Oil & Gas—a successful history of digital twin innovation
Demands for increased production coupled with the need to reduce environmental impact, increasingly hazardous and remote operating environments, and other challenges have not only driven investment in solar and wind energy, but also investment in technology—sensors, inspection robots, AI, and digital twins.1
Nextspace digital twins
Nextspace’s platform provides the data interoperability that brings multiple technologies together in one data model for analysis and visualisations to help teams understand complex situations at a glance—more informed and faster decision making.
Oil & Gas interoperability
Oil & Gas assets, facilities and operations are capital-intensive, complex, and data-rich. The safe efficient construction and operation of facilities rely on specific measurement and control.
Therefore it’s unsurprising that data unlocks the production potential of complex process facilities and enhances asset investment returns.
McKinsey calculate that the typical offshore platform runs at approximately 77 percent of its maximum production potential, an industry shortfall of 10 million barrels per day, or US$200 billion in annual revenue.
“The primary source of O&G’s performance gap is the operational complexity of production and processing facilities. Think about a crew of two or three control room operators on an offshore rig. The crew works at the center of a massive data hub. As many as 30,000 sensors continuously feed data into this hub from downhole, subsea, and topside equipment. In theory, the crew controls 200 or so operating variables, each with a multitude of different settings. In addition to the millions of possible control combinations these variables represent, the crew must also consider exogenous factors that affect production, including wave heights, temperature, and humidity.
Of course, the crew is supported by SCADA systems, simulation tools, extensive training and onshore experts. But those control systems and practices are usually calibrated to the design capabilities of the asset. They do not update dynamically. They seldom take exogenous factors into account. They typically are not updated when the asset is modified. There are substantive flaws in the tools—and in the training that offshore crews receive. Simulation tools, for instance, have only a limited capacity to process actual performance and operational data.
The complexity translates into material performance differences. Analysis of real performance data from an offshore field in the North Sea reveals more than a 5 percent difference in production output between the highest- and lowest-performing control room crews. At another asset, the difference was a staggering 12 percent. These performance data were adjusted for scheduled downtime and larger unplanned production outages.”2
Digital twins and Oil & Gas
In simple terms, a digital twin is a digital replica of a physical asset, such as a pipeline, a drilling rig, or a refinery. By creating a digital twin of an asset, companies can simulate performance and behavior under various conditions, identify potential problems before they occur, and optimize maintenance and repair schedules.
Digital twins of physical assets, processes, and systems provide a wide range of quantitative benefits to the Oil & Gas industry. Digital twin technology has already proved to be a valuable tool for Oil & Gas, with many leading companies having strong histories of investing in digital twin technology.
The oil and gas industry is constantly looking for innovative ways to use technology to increase efficiency, reduce costs, reduce hazards and improve safety.3
Digital twin technology has emerged as a powerful tool that can help achieve these goals:
Digital twins improve Oil & Gas productivity and performance, and reduced costs, waste and downtime.
Digital twins enhance asset maintenance, safety and environmental protection initiatives.
drives supply chain efficiency and site safety, and
Digital twins monitor operations, equipment, and personnel and helped predict wear and prevent costly failures before they occur.
Asset management & maintenance
One of the main benefits of digital twin technology for the oil and gas industry is the ability to improve asset management and maintenance.
By creating a digital replica of an asset, companies can monitor its performance in real-time and detect problems before they cause downtime or lead to costly repairs. This allows companies to perform preventative maintenance instead of reactive maintenance, which can significantly reduce costs and increase efficiency.
In addition, digital twins also allow for abetter understanding of the aging process of an asset, which will help in providing more accurate estimation of remaining lifetime and plan for replacement or repair.
Case studies have seen up to 50% reduction in downtime, and 20% improvement in productivity by using digital twins to optimize the performance of drilling rigs.
By creating a digital replica of an asset, companies can monitor its performance in real-time and detect problems before they cause downtime or lead to costly repairs. This allows companies to perform preventative maintenance instead of reactive maintenance, which can significantly reduce costs and increase efficiency.
In addition, digital twins also allow for abetter understanding of the aging process of an asset, which will help in providing more accurate estimation of remaining lifetime and plan for replacement or repair.
Case studies have seen up to 50% reduction in downtime, and 20% improvement in productivity by using digital twins to optimize the performance of drilling rigs.
