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Big data in Maritime and Oil and Gas industries

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Presentation on theme: "Big data in Maritime and Oil and Gas industries"— Presentation transcript:

1 Big data in Maritime and Oil and Gas industries
The new data reality and Industry impact

2 In a challenging world we make businesses better prepared
Low carbon energy Short-term cost efficiency Complexity and lack of global governance Efficient and fast Businesses operate in an ever more complex context and are faced with many dilemmas: Businesses rely on reliable and affordable energy, but must contribute and adapt to a world, where fossil energy is replaced by cleaner energy. Businesses are measured on short term cost efficiency, but must make complex investment decisions balancing financial, environmental and social aspects in order to stay competitive in the long term. They must navigate in a complex regulatory landscape – with ever more complex national and regional legislation – while operating in a globalized economy lacking effective global governance. At the same time. consumers, investors and other stakeholders demand full transparency. Business must constantly adapt and enter unchartered territories to find new and smarter ways of working, while stakeholders have zero tolerance of failure, especially when it comes to safety. We enable businesses to be better prepared for making the right decisions in this complexity. Reliable and affordable energy Long-term competitiveness Trust and transparency Safe

3 DNV GL – Industry consolidation
Is now a major player in the TIC (Testing Inspection and Certification, including Classification) industry (3rd party), and a large 2nd party (i.e. consultancy/advisor) in our industries. Broadening of scope taken place through accusitions over the last years. The merger between DNV and GL was the first consolidation among the global classification societies, but each of these two organisations had also consolidated their positions within the power transmission and distribution sector (KEMA), the renewable energy sector (Garrad Hassan and KEMA - DNV GL is now the largest independent renewables consultant), oil and gas (Noble Denton and Advantica).

4 Global reach – local competence
Our global reach and density of offices around the world is another factor that enables us to take a broader view. Our 16,000 employees are spread over 100 countries and we pride ourselves of having the same high standards where ever you meet us around the world. 150 years 400 offices 100 countries 16,000 employees

5 How Big data can transform our industries
How Big data con contribute to make our industries safer, smarter and greener Some examples from DNV activities

6 Big data–some key identifiers and business value
Sources Characteristics New Capabilities Business value Volume (real time) Behavioural data Technology Target marketing Velocity (real time) Sensor data Interconnectedness and data sharing Variety (Sensor, transaction, text etc.) Competence Health diagnostics Geospatial data Veracity (VERITAS) Transaction / Contributory data Data management & Governance Logistics and optimisation

7 Big data - the new data reality
"The Internet has changed the way we consume information and talk with each other, but now it can do more, by connecting intelligent machines to each other and ultimately to people, and by combining software and big data analytics, we can push the boundaries of physical and material sciences to change the way the world works.“ General Electric CEO Jeff Immelt

8 Impacting DNV-GL’s industries – main trends
Increased number of sensors Increased ability to exploit sensor data Cross system data analytics Applications; Use of sensor data across systems for performance-, condition-monitoring, predictive maintenance, and system optimization as well as automation of problem solving and decision making Taleris is a joint venture by GE and Accenture providing intelligent operations services across systems in the aviation industry, the service uses the customers own data streams and facilitate optimizing of asset utilization and predictive maintenance of critical systems Taleris, a joint venture company of General Electric and Accenture, is operating with progressive technology assets and capability from both Accenture and GE. Taleris is dedicated to providing airlines and cargo carriers around the world with intelligent operations services focused on improving efficiency by leveraging aircraft performance data, prognostics and recovery across all onboard systems. The business case for Taleris is that they believe that they can save airlines 10 % of their unscheduled cancellations, which, for the big operators, could save them in the order of hundreds of millions of dollars. Example from Aviation: + =

9 Impacting DNV-GL’s industries
Energy Grid management Technical operation Asset and operational risk management Energy efficiency advice, assessment and verification Health Diagnostics Patient monitoring and management Management of patient care processes across system barriers Epidemiology Maritime Technical operation and maintenance Energy efficiency Safety performance Commercial operation (logistics chain optimization) Automation of ship operations Management and monitoring of accident and environmental risk from shipping traffic Integrated operations Efficient drilling and production Asset and operational risk management Pipeline risk management Oil and Gas

