International Mining | BY Paul Moore

A detailed insight into Accenture’s capabilities in helping shape mining’s future in terms of advanced data analytics and machine learning

IM Editorial Director Paul Moore took some time to catch up with Constantino Seixas, an Accenture Managing Director and Latin America lead for Industry X Digital Manufacturing and Operations.

The company sees the value of a connected mine in digging deep into the wealth of data available across the entire value chain to provide integrated, end-to-end situational awareness and systemic management, and it has been one of the leading companies helping mines progress from structuring through to implementing data-led transformation with AI front and centre

Where does Accenture see the future of mineral processing in terms of the industry moving to greater use of AI and machine learning?

Data analytics is essential to mining activities. Almost all mining companies implemented data historians more than a decade ago and accumulated huge quantities of data from all production processes, including those in the mines, concentration plants, smelters, refining units and logistics – including ports. However, very few companies are using this data to its full potential yet. Data is fundamental to understanding process behaviour, identifying operating patterns, detecting where the bottlenecks are, calculating variability, predicting quality and asset failures and for root cause analysis. Absolutely every unitary process in mining will benefit from data analytics, automation and artificial intelligence (AI) technologies. If you are drilling, you can capture much more data than before, including for example on the rock hardness, based on the drill torque, and rock composition, using new instruments that are able to characterise the ore. After blasting, the fragmentation can be analysed by image processing, using drones, or by measuring rock size distribution inside the shovel bucket. This improves possibilities in optimising fragmentation and selecting the ore that will be fed into the concentration plants; that is part of the precision mining concept. Of course, all of that depends on determining the exact position of mining equipment, using a high-precision Geographical Positioning Systems (HPGPS), and having real time connectivity. Safety applications also depend on data analytics. Video analytics can detect the presence of operators in the plant red zones, workers positioned under a hanging load, and the level of fatigue of truck drivers or crane operators, to name a few examples. New sensors are used to monitor tailing dams and this data is analysed by real-time algorithms that can detect an alarm condition. The mining industry is using AI in building models to make inferences about the future state of a system, or to create diagnostics to find the root cause of an anomaly.

Constantino Seixas, Accenture Managing Director and Latin America lead for Industry X Digital Manufacturing and Operations

What processes do you through with customers to help them achieve a transformation in their data analytics using AI?

The areas in which AI can be applied are manifold. Accenture is using AI in production planning, fleet dispatching, process control, quality forecasting, instrumentation data validation, creation of virtual sensors (soft sensors), asset failure prediction, multi-plant synchronisation and product-blending optimisation, to name but a few applications. In all cases, value realisation can be measured. Achieving a good result is the expected consequence. Sustaining the result is another story and depends on defining permanent optimisation programs. Accenture also deploys data-driven consulting in its day-by-day activities. When consultants are visiting a plant, they collect data that helps to understand if there are opportunities for improvement and invariably there are. Suppose the team is visiting a concentration plant and collecting flotation plant data. By studying the variability in flotation recovery and in fine copper production, it is possible to measure the productivity gap and to estimate the benefits to be achieved from addressing this.

In some cases, the journey begins as an ad hoc problem-solving project, with the client facing a challenge to understand a quality, production or maintenance problem and we then help them deploy data analytics to solve it. From this experience, the client learns about the power of data and understands that the data they already have is an asset to be explored. Accenture can then structure a data-led transformation roadmap, showing all possible applications of AI across several key dimensions: operations, maintenance, asset management, logistics, sales, procurement and HR, to name a few. Accenture and the client can then collaborate to prioritise the projects and define a roadmap for implementation. One of the first steps in this process is the definition of the data analytics platform. It will collect data in a data lake and supply data analytics tools to extract knowledge from the data.

In general, the client already has data in a historian such as Osisoft-IP, Aspentech IP.21 or IBA, which can be used to generate immediate results. When focusing on any program, we can also detect information gaps – things that are not being measured – because there is no instrumentation, or the instrumentation is there, but there is no connectivity with the historian and the data needs to be collected manually. Part of the data-led transformation roadmap involves creating an infrastructure, involving edge computing and the cloud to collect all the necessary data for each prioritised program. Edge computing is used to process data locally. If the objective is to measure bubble size, speed, collapse rate and colour in flotation, it is better to process the information close to the site and edge is optimal for this purpose. If the objective is to use deep learning that requires more powerful processing and a GPU, the image can be sent to the cloud to be processed there.

Suppose that, during the construction of the data-led transformation roadmap, tailings dam management is selected as a program. Data from piezometers, inclinometers, water level sensors, flowmeters, radars, seismic sensors, robotic total stations, and GPRs (ground penetrating radar) will be needed to feed the data lake, as well as images collected by fixed cameras and drones, InSAR satellite information and data from geotechnical inspections. It is possible that not all this information will be available, and firms must continue to evolve their capabilities until it is possible to gather all necessary data in a reliable way, reducing manual data input.

For automated data, coming directly from instrumentation, it is common to build a data validation layer that is called a data integrity engine, on top of the data historian or one level above, to check if the acquired data is correct. Univariate and multivariate algorithms are used for data validation. To clarify, if an instrument is always indicating the same value and that measurement is frozen, the instrument would then be selected for local inspection. An algorithm that calculates the standard deviation of this measurement can detect the problem automatically. Smart instruments facilitate the diagnostic and are able to inform the user about multiple abnormal conditions. In fact, data analytics verification works in any condition, even with non-intelligent instrumentation.

