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Case Studies - Opgrade

  • 11 Jan 2012 5:22 PM | Marketing Department (Administrator)

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    Methodology

    Opgrade Performance Study
    of an existing plant

    50 Words or Less

    Management wants only as much storage as is really necessary, yet operations always asserts that they need as much as storage as can get! Through our Opgrade methodology, our client’s management team can show operations that only 50% of their planned asphaltene storage is truly beneficial to the bottom line.

    Project Background

    Our client, a Canadian energy company, has a facility in Alberta to recover and upgrade oil-sands (bitumen) reserves.  When the bitumen arrives at the upgrading facility, it is first separated into distillate and residues through a process called distillation.  The distillate is then sent to a Hydrocracking (HCR) Unit which uses hydrogen, heat and catalyst to upgrade longer hydrocarbon molecules (distillate) into shorter molecules (Premium Synthetic Crude), which are then pumped to a pipeline for sale at roughly the WTI (West Texas Intermediate) spot price for crude.

    An optimist says "the glass is half full." A pessimist says "the glass is half empty." Opgrade says "the glass is oversized!"

    The stickiest, heaviest portion of the bitumen, the asphaltenes, are separated from the residues and are too thick to be transformed into Premium Synthetic Crude (PSC). Instead, this facility uses these asphaltenes as fuel for a gasification process that breaks down the asphaltenes into hydrogen and carbon monoxide in the presence of sub-stoichiometric oxygen, high temperatures and catalyst. This process converts what would otherwise be a low-value byproduct into something with a much higher value, make-up hydrogen for the HCR.

    To maximize production (and therefore profits) the HCR Unit needs a consistent and continuous input stream of both bitumen and make-up hydrogen to replace the hydrogen consumed by the process.  Any reduction in either the distillate or make-up hydrogen roughly translates to a roughly equivalent reduction in HCR production. Unfortunately, nothing in a plant is perfectly reliable, so frequent and lengthy outages substantially reduce HCR production and therefore profits.

    The Problem

    To smooth out the input flows to the HCR, distillate tanks are in place to theoretically decouple distillation and the HCR allow the HCR to continue running if distillation has an outage, or to allow distillation to continue running if the HCR has an outage.  The problem, however, is that the when distillation has an outage, the gasifiers lose their feed and must shutdown, thus forcing the HCR to shut down.  Similarly, if the HCR shuts down, the gasifiers cannot send hydrogen to the HCR, which means they cannot accept feed, thus forcing distillation to shut down too.

    Without a means to store asphaltenes, distillation and the HCR remain coupled, meaning the whole plant shuts down anytime any individual area experiences an outage. But how big should the tanks be? In industry, most calculations for tank sizes are based on either:

    ·   Tribal knowledge (guessing)

    ·   Single-point improvements
    (ignoring system effects)

    Simulation provides an effective means to empirically evaluate plant performance to both improve confidence and reduce risk. Outside of our Opgrade methodology, however, there isn’t a unified simulation method to both properly and empirically account for all of the complexity of the real world when evaluating how tank sizes affect plant performance.

    Study Objectives


    Figure 1 – A simplified Block Flow Diagram of the facility with new capital in red


    The natural goal of this study is to optimize the size of the new asphaltene storage tanks by calculating how variations on the design capacity will affect revenue. Specifically:

    ·     10% of the tank design capacity

    ·     50% of the tank design capacity

    ·     80% of the tank design capacity

    ·   100% of the tank design capacity

    ·   120% of the tank design capacity

    Where 100% design capacity is the size as calculated by our client’s engineers.

    System Description

    Figure 1 above is a simplified Block Flow Diagram (BFD) of the modeled system. Though the actual model includes many more elements than are shown here, including upstream bitumen recovery units and certain utility or support units, this figure represents the configuration of the critical units in the system.

    Failure Data

    With all reliability-based studies, good failure data is fundamental to providing meaningful results.  As the adage goes, garbage in: garbage out.  For all Opgrade studies, we prefer to use actual operational data, but when that data is not available, we use a combination of trusted failure data sources from industry and vendor databases.

    Regardless, all data undergoes a thorough vetting process with the project team before it is used in any study.

