Probe Orientation

The combination of all the above mentioned components and arrangements makes the Boom Loop possibly the most versatile flow loop that the E/CRC and TUSMP has in their arsenal of experimental setups.

Procedure (Input Data)

The Boom Loop is the largest experimental flow loop available to the research centers at the University of Tulsa. The sheer size and power involved in creating the desired flows in the loop require more than a single researcher to safely accomplish the experiments. Safety is of the greatest concern, and can be viewed as the main reason for multiple researchers. Although, most of the tasks involved with operating the loop can be carried out by a single individual. The startup procedure for the Boom Loop can vary depending on the type of test that is going to be executed. There obviously would not be a need to prep the gas side of the flow loop if a liquid only test was going to be run. This study delves into multiphase flow, so there is always the need to start both the gas and liquid phases for the experiment. The first task to initiate the startup procedure for the loop is to close all valves associated with the injection of both the liquid and gas phases. The gas phase is always begun first. Begin by checking the fluids of the three compressors, including fuel, and fill all to the appropriate level. Decide if the flow conditions require one or two of the large compressors for the gas flow rate to reach the proper superficial gas velocity in the test section. Start the diesel

Probe Face


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compressors to fuel the gas flow rate required to run the large 3” ARO diaphragm pumps, and let them warm up while the rest of the flow loop is prepared for the test. The slurry for the liquid phase is the next step to beginning the test. The amount of setup time revolves around the viscosity of the liquid for the test. If the experiment calls for a 1 cp liquid, then water is merely added to the slurry tank. If the test requires a viscosity greater than one, then the CMC is added accordingly. CMC is mixed with water in a separate bucket by means of a mortar mixer drill. The mixing process of the CMC and water is much easier and efficient in small batches, because it produces fewer clumps and clots that keep the blend from becoming a homogeneous mixture with a constant viscosity. The viscous concoction is thoroughly mixed then placed in the slurry tank. This step is repeated until the desired amount is added to the slurry tank. The slurry tank is then filled to the proper level with water. The level of water required in the slurry tank varies with flow regime and flow conditions. An example of this is portrayed by the difference in fluid levels of the tank between annular flow and slug flow. Slug flow requires more liquid in the tank than annular flow does, because slug flow has a much greater liquid holdup than that of annular flow. In other words, more liquid is in the test section which in turn reduces the amount of liquid in the slurry tank. The compressors are warmed up and ready to be engaged and the slurry tank is mixed and filled accordingly. The next step is to begin the flow of each phase. The gas phase is always begun first. The inlet and outlet conditions of the gas phase are read from temperature and pressure gauges located near the entrance and exit of the flow loop. An electronic flow meter is located just upstream of the adjustment valve, but downstream of the compressors. The reading is taken from the flow meter and the superficial gas velocity is calculated through the use of compressibility relationships which are tabulated in a spreadsheet. The gas flow rate is adjusted by the valve until the proper gas velocity is met. The liquid flow rate is begun after the gas flow rate has been established. It is important for the gas flow rate to be at operating conditions when the liquid flow starts. This is due to the back pressure produced by the gas flow that affects the performance of the pneumatic diaphragm pumps in a non-linear manner. As mentioned previously, the intermittent nature of slug flow and the diaphragm pumps renders digital flow meters useless for this application. Therefore a somewhat unconventional approach, by today’s technologically advanced standards, is taken. The liquid flow rate is calculated by determining the decrease of the liquid volume in the tank and the amount of time it took for that decrease. The dimensions of the tank are known, so the internal volume per unit length can be found. Split times are taken for each unit length the liquid level has dropped. The split times are then averaged to increase the overall accuracy of the measurement. Armed


