Here is my recap while live-blogging this session in real-time. Apologies in advance for any errors or omissions.
Reduce Energy for Distillation - James Beall
James opened discussing the importance of a control foundation. He noted that this provides some of the best ROI for improvement project. Advanced process control (APC) particularly provides significant results. Some typical expected results include:
- 4-8% increase in throughput
- 5-10% reduction in energy costs
- 2-8% reduction in product inventories
- 40-80% reduction in quality variation
- 1-5% increase in equipment availability
He stressed the importance of taking a holistic approach:
- Understand process objectives
- Understand impact of measurement devices and final control elements (e.g. valves)
- Review control strategies
- Utilize advanced regulatory control (Cascade, Feed Forward, …)
- Choose appropriate PID options (e.g. Form, Structure, etc.)
- Tune loops with a coordinated response to maximize process (vs. loop) performance
- Eliminate variability at the source where possible
- Move variability to less harmful places with control techniques
For a distillation column, typically loop interactions are present. Manual control had shown that the steam usage could be reduced by 25% and still meet product specs. The automatic control scheme became unstable as the energy was reduced.
The solution was to test control valves and recommended improvements by using Emerson’s EnTech Toolkit (InSight) to determine process dynamics and loop interaction. The tuned loops to have a coordinated, non-interacting response using Lambda tuning. The including reducing reflux 27% and steam usage by 25% with a project payback of 3 months.
Control Performance Visibility and Awareness- Hydrogen Plant - Jay Colclazier
Jay opened discussing the problem of improving awareness and visibility of control performance. Most people would agree that Control Performance is important. However, it is usually difficult to quantify Control Performance is often not very visible.
The solution is to implement simple metrics and improved visibility through control performance reports and real-time KPI values. The team uses Inspect portion of DeltaV Insight focusing on control utilization metrics. Also Jay noted that they used built-in reporting functions which were summarized and distributed weekly.
For the real-time KPIs, they calculated process KPI values in real time inside a DeltaV control module:
- Yield Values
- Material Balances
- Throughput measures
- Heavily filtered to take out short-term variability
These values were added to the Historian and a few of the KPIs were made visible on the DeltaV Operate screens for the operators.
Jay share an example of a Steam/Methane Reformer and PSA KPIs:
- Hydrogen Yield
- PSA Yield
- Reformer Material Balance
- PSA Material Balance
- Steam/BFW Material Balance
- Unit Cost
Wellpad Optimization - Warren Mitchell
Warren opened decribing the production optimization challenge. Across a field with hundreds of injector/producer pairs, into which wells, in what order and when do you put the steam in order to produce the greatest volume of bitumen?
Goals of the project included:
- Automate and improve the ease of operating >100 wells/operator
- Stabilize the producer well
- Minimizing the well sub cool by effectively controlling the steam trap down-hole
- Optimize pad steam injection and emulsion production rates
- Mitigate the impact of upstream disturbances
- Improve the utilization of high-efficiency wells
- Reduce production and steam injection variability to sensitive wells
- Protect reservoir team chambers from large swings in steam injection rates
- Coordinate the well pad operation with separation and steam generation processes
- Improve the consistency of how well pads are operated
Here are some of the details around two of the solution’s Spartan has developed for SAGD facilities that are having a big impact on operating sites today. The Spartan team has been working with SAGD producers since the first pilot sites got off the ground (20+ years now).
Warren represents a group called ‘Advanced Process Solutions’ at Spartan and it is their job to help process manufacturers and producers deploy advanced process control and information technologies to lower operating costs. The team typically gets involved after a plant has started up to tune and then optimize it’s performance. They are seeing more and more producers begin to think about these issues during FEED and detailed engineering. There is much that can be done upfront to make sure the plant does not take years after start-up to reach steady state conditions at your nameplate capacity.
Well pad optimization is just a component of site wide optimization; also includes steam header coordination, boiler load optimization, separation & recovery optimization, water treatment optimization, etc. Even though the ground-up implementation approach is critical to the success of these projects, they must be designed within the framework of a site-wide optimization strategy.
The biggest thing that downhole instrumentation (P/T) gives the team is the liner subcool, a measure of the amount of fluid in your steam chamber above the producer well. The biggest risk to damaging SAGD wells is flashing steam across the liner, which occurs when the fluid that is coming into the well drops to its saturation pressure for the temperature it is at as it passes through the liner.
