May 2020

Special Focus: Maintenance and Reliability

Shell improves asset management of instrumentation through digital transformation

In May 2019, ARC Advisory Group hosted a meeting of the European Digital Transformation Council (DTC) in conjunction with the ARC Industry Forum in Barcelona, Spain.

de Leeuw, V., ARC Advisory Group

In May 2019, ARC Advisory Group hosted a meeting of the European Digital Transformation Council (DTC) in conjunction with the ARC Industry Forum in Barcelona, Spain. During the meeting, Peter Kwaspen from Shell gave a presentation on the company’s unique digitalization approach to asset management for its large installed base of instrumentation.

While these types of presentations typically remain confidential within the DTC, Shell decided to make an exception for this story, since the company believes it holds valuable lessons for all industry stakeholders.

Asset management goals

To remain competitive, industrial organizations must improve their asset availability—ideally, while also reducing their maintenance costs. However, well-established companies like Shell typically have a large installed base of aging assets, which usually require more maintenance than newer ones to maintain operational performance and safety.

In a previous project at Shell, Mr. Kwaspen had successfully developed a predictive maintenance approach for a large installed base of conventional (non-smart) instrumentation in process plants by training neural networks on process information from historians (FIG. 1).

Fig. 1. Actual and predicted values for an abnormal valve (top chart) and a normal valve (bottom chart) over 5 mos.

With the potential to reduce failures of a very large number of conventional control valves at Shell, top management decided to productize the solution to enable global rollout. In the follow-up project, the aim was to exploit another category of unused data to improve maintenance performance—the data available in smart instruments and actuators.

Shell’s asset management system

Shell’s overall asset management system (AMS) (FIG. 2) encompasses multiple processes for managing equipment, executing maintenance and ensuring safe production. The overarching goal is to ensure that activities are performed consistently, in a coordinated manner, and improved systematically to deliver excellent and sustainable business outcomes.

Fig. 2. Applications and related processes of Shell’s AMS.

One of the goals of the “manage equipment care” process within Shell’s overall AMS is to have a complete and up-to-date information repository to support risk-based decisions relative to maintenance planning and execution. This process also includes improving maintenance efficiency and effectiveness and the quality of maintenance activities by continuous analysis of equipment performance history.

The company’s “perform maintenance execution” AMS process, in turn, includes:

  • Preparation for just-in-time maintenance
  • Timely detection of asset degradation to avoid emergency and rush jobs
  • Activity prioritization and administration
  • Updating master data
  • Access to maintenance history for learning and improvement
  • Support for advanced maintenance solutions, such as predictive maintenance.

Next, the “ensure safe production” process requires an up-to-date overview of safety-critical and production-critical instruments and their health statuses. The goal here is to improve situational awareness and provide automated alerts related to abnormal situations. The process includes proactive technical monitoring and improved management of cumulative risk from the degradation of performance of multiple equipment entities.

Preventing instrument failure-related downtime

A breakdown of maintenance tasks at Shell (FIG. 3), according to Mr. Kwaspen, was very similar to the responses ARC received several years ago in a survey of 141 enterprise asset management (EAM) professionals:

Fig. 3. Categories of maintenance work orders. Source: EAM user survey, March 2015, by ARC Advisory Group.
  • 10% of maintenance tasks are condition-based or predictive
  • 30% are reactive upon failure or emergency
  • 37% are proactive inspections and preventive maintenance
  • 23% relate to project management, administration and safety.

Reactive maintenance typically results in at least some production downtime or degraded performance, resulting in unnecessary damage and opportunity cost. As ARC estimates that more than 80% of equipment failures are random, it is likely that many preventive maintenance-based jobs are executed prematurely, also creating unnecessary cost.

However, condition- or predictive-based maintenance (ideally performed just prior to the failure), could eliminate both unnecessary damage related to reactive maintenance and unnecessary maintenance costs related to preventive maintenance to help ensure nonstop production and uncompromised performance. This makes the Shell case study particularly relevant for the process industry.

Business case for condition-based maintenance. Shell performed its own business case to justify the project. However, based on publicly available data, many producers can reach the same potential net benefits.

ARC research indicates that, in the process industries, equipment failure is the primary cause for approximately one-third of the cases of unplanned downtime. Equipment includes process equipment, piping, vessels, instruments and controls. It is reasonable to assume that unplanned downtime caused by failing instrumentation accounts for at least a few percent. The weighted average of the downtime reported by users was between 10% and 15%. (This is consistent with the benchmark of 3%–5% for best-in-class organizations and 25% of downtime plus slowdowns of poor performers, as reported in a 2010 McKinsey benchmark.)

ARC used the gap in downtime between the ranges of best and average performers, a relatively broad range of 5%–12%, to estimate a target for feasible improvement. The range of opportunity cost based on this gap and a range of 3%–5% of downtime attributable to instrumentation results in realistically accessible benefits of between 0.1% and 0.6% of turnover for an average-performing company. For a hypothetical $50-B oil and gas company, the benefits could range from $50 MM–$300 MM. Given these potential benefits, it is worth investigating what it would take to improve instrument asset management. Based on its own data, Shell reached a similar conclusion.

Supporting AMS with digital information from smart assets

Mr. Kwaspen described a next possible step for improving the effectiveness of Shell’s AMS to further improve reliability and uptime. While data from smart instruments have been used for decades to support process automation system engineering and configuration, even today these data are rarely used in the operate-and-maintain phase. A survey that ARC performed jointly with the User Association of Automation Technology in Process Industries (NAMUR) found similar practices in other process industry companies.

