A few decades ago, most of the industrial plants across the globe were typically designed and built according to several possible availability outcomes (High, Low and Expected Cases) due to limitations of manual calculations. Historically, the complexities of process plants and the numerous variables made it practically impossible to achieve accurate results for the best plant configuration that could deliver maximum return on investment (ROI).
Considering this to simulation modeling, people used to make capital investment decisions in major projects based on a few simulations, which often resulted in bad decisions that lead to diminished shareholders value.
Since the launch of computer simulation technology, it is now possible to run thousands of lifecycle simulations accurately and run sensitivities (what-if studies) to make safer, more profitable and better informed decisions. Availability simulation modeling has proven to deliver cost-effective and optimized plant configurations based on net present value (NPV), thereby significantly reducing the initial cost of capital investment. These lifecycle simulations are run based on the Monte Carlo simulation technique that randomly generates thousands of what-if scenarios of possible system performance. Each one is captured and used to produce a frequency distribution that allows ‘probable’ performance (not simply ‘possible’ performance) to be quantified. Availability modeling has now been widely used to identify the best major investment decisions for new and operating assets in the oil and gas industry.
The best time to conduct availability modeling for a new project is at the concept selection phase. The earliest involvement of simulation modeling has maximum influence on the project, since having the right technology and design selection delivers maximum benefits. Secondly, the best equipment configuration (1x100%, 2x50% or 3x33.34%) for critical assets can be evaluated and justified, thereby delivering optimum production availability. Keeping the reliability, availability and maintainability (RAM) model live by updating through actual performance data, the analysis outcome could be confidently used for making key future improvement decisions.
Effective utilization of availability modeling during the operation phase of the project is that grey area many industries across the globe still need to explore. Let me share a few real-life examples of ‘unconventional’ usage of availability modeling during operation phase to achieve business performance improvement.
Example 1 - Critical Spares Risk Analysis Using RAM Modeling for Turbine-Driven Centrifugal Compressors
Availability modeling was used to optimize the number of critical spares in stock for 14 sets of identical, 30-year old, gas turbine-driven centrifugal compressors based on their risk assessment. The actual demand rate of each critical spare from 15 years of previous history failure data was used as an input to the RAM model, considering the interchangeability between 14 identical machines. The result was displayed as statistically averaged downtime per year, per machine for 0, 1, 2, 3 spares in stock, for each category of spares. The risk of holding one (or more) spares versus not holding a spare was therefore evaluated to optimize the number of critical spares.
Figure 1: Example of statistically averaged maximum outage days/year for zero spare in stock for each category of spares
Example-2 - Maintainability Modeling Identified Best Location for Maintenance Crew
Evaluating the best location for a single maintenance crew for two gas plants (A & B) was analyzed using availability modeling based on the crew’s impact on production efficiencies when taking different mobilization and response times into consideration.
Figure 2: Snapshot of reliability block diagram (RBD) for the gas system
Plant A was the older plant and where the maintenance crew was already based. Plant B was the newer facility; however, being at a remote location, it was not feasible to base the crew there. Alternatively, there was a central station between A & B, where the crew could be located depending on the economical justification. By locating the maintenance crew at the central station, the overall system availability model for both plants A & B identified an extra gas production of 289 million m3 over life.
Example 3 - $6 Million Potential Cost Savings through Optimized Configuration of Produced Water Re-injection Pumps
Produced water re-injection pumps are production critical for crude oil production. One oil company, motivated by an increase in the water production forecast for the next 15 years at one of its water disposal stations, conducted a concept study to have an additional pump to meet the oil production targets. Availability modeling as part of a concept study was conducted to identify the best optimized configuration for the additional new pump with the existing 2x60% configuration. The existing 12 stage high pressure centrifugal pumps are each driven by a 3.65 MW electric motor, variable speed via fluid hydraulic coupling.
The initial proposal under study was for another identical variable speed pump to make a 3x60% configuration. After modeling various cases, the most optimized case identified was a 2x60% + 1x40% (fixed speed motor driven pump). The RAM analysis outcome potentially saved more than $6 million (as initial Capex cost) by selecting the optimized case configuration based on the NPV (economic analysis).
Example 4 – Crude Oil Transport Pipeline System - Business Exposure
(Just one percent of system availability corresponded to two million barrels of crude oil transportation to the export terminal)
Due to the strategic importance of a crude oil transport pipeline of an oil and gas company, a RAM model was developed to predict the system’s future performance. The model was kept alive and dynamic by periodically updating it with the actual reliability data of all critical assets (booster and shipping pumps). The analysis outcome was used to identify opportunities for making key business improvement decisions. The key deliverables of the RAM analysis were:
- Overall production availability;
- Ranked equipment criticality listing (causing highest loss of availability);
- Impact of future strategies (what-if scenarios);
- Production volume/Deferment volume;
- Identification of bottleneck opportunities for improvements.
Figure 3: Example of ranked equipment criticality pie chart related to loss of availability
Availability modeling in the concept phase of a new project greatly improves ROI. It helps in optimizing plant configuration and eliminates unwanted redundancy and associated operating costs over the plant life. A common approach used in the industry to improve availability is increasing redundancy. However, there are a lot of other things that could be done before adding redundancy to improve availability, e.g., optimizing shutdown maintenance frequency and durations, reducing the number of interventions by using a condition-based strategy rather than hard time overhauls and improving maintainability. Using availability modeling allows companies to evaluate all of these and tells what could most probably be achieved.
All decisions involve actions/events in the future and thus have uncertain outcomes. Any system subject to uncertainty can be understood much better using simulation modeling. Uninformed decisions cost companies millions of dollars every day.
Modeling also should be used in the operating phase, especially when operating context changes during the life of the plant. What-if scenarios offer a powerful way to estimate the outcome of changes in the equipment configurations, shutdown intervals and durations, and maintenance policies. There are many unconventional ways of using availability modeling to improve business performance. This is where the industrial world should now make the step change.
"Doing a business case analysis without simulation is like driving with your lights off at night." (Senior Financial Analyst)
- Narayan V., Wardhaugh, J.W., Das, M.C. Case Studies in Maintenance and Reliability: A Wealth of Best Practices. New York: Industrial Press, 2012. ISBN-13: 978-0831102210.