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with the average time per unit length decrease in volume and the volume per unit length, the liquid flow rate is easily calculated. With the liquid flow rate and internal pipe diameter of the test section both known; the superficial liquid velocity is attained. With the superficial liquid and gas velocities achieved, the data acquisition can commence. Baseline measurements are recorded from the probes before sand is added to the flow. The very beginning and end of each test is important to the ER probes. This is because of the temperature fluctuations in the test section. The fluctuations occur because the gas phase can reach temperatures up to 130° F, and the liquid temperature is close to ambient or 70° F. ER probes are highly dependant on temperature, since the electrical resistance of a material is constantly changing with temperature. Consequently the initial and final temperatures need to be the same to ensure that an accurate metal loss measurement is obtained. This can be accomplished by one of two methods. When the test is completed, either the liquid or gas phase can be shut off. Shutting off the gas phase and introducing fresh water from an auxiliary tank encourages the temperature to decrease since the gas phase is the primary source of heating without causing further erosion. Or vice versa, shutting off the liquid phase promotes the temperature to increase since the liquid is the key source of cooling the probes. Once the base temperature is established for the ER probes, the sand can be added to the slurry tank to commence erosion on the test section. The essential amount of sand is then added to the liquid in the slurry tank to reach the desired concentration of 0.5% by weight. For the purposes of this study, the sand concentration was immediately brought to the needed amount instead of gradually increasing it over time. The time was noted at the start of the sand injection so the total sand run time could be found at the end of the experiment. The Cormon software features crosshairs that can be moved along the data on the output graph to mark time and metal loss. The initial crosshair is placed where the sand flow began. There is a final crosshair that is placed on the output graph when the sand flow stops. The metal loss values are shown for each crosshair in the lower right side of the panel. These are used to determine the difference of local metal losses at the separate points, and in turn calculate the penetration rate to compare with SPPS.

To further understand the operations of correctly acquiring data, an illustration of temperature matching in placement of the crosshairs for the ER probe output is shown in Figure 6 below. Notice the effect of temperature on the measured metal loss of the ER probe without erosion occurring in the test section.


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Figure 6. ER probe output panel with matching final and initial temperatures. Figure 7 illustrates the incorrect placement of the final crosshair on the ER probe output panel compared to the correct placement. The improper placement does not account for temperature variations, and assumes the metal loss at the end of the sand injection is correct. Each metal loss reading from both crosshair placements still use the same total sand injection time to calculate the penetration rate. Therefore the period of time associated with the un-corrected metal loss is the same for the temperature corrected metal loss. The duration of time that an experiment lasts is purely dependant on the erosion rates under the specific test conditions. A test with relatively high erosion rates takes less time to run than a test with relatively low erosion rates, because the metal loss rate is much greater.

Pure Effect of Temperature Without Erosion

Temperature

Metal Loss

Measured Metal Loss and Temperature for Each Crosshair


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Figure 7. ER probe output panel with different final temperatures.

The measured metal loss and temperature for the two scenarios is found in the bottom left corner of the panels in Figure 6 and Figure 7. The temperature corrected method produces a metal loss of 184 nanometers; while the uncorrected method produces a metal loss of 75 nanometers. The uncorrected method only accounts for approximately 41% of the metal loss. Now it is much clearer as to why it is so important to correct the metal loss for temperature variations. It is essential for this to be done when running experiments on the Boom Loop. The loop is located outside where the temperature fluctuates all day long and there is a relatively large temperature difference between the liquid and gas phases as well. Once the sand injection has ceased and the metal loss is compensated for, the only task left to accomplish in the experimental procedure is to flush or clean the entire system then shut it down. The procedure varies with sand size, because of the filtration methods involved with the different particle diameters. The relatively larger diameters of the 300 and 150 micron sand sizes allows the slurry to be gravity strained through a filter bag open to atmospheric conditions. Whereas the smaller 20 micron flour must be forcefully pumped through filter cartridges due to the pressure drop associated with the small pores. The arrangement of the system requires the slurry to be pumped from the collecting tank when filtering the larger particles, and pumped from the slurry tank when filtering the smaller particles. Once the filtration is complete, both the separated sand and water are disposed of properly. The only remaining chore is to flush the loop with fresh water to ensure the removal of all sand particles and debris for the subsequent experiment.

Measured Metal Loss and Temperature for Each Crosshair

End of Sand Injection

Beginning of Sand Injection

Corrected Metal Loss

Difference of Metal Loss If

Not Corrected for Temperature


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Results

The data collected and reviewed for this study was acquired on the Boom Loop with ER probes while the test section on the boom was in the horizontal position. The erosion or penetration rates from the experiments are input into a database to be compared to the calculated rates from SPPS. Some of the results have been normalized because the information is proprietary. This was accomplished by dividing the results by the erosion measured under a specific flow condition. That number is the erosion measured when using water at 1cP and Oklahoma #1 sand that has an average particle diameter of 150 microns. Test Conditions

The superficial liquid and gas velocities that are used in this study produce patterns exclusively in slug and annular flow regimes. This section explores the conditions used to gather the pertinent experimental data of the study. Figure 8, Figure 9, and Figure 10 envisage the experimental conditions on flow regime maps. These flow maps were generated using FLOPATN software developed by Pereyra and Torres (2005). The important details of the flow maps include the superficial liquid velocity on the y-axis and the superficial gas velocity on the x-axis, where they both have units of feet per second and are on logarithmic scales. The colored lines on the graph are the transition lines between the different flow regimes. The red dots are the experimental flow conditions placed on their corresponding superficial liquid and gas velocities. It is not obvious from the graphs, but many of the flow conditions were repeated with identical flow conditions.


