While offshore exploration continues to uncover new reserves to be developed, oil recovery techniques have extended the lifespan of existing fields, in turn maximizing the lives of the assets related to them.
But this creates a new challenge, namely understanding an asset’s remaining service life in the field.
Operators must evaluate the challenges associated with maintaining existing facilities in service. These challenges are more severe on deepwater fields and include structural and mechanical integrity of the assets; impact caused by refurbishment upgrades and repairs; efficiency of existing machinery and systems; and obsolescence of equipment and required replacement costs.
In this article, ABS—a provider of classification and technical advisory services to the marine and offshore industries—reviews the role technology is increasingly playing in helping operators use data to help better manage their offshore assets.
The structural integrity of the floating assets used in deepwater fields as well as their mooring system integrity are critical to a successful life extension. The original design of these assets considers a set of assumed conditions that include design fatigue life and strength parameters as well as corrosion characteristics.
The evaluation of these assets for life extension must consider the environmental events that the asset has been exposed to, the loading and operational conditions, the actual condition of the structural elements and the modifications and upgrades made throughout the operational life of the unit that may impact the strength and fatigue characteristics. It also needs to take into account corrosion and other degradation identified during life-cycle inspections.
Critical to this is the evolution of technology and its potential to increase performance and productivity through its ability to analyze an asset’s condition and operational performance.
Using a data ecosystem
Many of the critical answers to asset management already lie within an asset’s data. What emerging technology is allowing us to do is trap, harvest and analyze these data in one place to provide greater clarity and understanding.
Key questions when looking at asset longevity and performance will include:
- Where are emergent reliability risks?
- What are my corrosion degradation patterns?
- Which assets have abnormal operations and why?
- What is the condition of my critical structural connections?
- What is the impact of operational variation on my asset’s health?
- Are there patterns in my spare parts consumption?
- Which of my processes have reliability issues?
- And, ultimately, is my asset performing at optimal level?
So where do we start?
It all starts with collecting the data that will often already have been processed and analyzed either electronically or on paper from the original design information, the engineering assessments and analysis, the inspection records and survey results.
Environmental data may be acquired from industry sources or measured onboard. Operational data (e.g., loading patterns, production profiles, failure modes and maintenance data) are also available either measured manually or through monitoring systems. Data are also generated from repairs, maintenance, warranty claims, case finding, CMMS data as well as generally available sources such as ocean condition information.
The key is to maximize the use of these data—processing, analyzing and offering the conclusions that will greatly benefit the operator. Traditionally all of these data have been kept in different silos. Only by combining these data sets from multiple sources with modern data management technologies, can you make better decisions exercising Big Data analytics, which is the concept of using different data sources to lead you to insights.
What this also enables is a shift from a calendar-based maintenance approach to a condition-based approach. The goal in moving to a condition-based system is letting the condition of the asset (e.g., a pump or an FPSO hull structure) tell you how often you need to inspect and maintain.
As part of the condition-based class approach, ABS has defined a digital asset framework that lays out the capabilities that support the end-to-end process—from data collection and pre-processing to a digital twin that maintains and analyzes the data, to visualizations and analysis outputs that support risk-based decision-making.
Recognizing the value of incorporating digital technology, data analytics and advanced inspection technologies helps us prepare for current and future challenges through a streamlined class experience and a closer alignment with ongoing operation and maintenance activities.
It leverages data and digital technologies to provide greater insights into the condition of the asset to optimize inspection activities, support classification decision-making and provide greater flexibility than the traditional prescriptive inspection requirements.
This approach builds from the same concepts as the risk-based inspection (RBI) regime that has already been developed and implemented by the industry.
RBI determines the inspection intervals and scope based on the calculated risk profile of the individual elements in addition to other historical data. The condition-based class approach incorporates digital technologies to improve RBI processes and promotes a more holistic inspection and maintenance system.
The digital tools available today enable us to ingest, store, track and analyze condition data in a way that was never possible before.
Effective application of these digital tools are focused on three primary goals:
- Improving asset reliability;
- Minimizing the intrusiveness or impact of the class process on offshore operations; and
- Supporting the improved profitability of the industry.
Tools such as digital twin, or a digital asset framework can be used to support improved, condition-based asset management.
Data science drives informed decision-making
The first stages of the digital asset framework is data collection and pre-processing.