At this point, the fluid undergoes a phase change from liquid to vapor (steam), thus velocity increases substantially and can wash out liners, compromising ability to control sand. Liner subcool is calculated as the associated saturation temperature to the producing bottom hole pressure, then subtracting the producing bottom hole temperature (tells you how far you are from a saturated steam condition).
For every 10 degC of liner subcool, this represents 1 meter of fluid above your producing well. Optimal (most efficient) SAGD operation is at 0 degC subcool, however due to the nature that instrumentation is not 100% accurate, wells are not flat (go up and down), and drawdown is not evenly distributed along the well path, the team targets a minimum of 10 degC liner subcool, targeting 20 degC in the past due to our lack of reliable downhole P/T measurement.
With better instrumentation, the team will push sub cools lower than where they currently operate as it is more conservative.
Some qualitative results include:
- Smooth, Stable, Reliable Production.
- Decreased risk of flashing fluids across liners
- Decreased risk of pump damage
- Decreased risk or well and reservoir damage and abnormal steam chamber formation
Some quantitative results include:
- > 5% increase in well pad production
- ~45 % decrease in reservoir sub cool standard deviation
- ~80% decrease in pump sub cool standard deviation
- ~90% decrease in ESP speed standard deviation
Data Analytics in Process Optimization -Willy Wojsznis
Willy opened discussing optimizing principles:
- Developing model
- Defining optimization objectives usually targeting increased profit by increasing production rate and saving energy and materials.
- Applying optimization solution (LP, QP, NLP….)
Some reasons analytics may be used in process optimization include:
- Developing deterministic (causal) models require process testing which is not always acceptable – analytic models are developing based on historical data
- Another objectives like minimizing production losses due to the process faulty operation is not framed into classical optimization – process faulty operation can be detected by analytic model and losses minimized
Willy noted that he has been exploring analytic quality prediction and fault detection techniques for distillation columns. Analytics can serve as a backup for online analyzers since it provides a good back up for on-line analyzer and shows a potential for substituting on-line analyzer for some quality parameters.
He shared an example where an on-line analyzer was malfunctioning over period of one week. The analytic predictors both PLS and NN provided consistent quality indication and the analytic predictors helped the operator personnel to avoid irrelevant operation actions and engineering personnel to diagnose the on-line analyzer fault.
Another example was detecting abnormal conditions for analytics with on-line data and their relationships; i.e. correlations differ from those used for the analytic model development. Typical causes include grade change, not covered in the modeling data, new operation settings and process malfunctions.
Analytic quality prediction does not perform well at the abnormal conditions, BUT analytic fault detection is an excellent way to capture process abnormality.
Another example is measurements and valves operation validation. Continuous data analytics may be used as a regular tool for loop diagnostics. This measurement vailidation is done by exploring trends from the data historian by using control system tools and fault detection PCA, which is the the easiest way to detect measurements or valve faults.
Improving Equipment Performance - a Tale of Two Valves - Jim Coleman
Jim opened sharing a tale of two valves. Both "worked for years", then came a change in requirements and then they went "belly up". The maintenance team said the valves are good, but process control is not acceptable and limits production.
The ball valve caused caused the flow to oscillate which produced unacceptable performance and slip-stick cycling. The valve required deadband reset scheduling and works for ‘slip-stick’ cycling in self-regulating loops.
For the globe valve on a pressure vessel, they needed a linear relation between controller output and flow. The pressure control on the main vessel was never tuned well and its performance limited production. They were measuring flow vs. stem position which was very non-linear. They needed the PID to command flow and not command stem position since the combo is linear.
The solution was to use DeltaV signal characterizer (SGCR) blocks to provide valve linearization to the loop. This linearization provided excellent performance over the full range and allow a 50% increase in production rate.
Application of APC to LNG Process - Lou Heavner
Lou opened describing the location of the LNG process in Africa. It was the most value delivered project he has ever done. Lou described the steps in an advanced process control project cycle. It includes vendor selection, controller tuning, base data collection, step testing, model generation, post audit, project close out, site acceptance test, commissioning and model validation. A turnaround was performed to fix valves between commissioning looping back to another site acceptance test.
Control of the unit balancing required:
- Valve operability
- Effective control
- Balancing compressor loads
- Adjusting compressor loads for ship loading
Results from applying APC was 19.53 m3/hr or 162,500 m3/year additional production.