Mr. Kwaspen believes that consistent use of performance and health data from smart instruments could improve the performance of the AMS by helping populate the asset registry. This could aid the company in detecting mismatches between instrument and distributed control system (DCS) and/or safety instrumented system (SIS) configurations and enable the instrument history to be recorded.

He anticipates that up-to-date and accurate instrument information will also improve the accuracy of maintenance instructions and other support materials and improve turnaround planning. Timely detection of asset performance degradation will reduce emergency and urgent interventions. An accurate instrument history will improve the results of analyses by maintenance experts.

Improved asset information and availability of safety- and process- critical instruments will improve situational awareness. They will also enable instrument health alerts to the asset team to be automated and facilitate incident analysis to help manage cumulative risk.

Mr. Kwaspen is confident that the instrumentation information can be used for next-generation predictive maintenance solutions based on new data analytics and machine learning tools available in industrial cloud platforms. In addition to improving asset reliability and uptime, this should help reduce maintenance costs by enabling remote access to information for analysis and remote intervention for certain types of anomalies. It should also reduce the risk of health, safety and environmental (HSE) incidents that could be harmful to personnel and/or the environment.

Linking business processes to a technical implementation

Mr. Kwaspen sees Shell’s instrument asset management system (IAMS) as a combination of infrastructure, applications, services, and adaptations of business processes. He envisions connecting smart instruments and actuators to a dedicated IAMS application.

The plan calls for a phased approach for technical installation to help manage the impact on existing business processes. In a first phase, the basic functionality of remote and smart configuration and commissioning should be applied consistently (if not already the case). Shell plans to do this using leading commercial IAMS solutions.

In a second stage, IAMS will be used to support project engineering and turnaround activities to improve plant integrity. This stage will utilize an advanced maintenance support application that will combine an instrument configuration repository, asset performance monitoring and trending capabilities, and alarming and reporting. This builds on Layer 1 and may also use data from the DCS and safety systems.

In a third phase, the IAMS will be used to support maintenance processes. This should improve maintenance execution and simplify maintenance management. More advanced functions of the maintenance support application will be used to combine process and asset information from Layers 1 and 2 to enhance decision support for maintenance and reliability. This stage could impact maintenance processes, competences, skills and habits.

In the final stage, instrument asset information will be processed automatically and integrated seamlessly into the AMS processes to enhance maintenance- and operations-related decisions. The application will be integrated with the CMMS, scheduling and other relevant enterprise applications.

Upcoming challenges will include harmonizing the many different versions and types of device descriptions required for integration. For example, text-based device descriptions (DDs) presently coexist with electronic device descriptions (EDDs) and the Windows-based, device type managers (DTMs). These device descriptions must be managed rigorously. This will be time-consuming, since while the organizations that manage the EDD and DTM standards (the FieldComm Group and the FDT Group, respectively) continue to update the technologies, they have yet to combine the two into a single field device integration (FDI) standard. ARC believes that broad adoption of FDI would create major savings for technology suppliers and end users alike.

The NAMUR NE 107 recommended standard, broadly adopted by device suppliers, provides standardized diagnostic status information for smart devices. This provides plant operators and maintenance personnel with a quick overview of their priorities to help manage their time more efficiently. Mr. Kwaspen believes that integrating the diagnostics standards into IAMS and AMS could bring benefits. If more diagnostic information could be made accessible, this would help Shell realize the maintenance and safety benefits mentioned previously.

Human factors and implementation approach

Any new system or work process must be reliable and serve as a trustworthy source for decision-making. Shell rolls out new approaches such as this on a site-by-site basis. Site managers; local process owners for the AMS processes; and maintenance, engineering, turnaround and operations managers must adopt the approach and coach personnel in its use.

The instrumentation engineers and team leads in the process automation department, along with maintenance technicians and reliability engineers, are the main users. It is important to involve them from the beginning to help ensure that everyone understands both the system and the rationale behind it and can contribute to and improve its performance.

An implementation would involve a site assessment of systems architecture, applications landscape, instruments in use and business process analysis. To this end, the site’s expectations regarding functional requirements, analyses, alerts, reports and dashboards are gathered in interviews and workshops with stakeholders. Based on the differential between the current situation and the target, a cost-benefit analysis is performed and an implementation plan is made consistent with the company’s IAMS roadmap.

Initial results

From recent experiences at several sites, Mr. Kwaspen found that anticipated benefits exceeded his expectations. For example, up-to-date and accurate instrument information did indeed enable more accurate preparation of materials and instructions for maintenance.

In large-scale turnaround programs, instrumentation disappeared from the critical path due to strategic use of IAMS capabilities. Results included a flawless startup of the site, with no single instrumentation issue. In another case, timely detection of performance degradation of on/off valves reduced emergency and urgent interventions, which led to significant improvement in reliability. This showed that the estimation of 0.1%–0.6% improvement is very conservative.

Increasing reliability while reducing maintenance cost

Reducing maintenance cost and increasing reliability is not a contradiction. World-class oil and gas producers typically have asset availabilities in the 95%–98% range, with 25%–30% reduced maintenance cost compared to the average producer. The conditions for success include structured asset management processes (similar to those described in ISO 55000), flawless execution and making the right decisions related to maintenance savings.

Shell’s journey illustrates such an approach. It is not the easiest one, as standardization of industrial device integration protocols and device parameter sets will require effort and time. However, previously unused data from smart instruments and smart valves (performance, health, and configuration data) contains a wealth of information. While some of these data could certainly be obtained using low-cost, bolt-on, IoT-connected sensors, this alternate approach would not be acceptable in Shell’s case since the IoT sensors could not be used in hazardous plant environments, would increase the company’s cybersecurity exposure and would require additional infrastructure. HP

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