10 Impacting DNV-GL’s industries
Energy Health Maritime Oil and Gas Optum: The Optum One intelligent health management platform enables providers and delivery systems to improve health and manage cost. From: Clinical data of nearly 40 million patients 20 years of longitudinal claim data Claims data covering more than 109 million lives Robust socio-demographic and care management data Premier: Unlock your data to make real-time decisions Data integration, analysis and collaboration as a service

11 Industry impact – What are the patterns?
Automation of insights and decisions Increased willingness to share/combine data across barriers New entrants in established industries with new business models and operation modes Established actors transform their business models and operation modes New roles like industry specific data aggregators and data stewards emerge

12 How to succeed commercially with Big data enabled services?
Business models Operation modes Service delivery Commercialisation Capabilities

13 Big data and analytics impacting the future of shipping
Sources Characteristics New Capabilities Business value Volume (real time) Behavioural data Safer Operations Technology Velocity (real time) Sensor data Interconnectedness and data sharing Variety (Sensor, transaction, text etc.) Competence Smarter Operations Geospatial data The Big data trend Increased connectivity, new capabilities for capturing, storing, processing, presenting, and visualizing data, and transmission of large volumes of varied data at high velocity, have developed significantly in the past few years. In addition sensors have become cheaper and more commonplace, and deployment of sensors in all possible settings has increased dramatically and will continue to do so in the coming years. Systems and components will be connected across systems and locations (the ‘internet of things’) and this will enable analytics and automation across component and systems in new ways. Decisions and knowledge will gradually be automated as data and analytic capabilities are becoming available within our industries. In maritime and oil and gas industries there are vast amount of data of different kinds, but all arranged in siloes. The new connectivity, willingness (obligation) to share and the new capabilities to mange, process and visualise insights will enable data to be used in new ways across these siloes. Currently the main trends within all our industries are: Increasing use of operational data to optimize operations Use of sensor data for condition monitoring and planned maintenance Combining real time data with physical models for optimizing operation with respect to safety and environmental impact What does this mean in practice? New sources of big data from sensors to social media are capturing a wealth of information. When it comes to safety, such data can be used to augment class services with analytical insights from Class surveys, Port state inspections, Incidents, Fuel quality etc. Big data can help manage your fuel efficiency, giving you instantaneous information on your fleet performance and benchmarking it against your own fleet and history as well as other players on the market. And big data can help make the maritime sector greener by integrating information collected by sensors with environmental risk models. Veracity (VERITAS) Transaction / Contributory data Data management & Governance Greener Operations

14 Big data and analytics impacting the future of shipping
Improved maintenance levels through increased transparency Higher reliability through risk based maintenance Reduce risk related to human error through increased automation and simulation Increase navigational safety through real time analysis of shipping traffic Safer Higher Value chain efficiency through analysis of data across the logistics chain and the environment Improved Operational Efficiency through the improved value chain efficiency and optimisation of operation Smarter Big data also has the power to transform the shipping industry, making it safer, smarter and greener. Our vision for big data is to “bring the power of trusted, refined and combined data to our customers for them to gain competitive advantage through new information and insights.” Safer; Increased transparency through analysis and sharing of data across barriers will drive improvement of general maintenance levels Ability to analyse and learn from vast amounts of data across systems and industry enabling risk based maintenance, will drive towards higher reliability Reduce risk related to human errors through enabling increased automation and simulation Increase navigational safety through real time risk analysis of shipping traffic Smarter; Higher value chain efficiency through analysis of complex relationships of factors such as weather, port berth availability, cargo availability and Operational constraints Improved Operational efficiency reducing fuel costs with up to 30% through analysis of complex relationships such as operational parameters (speed, trim, resistance, engine rating and tuning etc.), weather, port availability and cargo availability (improved planning can enable slow steaming) Greener; Reduced emissions as result of the reduced fuel consumption (above), reducing emissions by up to 30% on existing vessels Reduced environmental impact from accidents due to the increased safety (above) Reduced emissions as result of the reduced fuel consumption Reduced environmental impact from accidents due to increased safety Greener