    Tank Volume Work-Off Ability

    To maximize the utility of any tank, it is imperative to retain or create the ability to work-off accumulated volumes of a full tank, or refill an empty tank, during normal operations.  Waiting for an upstream or downstream outage to cause a tank to empty or fill is never the best use of that tank.  For this study, tanks can fill or empty at 10% over normal operations.

    Results Summary

    To evaluate the possible tank capacities, sensitivity experiments were run that varied the size of the tank.  As illustrated in the chart below, the changes in revenue generate a curve of diminishing returns. If PSC sells for $100 per barrel, a 50% capacity tank only reduces average annual revenues by 0.1% or $1.6 million.

    Chart 1 – Diminishing Returns


    Since a 100% capacity tank would really be two 50% capacity tanks physically costing about $20 million each, deleting one of the 50% tanks will significantly improve the ROI of this capital project.

    Maximize Return on Investment

    Now that Opgrade can more accurately predict how tank sizes affect revenue our clients can allocate tankage with confidence and maximize their expected ROI like never before.

  • 29 Nov 2011 5:23 PM | Marketing Department (Administrator)

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    Methodology

    Opgrade Performance Study of an existing facility

    50 Words or Less

    Our client produces make-up hydrogen required for a hydrocracker unit by gasifying asphaltenes. Because gasifiers are notoriously unreliable, our client performed an Opgrade study of this facility and learned that building a 50% redundant hydrogen supply would maximize return on investment and could improve average annual revenues by $37 million.

    Project Background

    Our client, a Canadian energy company, has a facility in Alberta to recover and upgrade oil-sands (bitumen) reserves.  Pivotal to their existing facility is the Hydrocracking (HCR) Unit which uses hydrogen, heat and catalyst to upgrade longer hydrocarbon molecules (bitumen) into shorter molecules (Premium Synthetic Crude), which are then pumped to a pipeline for sale at roughly the WTI (West Texas Intermediate) spot price for crude.

    The stickiest, heaviest portion of the bitumen, the asphaltenes, are too thick and cannot be transformed into the Premium Synthetic Crude. Instead, this facility uses these asphaltenes as fuel for a gasification process that breaks down the asphaltenes into hydrogen and carbon monoxide in the presence of sub-stoichiometric oxygen, high temperatures and catalyst. This process converts what would otherwise be a low-value byproduct into something with a much higher value, make-up hydrogen for the hydrocracker.

    However, to maximize production (and therefore profits) the Upgrader plant needs a consistent and continuous input stream of make-up hydrogen to replace the hydrogen consumed by the process.  Any reduction in make-up hydrogen roughly translates to a roughly equivalent reduction in hydrocracker production. Unfortunately, gasifiers are not highly reliable, so frequent and lengthy gasifier outages substantially reduce hydrocracker production and therefore profits.

    The Problem

    When deciding which capital investment project to pursue, the goal is to select the project that will net the highest return on investment.  The problem is that in most industry pro formas, all assumptions for revenue improvements are based on either:

    ·   Tribal knowledge (guessing)

    ·   Single-point improvements (ignoring system effects)

    Simulation provides an effective means to empirically evaluate plant performance to both improve confidence and reduce risk. Outside of our Opgrade methodology, however, there isn’t a unified simulation method to both properly and empirically account for all of the complexity of the real world when evaluating plant performance.

    With Opgrade, our client knows they’re making the best possible capital allocation decision because they have the empirical evidence to back it up

    Study Objectives

    The natural goal of this study is to determine how additional hydrogen supply would affect revenue. Specifically, the objectives of this study were to identify the average improvement in revenue with the addition of:

    ·  25% excess hydrogen supply

    ·  50% excess hydrogen supply

    ·  75% excess hydrogen supply

    ·  100% excess hydrogen supply

    Where 100% hydrogen supply is the normal hydrocracker make-up rate.

    System Description

    Figure 1 below is a simplified Block Flow Diagram (BFD) of the modeled system. Though the actual model includes many more elements than are shown here, including upstream bitumen recovery units and certain utility or support units, this figure represents the configuration of the critical units in the system.


    Figure 1 – A simplified Block Flow Diagram of the facility with new capital in red
     

    Failure Data

    With all reliability-based studies, good failure data is fundamental to providing meaningful results.  As the adage goes, garbage in: garbage out.  For all Opgrade studies, we prefer to use actual operational data, but when that data is not available (as with a not-yet-built Hydrogen plant), we use a combination of trusted failure data sources from industry and vendor databases.