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Figure 8. Flow pattern map of experimental conditions in

3” pipe with liquid viscosity of 1 cp.

Figure 8 shows experimental conditions with liquid viscosities of 1 cP. This liquid viscosity is the most studied of the three due to it being the viscosity of water, which is the most abundant liquid on planet Earth. Notice that the transition between the slug flow regime and the annular flow regime is well covered, and many test conditions transfer into the annular flow realm. The next flow map seen in Figure 9 covers liquid viscosities of 20 cP, which is twenty times more viscous than water at 1 cP.



Slug

Annular

Stratified Wavy

Str. Smooth

Experimental Flow Conditions



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It is apparent that the transition lines have moved and are no longer the same shape or size. This is caused by the change in viscosity of the liquid. The liquid viscosity plays an important role in determining the flow pattern observed in fluid flow. Notice that slightly fewer tests have been performed with this viscosity and the transition from slug to annular flow also has fewer test conditions. Figure 10 shows the third and final liquid viscosity.



This liquid viscosity of 40 cP is the highest used for this study, and is 40 times

greater than the viscosity of water. Notice that the transition lines have shifted again, and the stratified wavy transition no longer exists. The experimental flow conditions in the transition from slug to annular flow are even less apparent at this viscosity.

Slug Flow Models

The slug flow models in SPPS were developed here at the University of Tulsa’s research centers, and data is constantly being collected to continuously develop and validate them. Several assumptions are made for the modeling of slug flow. The model assumes that there is no sand entrained in the Taylor Bubble. This means that particles are transported solely by the liquid phase in the slug unit. The next assumptions are for the two other components of the slug unit, besides the Taylor Bubble. The liquid film is the slowest moving component of the slug unit, and the slug body is the fastest moving component of the slug unit. The slug body’s velocity is equal to the mixture velocity of the gas and liquid



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phases. This being said, along with the assumption of no entrained sand in the gas phase or Taylor Bubble, it can be concluded that the slug body is the primary source of erosion in slug flow. Therefore further assumptions are needed in the slug body. These include that the sand particles are homogenously distributed throughout the slug body, and a no slip condition applies to the particles that are suspended in the slug body. The no slip condition holds true for the entrained particles in the liquid until they approach the wall. Once the particles reach this point the stagnation length concept is then applied. The stagnation length concept has been developed for the slug flow models in SPPS to estimate the associated impacting velocities under different flow conditions. As seen in Equation (1), the characteristic impacting velocity is crucial to calculating the penetration rate. Correctly modeling and predicting impacting velocity increases the accuracy of the penetration rate calculation. The stagnation length is the length at which the particle must travel through the fluid before impacting the wall, while the fluid is stagnant. Stagnation length also dictates the amount of decrease in particle velocity. The longer the stagnation length is, the smaller the impacting velocity will be for the same original velocity. An illustration of stagnation length is seen in Figure 11.

Figure 11. Illustration of Stagnation Length

The particle’s velocity is constantly decreasing from its original velocity as it travels

through the stagnation length and reaches its impacting velocity at the wall. This means that an entrained particle in the slug body travels at the same velocity as the slug body, due to the no slip condition, until it reaches the appropriate stagnation length. Then the slug body can


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be viewed as being stagnant and the particle must overcome the viscous effects caused by travelling through the fluid of the slug body in order to make an impact on the wall. This can also be viewed as a trade of energy from the particle’s momentum to the losses caused by friction and drag. The corresponding velocity at which the particle reaches the wall is the characteristic impacting velocity of the particle or VL.

In order to apply the stagnation length concept to the slug flow models in SPPS, the mixture properties must first be defined. The properties of most concern are density and viscosity, because they are responsible for the drag or fluid resistance on the particle. They determine the decrease in velocity of the particle as it travels through the stagnation length before impacting the wall. There are two apparent choices for mixture properties that apply to slug flow. They are the slug unit properties and the slug body properties, and a diagram of the units can be seen in Figure 12. The mixture properties will vary significantly between the slug unit and the slug body.

