Bad Actors in the Oil, Gas & Petrochemical industry
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Bad actors are a hot topic in different industries, but what are bad actors? How important are they? How to identify them and, most importantly, convert bad actors into good actors?
These questions will appear when investigating the reliability of assets. Operations and maintenance are interested in knowing the bad actors to improve overall asset reliability because bad actors cost a lot of money and reduce the quality of products. However, identifying bad actors has no standard answer, and which factors to check on might get a different answer depending on who you ask.
What is a bad actor?
Bad actors are assets, like GCs and process analyzers, associated with frequent failures, unplanned downtime, and high maintenance costs. These assets have a negative impact on the organization and represent opportunities for improvement of asset performance.
How important are bad actors?
Bad actors represent a list of assets that have the most room for improvement. Generally, bad actors are caused by a design mistake or maintenance plan mismatch.
On April 20th this year, oil prices dropped from historically low to negative. At that moment, companies had to cut a lot of costs. Bad actors, directly and indirectly, cause profit loss and high maintenance costs. Saving costs is generally easier than generating extra turnover, and a good approach with bad actors is therefore valuable.
Currently, companies use preventive maintenance to maintain the performance of their assets. The maintenance team will do their daily routine to inspect the assets and avoid asset failure. When an asset suddenly breaks, the team tries to solve the problem as quickly as possible to limit the loss of profit. It is important to keep track of additional information next to executing preventive maintenance, like:
- – What is the uptime
- – How often does the asset break down
- – How much costs and man hours are associated with those breakdowns
Combining this information will provide insight into the time and material spent based on the executed maintenance.
Fortunately, there are technological tools to make daily operations easier and safer with the rise of industry 4.0, based on data analytics and machine learning algorithms. The industry is ramping up its focus on data-driven digital tools and solutions to increase efficiency and lower costs further.
There are still significant challenges for the oil, gas, and petrochemical industries:
- – Industry mindset to see the benefits of digitalization.
- – Concerns about data security
- – The current network infrastructure might not be sufficient.
- Continuous Emission Monitoring Systems
- Predictive Emission Monitoring Systems (PEMS)
- Why Emission Monitoring System?
- Why AML 4?
- AML-Mobile takes to the fied
- 4 important asset KPIs worth tracking
- 5 advantages of cloud-based asset management software
- Mean Time Between Failures, MTBF, Calculation
- Reduce TCo in the Oil& Gas and Petrochemical industry
- How much cost unplanned downtime each year?
- AML-CEMS: Continuous Emission Monitoring System
- Climate Agreement 2050
- Hint awarded a global contract Fortune 500 energy company
- Letting the data dictate your decisions
- Data is the new oil: reducing costs with predictive maintenance
- Successful maintenance program
- Sensor data quality – better efficiency in maintenance
- Energy transition goals are not simple to meet
Avoid Bad Actors Today
Avoid Bad Actors
How to identify bad actors?
Identifying bad actors provide opportunities to improve the performance of the process. Bad actors cost a lot of money in maintenance and don’t add much to regulate the performance of the process. Bad actors can be identified using the following criteria:
- – Uptime
- – Frequency of failures
- – Maintenance cost (material and time)
- – Statistical process control results (precision and accuracy)
- – Asset complexity
How to avoid bad actors?
1. Collection of data
With industry 4.0, data has a more prominent role in companies. Identification of bad actors starts with the collection of data. Currently, engineers/technicians mostly use process data, like temperature, flow, and pressure, to check if an asset is running smoothly. Other data, like diagnostic data, is barely used in most companies, which is a waste of potential because we are using complex assets but barely use their capabilities. When you use the process, diagnostic, and maintenance data of an asset, you can more accurately predict when an asset will fail.
2. Consequences of failures
An asset that measures the quantity of a product delivered to a customer or that is responsible for the quality of the product will cause a loss of profit when it is unavailable. An asset accountable for safety will cause shutdowns if it does not perform well. For these assets, it’s critical to identify if they are bad actors. The following is essential to look at:
- – Single-line equipment
- – A highly common cause of failure
- – Design imperfection, and so on
Reliability Centered Maintenance, RCM, shall be carried out for such assets. Assets identified as bad actors can be further drilled down to sub-equipment or parts.
3. Technological advancement
As mentioned before, the oil, gas, and petrochemical industry is very traditional. Still, asset reliability, key performance (like uptime), and data are topics that the industry is starting to work with. This is a good step in the right direction, but there is still much to gain. Maintenance costs will keep rising with the rising number of analyzers and the increasing complexity. Keeping up with these trends will require changing the approach to tracking the performance of the asset. Organizations have various systems, like ERPs, asset management software, reliability analytics, etc. An example of information management will be shown in chapter 5.
4. Turn data into valuable information
All data needs to be stored in a central database, where analytical employees and tools can turn data into valuable information. If companies are working with an asset management system, like AML-AMADAS, asset data will be transformed into valuable information for management. By identifying bad actors and providing key performance indicators, a manager can quickly identify problem areas or assets and make decisions on what to do next. Identification of bad actors is made by setting up a list of criteria.
Combining key performance indicators, like availability rate and reliability rate, can provide a good indicator if an asset is a bad actor. For example, setting the availability rate threshold at 90% and the reliability at 85% will provide a list of assets to spend more resources on to improve.
It’s good to understand that for each company, the criteria can be different. Operators, technicians, and engineers can define the criteria by themselves and configure them in the AML-AMADAS application. The most common criteria are Availability rate, reliability rate, routine maintenance rate, fault rate, mean time between failure, mean time between repair, and breakdown rate.
Figure 1: Condition-based maintenance on real-time data from the assets
5. Example: AML-AMADAS, an asset management solution
AML-AMADAS is a comprehensive real-time information management system designed for enterprise-wide use by technicians, engineers, and managers. It retrieves real-time data from I/O devices, either directly or through a third-party system, enabling the information to be visualized at any level of the organization. It’s an easy-to-use and flexible system to monitor, evaluate and improve the performance of online- and offline analyzers.
The primary objective of AML-AMADAS is to provide insight into the assets of a plant/facility based on actual information from the field. Automated tracking and execution of recurring tasks are done based on the field data. Problems are displayed on-screen in a clear presentation of measurements and diagnostic analysis. Lessons learned are used to improve current and future tasks while reducing investigation and detection times. Based on a set of criteria, engineers and technicians can see an easy way how the plant is operating.
5.1. Bad actors in AML-AMADAS
Below you will see a screenshot of the AML application. In this example, the assets are working perfectly at plant B, plant north.
Figure 2: Overview of plant level
When there are bad actors, according to the predefined criteria, the engineers and technicians can see that in a very efficient and easy way. In the screenshot above, you see all the analyzers in the field. When you scroll down, you will see performance indicators and a list of bad actors. For example, if the reliability is below 85%, the analyzer/asset will appear in the bad actor’s table. In the screenshot below, seven assets have reliability below 85%, so the maintenance team knows which assets need more attention.
Figure 3: Bad actors on the plant level
Ps: this is an example of a specific functionality wished by an AML-AMADAS customer. Our development team studied the case and built the extra functionality so that all our customers could benefit from it. All our clients will receive this new functionality in the next update for free.