    Regardless, all data undergoes a thorough vetting process with the project team before it is used in any study.

    Existing Model = Quick Results

    Generating the results for the objectives took only a few days because a model of the existing facility was already fully developed when our client asked us to perform this study (see the related Case Study: Oil Sands Plant Infrastructure). This is a notable benefit to Opgrade Performance Studies.  Once the work to develop a baseline model is complete, running any number of informative performance studies on an existing facility is straightforward and fast.

    And Opgrade studies truly reflect the performance of your plants because our proprietary method combines the best features of several tools. Opgrade was designed from the ground-up specifically to predict a plant’s future revenues by calculating expected average production based on a plant’s actual equipment configurations, reliability data, mass balances, operational rules, and tank logic.

    Results Summary

    To evaluate the possible hydrogen plant capacities, sensitivity case models were run that varied the output.  As illustrated in the chart below, revenue improvements generate a curve of diminishing returns. If PSC sells for $100 per barrel, a 25% capacity plant generates $28 million in additional revenue, and a 50% plant generates $37 million.

    Chart 1 – Diminishing Returns

    Maximize Return on Investment

    Calculating return on investment (ROI), requires both the expected revenue improvements and capital costs. Since we now have a plot of the expected revenues (Chart 1), we plot internal rate of return (IRR) against plant capacity (Chart 2) to find the maximum ROI, if we assume the following schedule of Total Installed Costs (TICs, in $millions).

    Cap.

    25%

    50%

    75%

    100%

    TIC

    $80

    $100

    $115

    $125


    Chart 2 – 50% Capacity Plant Yields Max IRR of 37%

    Now that Opgrade can more accurately predict revenue improvements from capital projects, our clients can allocate capital with confidence and maximize their expected ROI like never before.

  • 03 Aug 2011 3:34 PM | Marketing Department (Administrator)

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    Methodology

    Opgrade Plant Performance Study

    50 Words or Less

    By utilizing our proprietary reliability-based capital investment decision tool, our client can proceed with confidence knowing that their $700 million capital improvement project will generate ample return on investment.  The study also identified $30 million in possible revenue improvements and at least $40 million in possible capital expenditure reductions.

    Project Background

    Our client, a Canadian-based energy company, has a facility in Alberta to recover and upgrade oil-sands (bitumen) reserves.  Their existing facility includes a Steam-Assisted Gravity Drainage (SAGD) plant that recovers bitumen by injecting steam into the ground, and an Upgrader Plant that transforms the recovered bitumen into a Premium Synthetic Crude, which is then pumped to a pipeline for sale at roughly the WTI (West Texas Intermediate) spot price for crude

    To maximize production (and therefore profits) the Upgrader plant needs a consistent and continuous input stream of diluted bitumen from the SAGD plant.  Our client’s existing SAGD plant was not producing its expected flow of bitumen and consequently they began looking at options to add another SAGD production facility.

    Every plant experiences both unplanned and planned maintenance outages.  Supply, intermediate and product tanks can mitigate the effect of these outages when they occur.  Therefore our client also wished to evaluate the addition of several tanks.

    Study Objectives

    As with all studies we perform of this nature, the goal of this study was to scientifically calculate how capital investment decisions affect revenue when taking plant reliability into account.  Specifically, the objectives were:

    1.       Build a model with a second SAGD facility and calculate the improvement in the on-stream factors for the various product streams

    2.       For each of the proposed new tanks, calculate the tank size that maximizes return on investment.

    A New Kind of Modeling Tool

    There are many reliability simulation tools available, but few incorporate tanks and none calculate flows with dynamic mass balances and actual operational rules. Therefore a new kind of modeling tool was necessary to calculate the results our client wanted.

    Our Opgrade Plant Performance tool fuses the best features of other limited tools into one comprehensive package.  Opgrade was designed from the ground-up specifically to predict a plant’s future revenues with a reliability flow solver that calculates expected production based on a plant’s actual equipment configurations, reliability data, mass balances, operational rules, and tank logic.