Figure 12. Diagram of Slug Unit and Slug Body

The cause for the difference in mixture property values, between the slug unit and

the slug body, is the gas in the Taylor Bubble. Gas has a much lower density and viscosity than liquid. Therefore the slug unit will have both a lower mixture density and mixture viscosity than the slug body. Choosing the slug unit properties for use in the stagnation length would employ relatively lower fluid properties which produce higher impacting velocities. From Equation (1), it is apparent that the higher impacting velocities would yield an increased calculated penetration rate. The slug unit properties are not used because they over predict the characteristic impacting velocity. Instead, the slug body properties are used.


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The assumptions used by the slug flow models rectify the use of the slug body properties. The assumptions state that the slug body is the primary source of erosion because the sand particles are transported solely by the liquid phase and the slug body has the highest liquid holdup. The majority of the entrained sand travels in the liquid of the slug body, so using the slug body properties actually help to keep the slug flow models mechanistic in nature. Therefore the slug body properties are used in the stagnation length. Equation (2) and Equation (3) show calculations of slug body density and viscosity respectively where HLLS is the liquid holdup in the slug body.

(2)

(3)

The particle tracking used in the stagnation length concept is one dimensional. The

particle is only allowed to travel along a path that is perpendicular to the wall at all times. The 1-D approach does not let the particle deflect up or down, and therefore only accounts for the particle’s axial velocity. This is not representative of the particle’s actual velocity, which can also have a radial component as well. The 1-D approach encounters errors, specifically for small particles, in the approximation of impacting velocity.

Particle impacting velocity is not constant throughout the slug body. There are relatively higher impacting velocities towards the front of the of the slug body, and there are relatively lower impacting velocities towards the rear of the slug body. This is caused by the difference in stagnation length for the different particle locations relative to the wall. The slug flow model must account for this, so the stagnation length distribution approach was conceived. The stagnation length distribution approach splits the slug body, the primary source of erosion, into two components. The two components are the slug front and the slug tail. Figure 13 illustrates the approach.


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Figure 13. The Stagnation Length Distribution Components

The entire slug body is split into 500 segments or stagnation lengths. The slug front accounts for the 500 increments in stagnation length. All the particles in the slug tail share a common stagnation length and impact velocity that is equal to the last stagnation length of the slug front. This is done to ensure that an accurate portrayal is achieved of the impact velocity over the entire slug body, since different stagnation lengths produce different impact velocities. The elbow and angle-head models share the same fundamental concepts with one other. They both follow and make use of the assumtpions stated earlier for slug flow. Both models apply the stagnation length concept, as well as the stagnation length distribution approach. There are only a few differences between the two. Three areas where the models differ include the definition of the slug front, the local velocity seen by the probe, and the fraction of sand particles that impact the probe faces. The Elbow Model

The elbow model defines the slug front as one pipe diameter at the front of the slug body. One pipe diameter was chosen for the measurement because it is the maximum height of a vortex in the flow, and is the maximum distance traveled by the slug before it impacts the wall. The Elbow model defines the slug tail as the remainder of the slug body after the slug front. As mentioned earlier, the slug tail shares a single stagnation length and therefore


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a single impact velocity, because the slug tail does not have as much impact on the overall erosion prediction that the slug front does. The Angle-Head Model The first difference seen between the two models is that the angle-head model defines the slug front as one probe diameter of the slug body, unlike the elbow model that defines it as one pipe diameter. The slug tail is defined as the remainder of the slug body after the slug front. The angle-head model also has the slug tail share a single stagnation length and impact velocity for each particle entrained in it. The second difference observed between the models is the velocity at the probe. The angle-head probe encounters a higher local velocity than the flat-head probe. This is caused by the angle-head probe protruding into the flow, where the flat-head probe is placed flush with the wall. Conservation of mass dictates that the reduction in area caused by the probe protruding into the flow will increase the local velocity near the probe. Figure 14 shows a drawing that helps explain this.

Figure 14. Reduction in cross-sectional area caused by the protruding probe.

Figure 15 shows a schematic of the variables necessary for the cross-sectional area

calculation of the pipe with the intrusive probe in the flow path. Equation (4) is how the flow area is calculated around the angle-head probe.


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Figure 15. Variables for area calculation of pipe at the angle-head probe

(4)

The probe reduces the cross-sectional area of the pipe creating higher local velocities

that need to be accounted for by the model. The local velocity at the probe can then be found by using Equation (5).