Shell as a case study
Oil & Gas operators face multiple pressures. Global energy demand is expected to grow by 30% between 2015 and 2040.4 At the same time, the industry faces strong environmental challenges as well as operating in some of the world’s most hazardous environments.5
Shell have increasingly invested in digital twin technology from multiple software vendors. In 2017, they were the first operator to join a digital twin joint industry project.6
Shell’s goals can be articulated simply: “drive real innovation in the oil and gas sector and create a safer and more efficient industry,” Lourens Post, Shell Global Manager, Fluid Flow & Reaction Engineering.7
In 2020 Shell expanded their digital twin implementation with major initiatives across a variety of facilities. Shell CEO Ben van Beurden described the acceleration of earlier initiatives:
We further integrated machine learning into predictive maintenance activities of our refining and our deepwater assets with the potential to now roll it out across other parts of the portfolio as well. We have implemented new digital features that allow us to further optimise the inventory of materials in our refineries, and we are increasing the use of artificial intelligence to run and optimise our assets in this unprecedented environment, as well as to simulate return-to-office scenarios for it. For us, all these actions are opportunities to further build resilient operating models and optimise costs.
— Ben van Beurden, CEO, Shell8
Shell has generated multiple benefits from their digital twins:9
Continuous loading of conditions and inspection data, providing the ability to carry out structural assessments based on the “as is” condition, from anywhere and at any time.
Identifying critical areas for prioritised inspection, maintenance and repair; reducing the personnel on board the asset; reducing the necessity for physical inspections in hard-to-reach areas, such as cargo tanks; and supporting scenario planning for extreme weather events and asset modification.
Enabling safe asset life extension by replacing the over-conservative estimates made with conventional simulation software, with accurate assessments that reflect actual remaining fatigue life.
Use of high-fidelity, physics-based models and machine-learning algorithms to simulate the actual operation of a plant or asset, and generates synthetic data where measurements are not available.
Streamlining capital projects. With a plan to deliver several subsea tie-back projects over the next 10 years, Shell is implementing an integrated digital project and engineering environment, which spans project conception in the early phase design through to handover.
Streamlining capital projects processes and accelerating time to first oil by providing interoperability across owner and supply chain systems.
An end-to-end platform that provides visibility and transparency to project and engineering data across Shell’s portfolio to deliver competitive projects.
Other benefits generated by digital twins for Oil & Gas operators
Connecting assets and transferring critical specialist knowledge. BP have used digital twin technology to provide visibility into operational issues. Interrelated machine failure is captured with the visualization and analysis of interactions between machines. Human intuition and experience has assisted in the past but does not scale and is impacted when long serving specialists retire.10
Total Energies employed a digital twin to train staff virtually and identify issues before commissioning a new rig. The twin’s main function was to train engineers, technicians, supervisors, and operators to increase their competency to manage normal and abnormal situations in the lead up to first gas. The twin mimicked specific unit operations and the offshore environment. It enabled several process design issues to be identified and addressed before commissioning, while some 90 issues were spotted during configuration and corrected before startup.11
Another example is BP's use of digital twin technology to improve operations. Named APEX, it took BP just a year to scale this program up to 30 of its assets. The company says APEX has taken a systems optimization process that used to require about 24 hours down to 20 minutes. The net result delivered by APEX in 2018 was an additional 19,000 B/D to BP’s baseline production.12
Digital twin technology drives multiple other benefits across the Oil & Gas industry. Confidential results promote the following outcomes:
Digital twin goal
Environmental protection at two USA refineries by simulating and optimizing operations.
Result
Reduction of greenhouse gas emissions by 2% and improvement in energy efficiency by 3%.
Digital twin goal
Remote monitoring and real-time expert troubleshooting of North Sea offshore facilities.
Result
20% reduction in unplanned downtime.
Digital twin goal
Simulate different scenarios and identifying potential hazards in USA refinery.
Result
Reduction of the number of safety incidents by 50%.
Digital twin goal
Simulate Gulf of Mexico production platform.
Result
20% reduction in unplanned shutdowns. This resulted in an annual savings of $3 million.
Digital twin goal
Simulate and optimize operations at an Asian refinery.
Result
15% reduction in energy consumption and a 10% reduction in greenhouse gas emissions.
Digital twin goal
Ease of access to data stored by Texan operator in different places and systems.
Result
15% increase in the efficiency of their operations.
Digital twin goal
Gulf of Mexico remote monitoring and troubleshooting issues.
Result
40% reduction in the time required to resolve problems.
Digital twin goal
Identify potential problems with the drilling rigs.
Result
15% increase in drilling efficiency.
Digital twin goal
Simulate the performance pipelines under different conditions.
Result
15% increase in pipeline efficiency and 20% reduction in downtime.
Unquantified benefits abound in this industry that routinely deals with confidential information. These commonly include:
More informed, better decision making.
Identification of potential hazards, spills, leaks, environmental concerns.
Optimal scheduling of inspections and maintenance by predicting possible issues.
Optimization of designs, virtual testing of equipment under different conditions, resulting in better quality build and maintenance.
Cognite as a case study
Cognite is possibly the only digital twin platform to provide clear ROI verification.
