Simple analytics for safety and cost reduction
Management around the world is about to realize that digital twins in combination with sensor data can be used to reduce unplanned corrective maintenance by the execution of preventive maintenance before failures occur. Technology for maintenance optimization has been used with success for systems that are easy to maintain. Technology maturing is required before it can be fully implemented for oil and gas wells and subsea structures. How can well and subsea engineers perform safety and cost analyses in the meantime?
Article by Asbjørn Andersen. Originally published on LinkedIn Pulse
An important component in oil and gas wells, both from a safety and cost point of view, is the downhole safety valve (DHSV). The valve is normally in an open position and must be tested regularly to prove its functionality. The test provides condition data like valve closure time, closure force and leak rate in closed position. Sensors can in addition indirectly provide environmental condition data for the DHSV, like flow velocity, temperature, pressure, H2S concentration, ion concentrations and particle concentration. Such data can be used to foresee certain failures, but current data collection processes and algorithms are not developed to a level that makes it possible to perform accurate predictions.
Well and subsea structures have traditionally simulated safety and costs related to unplanned events based on reliability data. The predictions are based on a probabilistic model presenting most likely values and related uncertainties. Such predictions are always preferred over no predictions at all. A prerequisite is the collection of inventory and failure data, which is not always a prioritized task among management unless the data collection can be fully automatized. Current technology is still not able to categorize failure data from free text with required quality.
While we are waiting for fully digitized solutions for data collection, well and subsea engineers should have access to a simple solution, including data collection templates adjusted for specific components, a simple tool providing reliability numbers and simple digital twins for system safety and cost analyses. These products should be reasonable priced to ensure that as many engineers as possible can buy the tools and start to experience the power of data.
A professional reliability management system (RMS) spreadsheet with extended use of macros is a great alternative to a professional RMS software when used by a limited number of people and for a limited number of components. The spreadsheet has one sheet for the inventory and failure data and one sheet for the analytics. Each line in the inventory and failure data sheet represents one component. Inventory data is located to the left and failure data to the right. The inventory data can easily be imported from other spreadsheets or be punched in manually. Only operational data is collected. Embedded sorting and filter functions in the spreadsheet are used as filter for both inventory and failure data. Only the filtered data is transferred to the analytics. This is an incredible easy, flexible and powerful way of making a data filter. It is also independent of the number of collected design features. The spreadsheet has simple analytics with graphical presentation of basic component reliability numbers.
When the RMS system is in place, one must establish the digital twins. These models can also be made by spreadsheets if fault trees and Monte Carlo simulators are not available. Simplified calculations that could be performed by spreadsheets are:
> System reliability
> External leak probability analysis
> Estimated intervention costs and production downtimes related to unplanned failures
> Optimization of component replacement interval
> Optimization of system replacement interval
> Optimization of test intervals
> Optimization of intervention strategies
These spreadsheets are perfect substitutes for professional software when a ballpark estimate is required. The spreadsheets can also be used as validation models for advanced simulations.
The tools discussed above are used by the oil and gas industry, but can also be applied in sustainable industries like offshore wind mills, water power plants, hydrogen production plants, geothermal power plants and offshore fish farming.
Please contact ExproSoft for further discussions and adaption of professional spreadsheets for your needs. ExproSoft can also guide you step-by-step through the process of performing quantitative analyses and how raw data sets should be filtered differently for different analyses to adapt to the purpose of each specific analysis.