15 DNV GL: Ambitions for the shipping industry
CO2 emissions 900 million tonnes per year Lives lost at sea 900 ship accident fatalities per year Average Freight cost 7-11% of cargo value Ambition: Ambition: Ambition: 60 % reduction in CO2 emissions 90 % reduction in fatalities in shipping Maintain or reduce present freight cost levels Out of these we have selected three indicators and put ambitious targets for reduction. These ambitions are based on acknowlegded climate change targets and current safety levels in land based industries. In our view, meeting these ambitions will have the most profound impact on sustainability, and will help clarify what the industry must do to achieve sustainably. 900 people die in ship related accidents per year. A similar number of crew die in occupational accidents. The crew fatality rate is ten times the OECD average for industry workers. Our ambition is to reduce fatalities at sea by 90 per cent Shipping is responsible for about 3 % of manmade emissions. In order to reach the 2 degree target, the world has to reduce emission by 60% in 2050, according to the IPCC. Our ambition is to reduce CO2 emissions by 60 per cent The shipping industry facilitates global trade and development. Over the past decades, shipping freight costs have steadily declined, and are down to seven to 11 per cent relative to cargo value. Providing efficient transportation services is an important part of a sustainable future. Even with the increased cost of reducing emissions and accident our ambition is to maintain or reduce present freight cost levels. Vision for Big data and analytics as one of many means to achieve these ambitions: “Bring the power of trusted, refined and combined data to our customers for them to gain competitive advantage through new information and insights.”

16 Analytics and big data already impacting all service lines
14 April 2017 Analytics and big data already impacting all service lines DNV Safety Insight – benchmarking safety culture DNV Petroleum services - Benchmarking of fuel suppliers Ship classification and ship board applications –operational quality Monitoring emissions to air and managing energy efficiency Analytic and Big data services Ship emission monitoring services – monitoring emissions to air from shipping traffic Predictive maintenance based on Sensor data from components and systems (COMPASS) The interesting thing for DNV is that there is a clear indication that there are similar trends in all our core business areas to capture more data, to expect risk and environmental considerations to take the data into account and to deliver analysis and models more as a continuous service as opposed to an ad-hoc service. The dotted lines are at the stage of tenders, piloting or evaluation (wind) Wind– field assessment and monitoring and benchmarking operational quality Environment monitoring, Stewarding role for Component reliability database, Electric cars – managing battery life for vehicle fleets Utilities - load balancing and smart metering Pipeline risk monitoring

17 Safer - The power of analytics
Augmenting class services with analytical insights from Class surveys, Port state inspections, Incidents, Fuel quality etc. “This is a tool for us to stop managing on perceptions and start managing on facts”. Customer feedback

18 Safer with smarter class - standardisation

19 Safer with smarter class - Survey Findings

20 Safer with smarter class - Class survey findings – examples Machinery
14 April 2017 Safer with smarter class - Class survey findings – examples Machinery Main generator F: - Main Stator Lead cables burnt damaged between the Stator Coils and Terminal Box - Terminal bar supporting bracket iwo the Terminal box burnt. - Control transformers & accessories damaged in the fire. - Outgoing cables between the Alternator to the Main Switchboard burnt iwo the Terminal Box. Fixed Gas Detection System was found out of order. The gas detection function for ballast tanks and pump room was totally shutdown. Oveboard piping for SW cooling line of IGG scrubber found leaking i.w.o. a flange near the ship's shell (under the ER lowest platform). Jury-rigged bucket found.

21 Safer with smarter class using Big data – What data?
Online integrated performance monitoring with customers management systems Evaluate and trend barrier performance and report barrier status after survey Reporting survey findings in barrier format Analysing survey findings on entire fleet in barrier format As data gets bigger and richer through direct integrations, it is important to focus on the key questions

22 Smarter and greener through operational efficiency analytics
“You cannot improve what you do not manage.” “You cannot manage what you do not measure” Know your fleet performance instantaneously Benchmark against your own fleet and history Benchmark against other players in the market Data analytics powered by DNV GL experts

23 Creating services on public data

24 Smarter and greener through ship tracking analytics
Augmenting class services and creating new analytic services from AIS data and environmental models towards existing and new customer groups. Cutting edge Big data solution e.g. Energy savings through better voyage and speed management

25 Eco Insight illustration for home page
Engine Weather Hull/Propeller Potential reduction in fuel consumption (up to 20% of total fuel cost) Speed Trim Total fuel consumption (up to 70% of total operational cost) Basic fuel consumption