    Process Inputs & Outputs

    Inputs to this process include the recovered bitumen from the field, diluent to dilute the bitumen (so it can flow in a pipeline), make-up water for steam production, and a small stream of bitumen imported from a third party.

    There are several products associated with this process. The primary product, for obvious reasons, is the Premium Synthetic Crude.  Secondary products include diluted bitumen from the SAGD plant and the “cracked” diluted bitumen (which contains olefins and therefore sells for a lower price).  Butane is also produced, but it is normally blended into the primary product stream.  Waste streams include sulfur and an ash slurry.

    Failure Data

    With all reliability-based studies, good failure data is fundamental to providing meaningful results.  As the adage goes, garbage in: garbage out.  For all O studies, we use a combination of trusted failure data sources from industry and vendor databases.  All data undergoes a thorough vetting process with the project team before it is used in any study.

    Modeling the Existing Plant

    Modeling the existing plant was a necessary step for two important reasons:

    1.       The existing plant model establishes the baseline for evaluating any possible production improvements.

    2.       Results from existing facility model were validated by matching them with operational history. This proves the methodology is sound.

    Additional Infrastructure

    The additional capital infrastructure was modeled in the same manner as the existing plant, including a second SAGD facility, new supply and product tanks, larger intermediate tanks, and new interconnects (see Figure 1).

    Figure 1 – Block Flow Diagram of the facility with infrastructure improvement options

    Objective 1 Results

    After carefully studying and validating this system, the additional capital is predicted to improve revenue by 14.9%

    Case

    On-Stream Factor

    Existing Plant

    75.3%

    Infrastructure Improvements

    86.5%

    Table 1 – Objective 1 Results

    Objective 2 Results

    To evaluate the proposed tank sizes, sensitivity case models were run that varied the sizes of each tank, one at a time.  As illustrated in the chart below, several tank sizes can be reduced without harming production.  This yielded capital savings of at least $40 million.

    Chart 1 – Objective 2 Results Sample

    Other Sensitivity Cases

    Realizing the power and benefit of the model, our client asked that we build several other sensitivity case models to evaluate other potential investments. One such case was to add additional capacity to produce hydrogen (a necessary component for hydrocracking, a part of the crude upgrading process). 

    The additional hydrogen supply improves the on-stream factor by 1.5%, yielding a possible annual revenue improvement of about $30 million.

    Maximize Return on Investment

    At the end of the study, our client was able to move forward confidently knowing that the infrastructure improvements they were evaluating would generate sufficient return on investment to justify the Capital Expenditure (CapEx).  In fact, assuming a WTI equivalent spot price of $100, per barrel, this Opgrade study shows the infrastructure improvements generate a return on investment (IRR) in excess of 30%.

    Beyond the CapEx justification, this study also demonstrated the possibility of at least $40 million in CapEx reductions and $30 million in annual revenue improvements. When compared to the cost of the study, either outcome generates a return of 100:1 in the first year.

    Learn More

    To learn more about Opgrade studies at Barber & Barber Associates, contact our sales staff today!

  • 22 Jul 2011 4:59 PM | Marketing Department (Administrator)

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    Methodology

    Reliability, Availability & Maintainability (RAM) Study

    50 Words or Less

    By employing our RAM study methodology, our client can sell more product because they can produce more steam. In this case, greater steam generating flexibility with fewer maintenance requirements clearly provides higher steam availability over time, yielding an annualized revenue improvement of up to $6.1 million.

    Project Background

    Our client, a Canadian oil-sands (bitumen) producer, has a facility in Alberta to recover and upgrade bitumen reserves.  Their primary method of recovering bitumen is through a Steam-Assisted Gravity Drainage (SAGD) process whereby steam is injected into the ground to reduce the viscosity of (soften) the bitumen so that it may drain into a lower wellbore and be pumped to the surface.  This client already has one operational SAGD field and is performing the front-end engineering and design for a second SAGD field.

    Consistent and continuous steam production is very important to SAGD operations because of the “steam chamber” that forms underground. To produce a steady supply of bitumen at the surface, this steam chamber must be stable and growing to come in contact with the bitumen reserves. To maintain a stable and growing steam chamber, one must supply a consistent and continuous volume of steam.  If steam production is lost or reduced, the steam chamber will start to collapse and may require several days of steam injection to re-stabilize the steam chamber and begin producing bitumen again.