(5)

The increased velocity at the angle-head probe is found by multiplying the ratio of the cross-sectional areas by the original velocity in the pipe. Note that the effect on velocity, that the angle-head probe has, is more significant for smaller diameter pipes. The effect is exponentially decreased as the pipe size increases.

The third and final difference between the two models is that the angle-head probe only experiences impingements from a fraction of the entrained sand particles in the slug body. The orientation of the probe only allows for a relatively small fraction of particles to impact the probe face. The elbow probe experiences impingements from a different percentage of particles. Figure 16 illustrates the fraction of particles in the slug body that actually make impacts on the angle-head probe face.

12

12 V

AAV =


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Figure 16. Fraction of particles that impact angle-head probe face.

Model Performance

The objective of this section is to present the results from SPPS and the experimental flow loop, and discuss the strengths and weaknesses of the slug flow models in SPPS in comparison to the real world. The results are presented differently for the two slug flow models. The erosion results have been normalized for the angle-head probe because the data is proprietary. The results were normalized by dividing the erosion by the erosion calculated for Experiment Number 3 with the angle-head probe. The penetration rate is given in [mil/lb] and is not normalized for the elbow probe. Angle-Head Model The erosion results from both the angle-head model and the angle-head probe are in Table 1. The test conditions are found along with their corresponding normalized erosion.


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Experiment No

Viscosity, Sand Size VSG [ft/s] VSL[ft/s]

Normalized Calc. Angle Head

Normalized Averaged

Meas. Angle Head

1

1cp, 20um

50 1.44 0.28 1.57 2 70 2.6 0.53 3.05 3 84 2.43 1.00 NM 4 82.5 2.56 0.94 11.09 5 20cp,

20um 50 1.46 0.08 NM

6 83 2.41 0.32 5.62 7

1cp, 150um

50 1.44 10.34 3.25 8 50 2.63 8.31 2.55 9 70 2.6 16.94 8.05

10 88 2.6 30.09 12.66 11 20cp,

150um 50 1.45 4.25 2.75

12 90 2.63 36.36 21.12 13

40cp, 150um

50 1.44 1.41 1.93 14 78.2 2.63 16.12 6.84 15 89.4 2.63 26.87 12.97 16

1cp, 300um

50 1.44 52.27 8.56 17 50 2.63 42.00 6.98 18 70 2.6 80.49 21.92 19 82 2.5 114.27 53.35 20 90 2.63 143.07 80.19 21 85 3.04 126.48 58.47 22

20cp, 300um

50 1.44 46.90 6.85 23 70 2.6 103.47 36.10 24 88 2.63 193.43 118.53 25

40cp, 300um

70 2.6 105.66 20.96 26 70 2.7 105.86 20.96 27 88 2.63 190.66 51.60

Table 1. Calculated and measured results for the angle-head probe.

The results have been plotted on graphs so that the information can be better visualized. Graph 1 and Graph 2 illustrate the calculated erosion versus the measured erosion for the angle-head probe. The calculated erosion is shown in blue, and the measured erosion is in red. The y-axis represents the normalized erosion and the x-axis shows the test velocities. The graph shows increasing viscosity from left to right with the black bars separating the different test viscosities.


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Graph 1. Erosion results for the angle-head probe with large particles.

The angle-head model captures the erosion magnitude and erosion trends quite well for the larger particles as gas and liquid velocities vary. This is an indication that the model is successful at simulating all the physical mechanisms that help influence erosion on the angle-head probe. It can also be seen that the model over predicts for all of the test conditions shown here, but the erosion trends are almost identical between the two. The following graph arranges the data similar to the previous graph, but instead shows the calculated and measured erosion for the smaller 20 and 150 micron sand particles. Graph 2 continues to show normalized erosion. Viscosity is increasing from left to right.


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Graph 2. Erosion results for angle-head probe with small particles.

It is apparent that the angle-head model begins to falter when predicting for the smaller sand particles. The 150 micron particle predictions are much more accurate than the 20 micron particle predictions though. The model over predicts for the 150 micron particles much like the 300 micron size. However, the model under predicts for the smallest particle size, the 20 micron silica flour. The angle-head model has both strengths and weaknesses associated with it. The model is very capable of capturing erosion trends, and although it is a conservative model and mildly over predicts, the amount the model over predicts by is somewhat steady for all the varying superficial velocities. The model is mechanistic in nature which accounts for its ability to capture the erosion trends even if the magnitude is not exact. The 1-D particle tracking used to estimate the impacting velocity is sufficient for the larger particle sizes. The weaknesses of the model include the erosion predictions for the smallest sand size, and that the discrepancy between the calculated and the actual erosion becomes larger as particle size increases. Overall the angle-head model is the better performer of the two slug flow models in SPPS.