A digital twin utilizes various data received from the offshore asset as well as data from the environment in which it is operating to build a virtual representation of the physical object. The data are collected over the life of the unit, from the early days of basic design through the operational phase.
Examples of data to be collected include original design documentation, modification records, inspection records, environmental data, operational data and sensor data.
Digital twin allows deeper insight throughout asset life cycle
At the center of the digital asset framework is the digital twin, which is the virtual representation of the physical asset as well as its associated processes, systems and information.
The digital twin is continuously updated throughout the asset life cycle through a combination of the collected data, engineering models and data analytics.
The digital twin provides a platform for information management and collaboration, where subject matter experts, managers and all other decision-makers share a common understanding of the asset’s current condition to support the decision-making process.
By enhancing information management and collaboration, a digital twin can enable
- A single source of truth for all relevant data, analyses and models available at any time and kept current throughout the life cycle of the asset;
- Controlled sharing of data, models and updated asset information between stakeholders;
- Support decision-makers with operational decisions by providing near-real-time conditions of the assets and the capability to simulate possible future states based on planned operations; and
- Inform maintenance and inspection planning based on actual asset state and risk profile.
Together these processes allow the continuous degradation of the asset to be clearly understood, forming a solid base for a life extension evaluation.
Using predictive analytics to enable condition-based inspections
Once a single source of truth has been established for the asset, it can be used to better forecast the evolution of future risks. Data science enables the development of a predictive analytics process that supports the ultimate goal of shifting from the calendar-based, prescriptive inspection regime to a data-informed, condition-based inspection model.
Some of the characteristics of this predictive model include
- Degradation forecasting: using acquired data to predict the degradation levels; and
- Forward-looking decision-making: using analytics models to predict degradation levels.
A decision to extend the service life of an asset is now supported by the unit’s own data. A decision can be made using the forecasting tools, which will enable a clear vision of the remaining life and the suitability for additional service beyond original design life.
Data analysis guides a targeted inspection regime
To address any critical elements identified during the service life of the unit that require attention based on the predictive analysis, the final stage of the framework is to use the analysis of the data to develop an inspection regime targeted to those elements.
The degradation forecast and associated determined risk provides guidance on the scope and frequency of the inspections as well as inspection sequencing.
The benefits of this approach include:
- Increased availability and flexibility by increasing operational availability of the asset by better aligning inspection activities with the asset operations;
- Improving efficiency and productivity by using digital capabilities to automate portions of the workflow;
- By focusing on the critical elements determined by the data analysis and risk assessment, operators may be able to reduce the impact of inspections on production;
- Using the results of data analysis, maintenance activities requiring technicians on board will benefit from a streamlined and focused inspection regime; and
- Materials and logistics management system: additional efforts can be devoted to the development of a targeted system to organize and properly manage the need of spare parts, manufacturer’s representatives, supply boat schedule, helicopter traffic and other logistic activities.
The higher the visibility of an asset’s health to an operator, both on board and onshore, the greater the opportunity to improve performance and safety and maximize the service life of the asset.
Tools, such as the ABS Condition Manager software, can help visualize the condition of structural and machinery aspects of an FPSO. The digital solutions applied to the FPSO can impact the entire chain, from equipment and inventory to operational efficiency, including optimization of inspections and onboard activities.
Using the results of data analysis and applying these results to the unit allows the operator to assess the conditions and availability of machinery and systems, better managing the asset and in turn maintaining the asset in operation beyond its original service life.
Managing Risk with Innovative Data Techniques
Contributed by ABS Group
Operators have untapped insight into historical performance data and predictive modeling to support a wide variety of operational needs.
With the rise of data science, risk management consulting firm ABS Group is helping operators apply machine learning and advanced digital tools to extract and apply data to improve their predictions. The results are risk analysis solutions that generate higher quality information in both scope and certainty.
Asset performance management related to safety is not new, but risk management solutions that integrate key performance indicators, incident and near miss data, modeling results and subjective inputs from the workforce are propelling organizations into the next phase.
Modern data science tools are capable of extracting, integrating and analyzing previously inaccessible and siloed data. Monitoring culture and ultimately predicting safety performance are no longer impossible tasks. Use of these technologies are the vision for the future of risk management.
There are plenty of opportunities to improve data and risk management for better maintenance practices. Understanding how to seize these will enable significant improvements to safety and cost savings. And understanding how to manage operational risk using data will be key to unlocking a more reliable and sustainable operation.
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