    Therefore, to make better steam production capital investment decisions, the client asked us to perform a RAM study of two steam generation equipment configurations to determine the configuration that yields the highest steam On-Stream Factor (OSF).  Knowing how equipment configuration affects steam production (and ultimately bitumen sales) helps our clients make better informed decisions and aids in establishing (or refuting) the economic justification of capital expenditure.

    Steam Generation Equipment

    We were asked to evaluate two basic options: A) use two combustion gas turbines (CGTs) with heat recovery steam generators (HRSGs) and one once-through steam generator (OTSG, i.e. boiler) to generate steam, or B) one CGT with five OTSGs to generate the same quantity of steam.  See Figure 1 below for a comparison of the configurations.  Option A has fewer points of failure; option B is more flexible.  For both options, it is imperative to have a very reliable steam production configuration to protect the stability of the underground steam chamber.

    Steam Generation Options

    Though there is more equipment necessary to produce steam beyond those shown, these are the most prominent items in the system and thus the only ones modeled.

    Water Supply & Treating

    No matter how you generate steam, you need high-quality water to run boilers reliably for extended periods.  For this reason, both configuration options utilize two Hot Lime Softeners to reduce the mineral content in the boiler feed water.  When a softener is offline, only half the boiler feed water is available to produce steam, and thus only half the steam can be produced. And again, while there is more equipment necessary to supply and treat water, the Hot Lime Softeners are the only ones included in this study.

    Failure Data

    With all reliability studies, good failure data is fundamental to providing meaningful results.  As the adage goes, garbage in: garbage out.  For all BBA studies, we use a combination of trusted failure data sources from industry and vendor databases.  All data undergoes a thorough vetting process with the project team before it is used in any study.

    Scheduled Maintenance

    All of the equipment items in this study undergo periodic scheduled maintenance activities.  For this study, modeling the Hot Lime Softeners is important because they experience a scheduled annual outage with a one-week duration.  It makes operational sense, then, to “shadow” scheduled boiler maintenance activities within these softener outages, thus minimizing the overall downtime. 

    With option A in particular, it is important that the annual combustion turbine maintenance activities be shadowed within the softener outages.  If for some reason turbine maintenance cannot coincide with softener maintenance, a significant reduction in steam on-stream factor will result. 

    With Option B, however, if the single turbine’s maintenance cannot coincide with one softener’s maintenance, it would simply be scheduled to coincide with the other softener’s maintenance activities.  Additionally, OTSG boiler maintenance can be scheduled anytime outside of turbine maintenance as any single OTSG can be offline without affecting steam production.

    Sensitivity Cases

    Performing sensitivity cases is the best use of any RAM study.  Sensitivity cases allow you to evaluate the “what if” scenarios to optimize the system configuration.  In this study, the following sensitivity cases were evaluated:

    1.       What if Hot Lime Softener maintenance need only be performed every 2 years instead of annually?

    2.       What if once every three years the scheduled maintenance for turbine #1 cannot be shadowed by the scheduled maintenance of Hot Lime Softener #1.

    General Results & Conclusions

    After carefully modeling and studying this system, Option B, with greater flexibility with less maintenance requirements, clearly provides the most available steam over time. As can be seen in Table 1 below, the long-term On-Stream Factor (OSF) for Option B is consistently 0.3% to 0.4% higher than for Option A.  On an annualized basis, 0.3% translates to increased revenue of as much as $6.1 Million per year.

    Case

    Option A OSF

    Option B OSF

    Base

    94.3%

    94.6%

    1

    94.3%

    94.7%

    2

    94.1%

    94.4%

    Table 1 - Overall Results

    Even without any scenarios, Option B produces more steam than Option A.

    For case 1, Option A sees no benefit because though the Hot Lime Softener maintenance interval is doubled, the turbine maintenance interval is not. Option B improves in case 1, however, because maintenance of any single boiler does not impact steam production. Therefore, reducing Hot Lime Softener maintenance improves overall steam production for Option B.

    For case 2, both options see a reduced steam production of 0.2% from the base-line, but Option B is still produces more steam in the long run than does Option A.

    Learn More

    To learn more about RAM studies at Barber & Barber Associates, contact our sales staff today!

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