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Elbow Model The erosion results from both the elbow model and the elbow probe are in Table 2. The test conditions are found along with their corresponding erosion in [mil/lb].

S.No. Sand Size

(microns) Viscosity

(cp) Vsg (ft/s)

Vsl (ft/s)

Metal Loss on

probe@45 (in

mills/lb)

Calculated Elbow

1-D (mil/lb)

55 20 1 70.0 2.60 3.65E‐05 3.67292E‐06 43 20 1 82.5 2.56 1.96E-05 5.48639E‐06 48 20 20 83.0 2.41 1.75E‐05 1.43966E‐06 7 150 1 50.0 2.63 1.81E-05 4.77583E‐05

16 150 1 50.0 1.43 2.67E‐05 5.95815E‐05 27 150 1 50.0 1.45 3.59E‐05 5.91124E‐05 28 150 1 50.0 2.62 4.34E‐05 4.7789E‐05 53 150 1 70.0 2.60 4.87E‐05 0.000102891 6 150 1 88.0 2.63 3.15E-05 0.000189344 9 150 1 88.0 2.60 5.27E-05 0.000189153

14 150 20 49.4 1.43 1.16E-06 1.29736E‐05 11 150 20 50.0 1.49 2.98E-05 1.32316E‐05 32 150 20 90.7 2.63 1.01E-04 6.81219E‐05 33 150 20 90.7 2.63 7.41E-05 6.81219E‐05 1 150 40 50.0 1.44 1.05E-05 9.3479E‐06

35 300 1 50.0 1.44 1.50E-04 0.000182375 26 300 1 51.0 1.45 1.02E‐05 0.000187391 65 300 1 50.0 1.43 5.02E‐05 0.000183111 36 300 1 50.0 2.61 7.98E-05 0.000146468 66 300 1 50.0 2.62 1.30E‐04 0.000146368 25 300 1 51.0 2.63 1.06E‐04 0.000151881 63 300 1 70.0 2.64 1.27E‐04 0.000296584 51 300 1 70.0 2.60 1.53E‐04 0.000296642 41 300 1 82.3 2.51 1.03E-04 0.000440427 24 300 1 90.0 2.63 1.82E‐04 0.000559538 39 300 20 50.0 1.44 7.26E-05 5.58207E‐05 40 300 20 50.0 1.44 5.64E-05 5.58207E‐05 23 300 20 88.0 2.63 1.14E‐04 0.000483029 19 300 40 88.0 2.63 1.17E‐04 0.000297428 22 300 40 88.0 2.63 5.03E‐05 0.000297428

Table 2. Calculated and measured results for the elbow probe.

It is apparent from the data that the actual erosion rate is given for the elbow probe, unlike the normalized erosion for the angle-head probe. The SPPS erosion predictions are plotted along with the measured erosion for the elbow, and are shown in Graph 3. This


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graph shows the erosion of the large sand particles that have an average particle diameter of 300 microns. The penetration rate in [mil/lb] is represented on the y-axis, and the test velocities are on the x-axis. Calculated erosion from SPPS is in blue, and the measured erosion from the Boom Loop is red. Viscosity is increasing from the left to right. The black bars represent the change in viscosity between each data set.

Graph 3. Erosion results for elbow probe with large particles.

The Elbow model is somewhat sufficient in capturing proper erosion magnitudes, and is able to detect some of the overall erosion trends. This again validates the mechanistic nature of the slug flow models. The elbow model does not consistanly over predict the way the angle-head model does. The elbow model is mildly more sparatic when comparing to experimental results. Note that there was not as much experimental data available for the elbow probe as compared to the angle-head probe. Lack of data points increases the amount of experimental uncertainty associated with the elbow probe results.

The next graph represents the comparison between measured and calculated erosion for the larger particle erosion in the elbow. The graph is arranged with increasing viscosity from left to right. The smallest sand size begins from the left.


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Graph 4. Erosion results for elbow probe with small particles.