26 Vessel consumption and emission rank


28 AIS Business Intelligence
Insights can be used in daily operations, tactical planning or strategic decision making Daily operations Tactical planning Strategic decisions 1 AIS Business Intelligence Improved basis for strategic decision making Benchmarking against partners and competitors Understanding of underlying business performance drivers Network & Fleet Fuel consumption monitoring Charter fleet performance Off-hire analysis New fleet operating profile Carbon footprint profiling 2 Port operations Berth availability in next port of call Turnaround time Anchorage waiting times Change port of call 3 Voyage operations Schedule integrity versus peers Speed profiles Delay management tactics of peers ECA zone routing 4 Overall operations Average trade lane utilization Pro forma schedules (port/ anchorage/ sea passage) Cost curve modelling (fuel, asset, port, etc) 5 Purchasing (e.g. port, bunker) Recent bunkering activity in upcoming ports Choose experienced repair yard Competitor bunkering footprint AIS Business Intelligence 17. October 2014

29 Port Operations – berth availability, turnaround times etc.

30 Big Data can enable transition from rule based via condition based to risk based maintenance also in maritime and Oil&Gas Monitoring signals Diagnostics Prognostics Condition based maintenance was introduced in aviation 40 years ago. The green curve represent the exponential growth in passenger numbers in aviation present for a long time. The fatalities per year however peaked in 1980 and has steadily decreased since then. Prior to this peak, CBM was introduced gradually, represented here by the blue lines marked MSG Maintenance Steering Group. The same approach can be applied for maintenance in shipping: rule based maintenance procedures where components are replaced after a set number of hours in operation can first be replaced by condition based procedures. Ultimately a risk based approach can be used, where statistical analyses of sensor data, inspection report and other data can further optimize the maintenance procedures. This represents an analgy with the approach by google translate, where on moves from a rule based regime to a statistical or data-driven regime allowing for real-time risk-based maintenance. First, one does not start with data, but rather a problem to be solved. Then, given that we can collect the right data, and properly analyze and act upon them, we may make better decisions. The benefits are many: Increased safety and reliability Reduced number and frequency of inspections and repairs Improved spare parts exchange and logistics Reduced costs related to maintenance and downtime Preservation of asset value. As an example, a second-hand ship will be more valuable if one can prove that its current condition is tip-top.

31 Smarter – exploring data collection and diagnostic and prognostic methods
Timely maintenance based on prognosis from real time machinery sensor readings

32 Safer – New Autonomous short sea ship concept
“How far can we go with respect to energy efficiency, emissions and safety and still maintain cost effectiveness?” Safety!! Kilder vi har anslår at 85% av ulykker til sjøs er som følge av menneskelig svikt. De resterende tekniske feilene I stor grad er relatert til roterende maskineri. Vi ønsker å trigge en diskusjon på hvordan best mulig eliminere/mitigere disse ulykkene. Og selv om vi anerkjenner at det er en del kontroverser, ønsker vi å strekke strikken så langt som mulig og ønsker derfor å se nærmere på autonomitet I shipping. Vi ønsker å teste dagens teknologier som: -CAMERAS -LIDAR -RADAR -GPS -AIS -ECDIS Vi ser for oss at det vil være mange små steg på veien mot det ubemannede skip, med først beslutningsstøtte før man beveger seg mot mer og mer autonomitet. . I “The future of shipping” er en av ambisjonene å minimere antall dødsfall med 90% fra dagens nivå. Vi tror dette ikke vil være mulig uten økende bruk av autonomitet.

33 Greener – reducing environmental impact
Licence to drill and operate in sensitive areas Managing impact on the environment through sensor based Big Data platform and environmental risk models


35 Safer and smarter - Industry data aggregation – data steward
Component failure data across competitive barriers stewarded by DNV GL . “The most valuable database in the offshore industry” OREDA® is a project organisation sponsored by eight oil and gas companies with worldwide operations. OREDA's main purpose is to collect and exchange reliability data among the participating companies and act as The Forum for co-ordination and management of reliability data collection within the oil and gas industry. OREDA has established a comprehensive databank with reliability and maintenance data for exploration and production equipment from a wide variety of geographic areas, installations, equipment types and operating conditions. Offshore subsea and topside equipment are primarily covered, but onshore equipment is also included. The OREDA® data are stored in a database, and specialised OREDA® software has been developed to collect, retrieve and analyse the information.


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