In an industry known for the absence of verified ROI–either because it was not available or measured, or because it was deemed confidential—Cognite engaged Forrester and helped client anonymity by combining all their Customers into one case study. 30 large industrial facilities, 25,000 employees and revenue of US$2 billion/year.
Shell’s goals can be articulated simply: “drive real innovation in the oil and gas sector and create a safer and more efficient industry,” Lourens Post, Shell Global Manager, Fluid Flow & Reaction Engineering.
The result is a definitive win for advocates of digital twins.
400
%
Return On Investment
$21.56m
net present value return
Prior to using Cognite Data Fusion, interviewees noted how their organizations were digitally immature, experiencing challenges with siloed data, and were therefore unable to leverage data to optimize production, reduce costs, or improve efficiency.
Quantified benefits
Three-year, risk-adjusted present value (PV) quantified benefits for the composite organization include:
Incremental productivity savings of $1.5 million due to improved SME efficiency.
Cognite Data Fusion empowers industrial data scientists by operationalizing and contextualizing data as well as by providing much greater data accessibility and visibility, transforming the way data scientists and SMEs are collaborating.
Benefits of $4.8 million arising from reduced shutdown time.
The opportunity cost of large, industrial assets being out of production is significant. Using a digital twin and better component data visibility, SMEs are able to safely minimize shutdown periods when data anomalies arise.
Real-time data access enables a 1% improvement in productivity.
Live data access enhances operational flexibility and decision- making by increasing site safety, improving predictive maintenance, and raising machine performance.
Optimizing planned maintenance results in savings of $4.3 million.
Cognite Data Fusion creates contextualized data to optimize planned maintenance by analyzing and interpreting available resources, workflows, and component lifecycles.
Energy efficiency savings of $5.1 million.
Intelligent data can be used to reduce energy use and therefore operational costs.
Benefit gains of nearly $9 million from optimizing heavy machinery.
The largest benefit arising from the deployment of Cognite Data Fusion is through the optimization of heavy machinery and industrial processes.
Unquantified benefits
Benefits that provide value for the composite organization but are not quantified in this study include:
Health and safety.
There was evidence from customer interviews that organizations mapping incidents to facilities could reduce the amount of human movement through potentially dangerous “hot” areas, reducing risks to employee health and safety.
Environmental, social, and governance (ESG) reporting.
Some organizations were starting to develop use cases to examine how to contextualize data to facilitate ESG reporting.
Costs
Three-year, risk-adjusted PV costs for the composite organization include:
Implementation costs of $241,000.
Deployment and integration of the Cognite platform requires both internal and external resources.
Subscription fees of $3.2 million.
Cognite Data Fusion has a subscription-based licensing model centered on a range of factors including the number of industrial sites, the types of data to be fused, and the number of data fusion services being deployed. Over the three-year study period, subscription fees total $3.2 million.
Operating costs of $2 million.
These costs relate to the development of new use cases and scaling proven solutions to new facilities.
The representative interviews and financial analysis found that the composite organization experiences benefits of $26.95 million over three years versus costs of $5.4 million, adding up to a net present value (NPV) of $21.56 million and an ROI of 400%.
Citations
- “Digital Transformation at BP is Starting to Add Up to Billions” by Trent Jacobs, Journal of Petroleum TechnologyVisit link
- “Why oil and gas companies must act on analytics” by McKinsey & CompanyVisit link
- “The rise of the robots” by BPVisit link
- “Floating LNG” by Shell GlobalVisit link
- “Shell suspends production at Prelude FLNG after fire breaks out” by Offshore EnergyVisit linkand “Deepwater Horizon—BP Gulf of Mexico Oil Spill” by EPAVisit link
- “Shell joins digital twin JIP” by Offshore EngineerVisit link
- “Shell joins digital twin initiative for offshore oil and gas assets” by Internet of BusinessVisit link
- “How Shell is fleshing out a digital-twin strategy” by Cliff Saran, Computer WeeklyVisit link
- “How Shell is fleshing out a digital-twin strategy” by Cliff Saran, Computer WeeklyVisit link
- “BP and Baker Hughes Go Live with Big Analytics on All Gulf of Mexico Platforms” by Trent Jacobs, Journal of Petroleum TechnologyVisit link
- “Digital Transformation at BP is Starting to Add Up to Billions” by Trent Jacobs, Journal of Petroleum TechnologyVisit link
- “Digital Twins Mature in the North Sea” by Matt Zborowski, Journal of PetroleumVisit link
Industries
When implemented correctly, digital twins deliver significant ROI. This is why more industries are building digital twins into their core asset and operational management processes.
Our platform
Data-first digital twins built on Nextspace are customizable and extensible. Our platform helps you integrate, federate, and futureproof valuable data.
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