The elbow model over predicts for the 150 micron particle size in water’s viscosity, but under predicts in increased viscosities with the same particle size. Much like the angle-head model, the elbow model under predicts for the smallest 20 micron sand size. The elbow model captures neither magnitude nor the erosion trend for the smallest particles. The elbow model has strengths and weaknesses associated with it much like the angle-head model. The elbow model is mechanistic in nature just like the angle-head model. The two models do not share all the same characterisics as each other when comparing their results. The elbow model overpredicts for most test conditions except the increased viscosity 150 micron erosion. The magnitude of erosion was significantly improved from the model used in the previous version of SPPS. It still does not exactly predict erosion, but the erosion trends can be seen. This holds true except for the the predicted erosion for the smallest sand size, the 20 micron Silica Flour. Further Model Development

The greatest short coming for both slug flow models is that they fail when predicting erosion for small particles. Improvement is needed the most in this area. The cause for the


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under prediction of erosion for small particles is the 1-D particle tracking in the slug flow models. The 1-D approach forces the particles to travel in a perpendicular path towards the wall at all times and is never allowed to deflect from its linear path. This method encounters errors as the small particle approaches the wall. Small particles cannot overcome the viscous effects near the wall. The small particles do not have as much momentum as the larger particles. The problem is not as prevalent for larger particles, which can overcome the forces near the wall to make impacts. However, the smaller particles cannot impact the wall in a 1-D manner and their impact velocity becomes zero. An impact velocity equal to zero yields no erosion, and the models under predict.

Both models can be improved by implementing a 2-D approach to particle tracking. Therefore a 2-D approach needs to be employed in place of the current 1-D approach. This will increase the accuracy of the impact velocity in the models overall, especially for the small particles. This will ultimately improve the magnitude of erosion predictions, and the ability to capture all the erosion trends for any sand size or test condition.

There are several considerations for the 2-D approach to particle tracking. The two primary varibles for the approach include the number of particle groups and the number of particles in each group. Figure 17 illustrates these variables of the 2-D approach.

Figure 17. Illustration of 2-D particle tracking.


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The particle groups are arranged axially in the pipe. The particle groups can be viewed as being analgous to the stagnation length distribution from the 1-D approach. The particles in each group are arranged radially in the pipe. This is where the 1-D and 2-D approaches vary. The stagnation length distibution only has a single particle in each particle group for the 1-D approach, but the 2-D approach can have many. This improves the impact velocity prediction for each particle goup, which yields an improved impact velocity estimation over all of the stagnation lengths. The number of particle groups and number of particles in each group can be completely customized.

The 2-D approach to particle tracking not only changes the number of particles released at a given stagnation length, but also changes the tracjectory of the particles in the flow in comparison to the 1-D approach. As mentioned previously, the particle travels in a straight path towards the wall in the 1-D particle trajectory. However the 2-D approach allows another degree of freedom in direction that each particle can travel. Therefore the 2-D particle tracking is much more representative of the actual real world physical sytem that it is attempting to recreate.

The following figures have been included to illustrate the physical differences in the particle trajectory between the 1-D and 2-D approachs to particle tracking. Figure 18 shows the particle trajectory for every particle at every stagnation length using the 1-D approach.

Figure 18. 1-D particle tracking particle trajectory.

The particle is only allowed to travel perpendicular to the wall. The forced linear

path does not have much affect on the particles velocity, until the particle nears the wall. When the particle is near the wall, the forced linear path dramatically decreases the velocity. Moreso than if the particle was allowed to move in a 2-D manner. Figure 19 represents possible particle trajectories using 2-D particle tracking. Note that Figure 19 is not to any scale and trajectories may differ.


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Figure 19. 2-D particle tracking particle trajectories.

It is important to know that the radial location of where a particle is released from,

for any given stagnation length, can influence the particle trajectory and both components of the particle’s velocity. Figure 19 shows that the further the particle is released from the centerline of the flow, the greater the radial velocity component can be and furthermore the greater the particle trajectory will be non-linear.

The addition of the second dimension in particle tracking provides the ability to have a radial component in the particle velocity. The radial velocity component is important for determining the particle trajectory. The single component axial velocity produces a linear 1-D particle trajectory towards the impact wall. The combination of the radial and axial components of velocity produce non-linear particle trajectories that are more like actual trajectories found in experimentation.

Summary and Future Work Summary In this study, experimental data of erosion in slug and annular flow was collected to compare to the erosion predictions of the slug flow models in Sand Production Pipe Saver or SPPS. The experimental data was gathered over a wide range of flow conditions. Several different conditions were varied to gain a firm understanding as to the effects of viscosity, sand size, and superficial velocities. The liquid viscosities studied were 1 cP, 20 cP, and 40 cP. The average sand particle diameters used were 20 µm, 150 µm, and 300 µm. The multi-phase Boom Loop was used in lieu of two Electrical Resistance (ER) probes to collect the


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data. A flat-head probe was located in an elbow and an intrusive angle-head probe was located in a straight section of pipe. The objectives of this study were to collect erosion data under a wide range of flow conditions and then compare the results to the slug flow models in SPPS. The flow conditions included the slug and annular regimes, but focused primarily on slug flow. The experimental erosion data was first used to validate the penetration rate predictions of SPPS. The experimental data was then used to further improve the erosion predictions of the slug flow models in SPPS. A review of the model performance comparing the experimental erosion with the predicted erosion presented a clear path for the next step of model improvement, increasing accuracy of small particle erosion. Every aspect of this study helped contribute, in one way or another, to the conclusions drawn and overall knowledge gained. The content contained alone in the experimental results of erosion is enough to conclude many things, but the focus of this study is geared more towards the slug flow models and their performance and improvement. The conclusions of this study are listed below.

• The current version of SPPS contains two models that are specific to slug flow and mechanistic in nature. The slug flow models employ the stagnation length concept, the stagnation length distribution, and 1-D particle tracking. The two models are for the angle-head probe and flat-head probe.

• The stagnation length concept is used to determine the characterisitc particle impact velocity. The stagnation length is the length the particle must travel through the fluid before impacting the wall, while the fluid is stagnant. The longer the stagnation length is, the lower the impact velocity will be. The mixture properties used in the stagnation length concept are from the slug body.

• The stagnation length distribution is used to determine the particle impact velocities over the entire slug. The stagnation length distribution accounts for the varying impact velocities caused by the difference in stagnation length throughout the slug body. A particle at the front of the slug body will have a shorter stagnation length and therefore a higher impact velocity compared to a particle at the rear of the slug body.

• 1-D particle tracking is currently being used along with the stagnation length concept in the slug flow models. 1-D particle tracking provides sufficient results for cases with large sand particles. 1-D particle tracking does not adequately capture the impact velocities for small particles. This is caused by the forced particle trajectory and the viscous effects near the wall.


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• The angle-head model successfully captures the general erosion trends seen in the experimental data. The model even predicts comparable erosion rate magnitudes. The model is conservative and mildly over predicts by a factor of 3. The model captures the effects on erosion caused by varying viscosity and superficial liquid and gas velocities. The only area where the angle-head model is not as successful is the variation of sand size. As sand size increases, the angle-head model predicts erosion increasing at a higher rate than the experimental data suggests. Therefore, the discrepancy between the measured and predicted erosion rates gets larger as sand size increases. The angle-head model does not accurately predict erosion magnitudes or trends for small particles.

• The elbow model is not quite as successful as the angle-head model when comparing their performance. The magnitudes and trends from the experimental data are loosely captured by the elbow model. However, the magnitude of predictions from the elbow model was greatly improved from the previous version. Much like the angle-head model, the elbow model suffers in capturing small particle erosion.

• The primary conclusion to this study is that both slug flow models, inherent to SPPS, can be improved by implementing 2-D particle tracking in place of the current 1-D approach. 2-D particle tracking will improve the estimated characteristic particle impact velocities and advance the models in providing more accurate erosion predictions.

Future Work As time and labor progressed throughout this research interesting topics were traversed that originally were not foreseen, while others were predicted before the study began. Unfortunately many subjects and questions were not addressed due to time constraints. To help improve the continuation of this topic for future research, several recommendations are listed below.

• Create a more robust erosion databank to further verify and validate the slug flow models in SPPS. Including conducting more experiments under a wider range of slug and annular flow conditions while filling in the gaps of experimental conditions from previous testing. Data acquisition should concentrate in the elbow as well as the straight section. Collect data for all three probes; probe @ 45, probe @ 90, and the angle-head probe. Most importantly collect much more erosion data for the smaller 20 micron sand size.

• Implement 2-D particle tracking in place of the current 1-D approach for the slug flow models in SPPS. Compare the 1-D and 2-D erosion predictions with one


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another and the measured erosion from experimentation. Determine the strengths and weaknesses for the 2-D particle tracking across all test conditions.

• Conduct a sensitivity analysis for the 2-D particle tracking in the slug flow models. Vary the amount of particle groups and the number of particles in each group to gain a firm understanding of the corresponding effect on the impacting velocity calculation. Determine a median between the accuracy of erosion predictions and the computational time required to complete them.

• Apply results to the new version of SPPS.

EROSION IN SLUG FLOW X-1 EROSION IN SLUG FLOW Introduction

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