Accenture supplies all services for executing this kind of program, including those associated with instrumentation, communications infrastructure, edge computing and cloud, historian and applied intelligence. We deploy data engineers with deep mining expertise who can contribute effectively, bringing knowledge from their wealth of previous experience. Most of the mining companies are also structuring internal data analytics teams inside their IT department, or as an independent entity, closer to their operations. Accenture provides a collaborative program to support those teams. This includes theoretical and on-the-job data analytics training that speeds up deployment of the client’s internal programs. Accenture also provides services to sustain those programs so the momentum won in the project phase will not be lost.

Can you give an example of how you have been able to work with clients to add value using real time data analytics?

Accenture developed a solution that identifies several abnormal behaviours in the zinc electrowinning process, based on real-time measurements. The normal way to identify a short circuit in a pot line is to detect a hot spot that represents the short circuit point where a kind of polypus will short-circuit anode and cathode plates. To make this detection the operator typically uses a manual infrared scanner and needs to walk on the top of electrolytic cells. However, Score Zinc, the system developed by Accenture, detects this as well as other potential problems, based on pot voltage, electric current and temperature measurements. This system then alerts personnel to an abnormal condition and indicates where the problem is located, saving time and reducing the fault duration. This improves the current and energy efficiency in the pot line. A model, or digital twin, is used to compare the normal and bad electrolysis behaviours and is the crux of this solution.

What unique strengths do you bring to mining and mineral processing customers and what are your differentiators?

Accenture delivers end-to-end value. We help in all project phases from conceptual design to value realisation. Given any business situation, Accenture will look for improvement opportunities. Accenture is an agnostic company, meaning that we can deploy any technology, product, or application according to client needs. We have alliances with most technology providers, and we are trained in their solution suites. Accenture can deploy any ERP or cloud platform and we have thousands of trained people in each technology. Depending on the client’s choice, it can sometimes happen that we don’t have a trained team in some product or technology in a certain geography or that all our team members are already working on another project with another client. In this case, it is very easy to train another team in a new product, because for an expert in a certain area, migration to another product brand is very straightforward.

What differentiates Accenture when supplying a solution is the industry knowledge that we bring and our internal ecosystem that allows us to benchmark our solution to what other colleagues are doing with leading clients all over the world. When we talk about doing data analytics inside a remote operations centre, for example, we bring the experience of 25 previous projects, most of them in the mining segment. We deploy all solution layers from instrumentation to ERP, including robotics. For a decade now, Accenture has been building the broadest suite of engineering and manufacturing services, which we call Industry X. We have acquired almost 30 companies to help clients with their core operations. Recent examples include Pollux, a provider of industrial robotics and automation solutions in Brazil, and umlaut, an international 4,200-people company providing engineering consulting and services.

How far off is a true autonomous mineral processing plant in your view with only minimal maintenance inputs?

This horizon for achieving a truly autonomous mineral processing plant is not very far away. Mining companies are very eager, when it comes to adopting autonomous systems. We have already seen several companies implementing autonomous drilling, autonomous hauling, autonomous train loading, autonomous stacking and reclaiming, autonomous sampling, to name but a few applications. Some of these not mature yet as solutions, but are being deployed. Autonomous ship loading will be the next big focus. Maintenance is still a laggard when compared with operations. Most miners already have condition-based maintenance in place, and several are implementing predictive maintenance that will reduce unplanned downtime and reduce maintenance costs, but it is important to emphasise that self-healing plants are just a dream right now. Mechanical equipment requires maintenance. Robots can perform some functions like exchanging fabric filter elements of a press filter, yet they are not able to replace people in all maintenance tasks, as it stands. There are very good examples of advances in automatic equipment monitoring, however.  Today, a small wireless box can be used to monitor a motor in real time and detect more than a dozen possible problems in a centralised way. 5G has also arrived, connecting everything in a mineral plant. This will drastically reduce the cost of implementation of digital solutions. When it comes to people safety, operations, maintenance and logistics, all will be positively impacted by the convergence of AI and improved connectivity.

This blogpost is originally from Aquantico | Challenge, innovate & deliver value

Riviera Maritime | BY Martyn Wingrove

Container ship managers explain why they adopt IoT, data analytics and remote diagnostics to reduce their environmental footprint.

Shipmanagers discussed their growing use of digitalisation technologies during Riviera Maritime Media’s Vessel Optimisation Webinar Week in early March 2021. Bernhard Schulte Shipmanagement (BSM) head of data governance and analytics Frank Paleokrassas explained how BSM has unlocked significant fuel savings and reduced emissions across its fleet. He said BSM saved US$5M in 2020 just from managing hull maintenance and cylinder oil consumption. “It is not that much. There is scope for a lot more,” said Mr Paleokrassas.

This could include digital twin benchmarking, automatic alert systems, predictive hull inspection recommendations, emissions reporting, performance analysis and prescriptive engine fault diagnostics.

Data telemetry is used to remotely monitor onboard system performance. “We have 50 ships with telemetry integrated with our enterprise resource planning tools,” said Mr Paleokrassas.

He expects to save even more using information for voyage optimisation, weather routeing, and improving the performance of engines and propulsion. “We are moving into prescriptive analytics and have a joint venture with Navidium,” he says. Navidium provides information and analysis on ship performance to operators.

Ships in its fleet are benchmarked for voyage, hull and propeller performance and engine operation, while BSM also monitors lubricant oil consumption. It uses a traffic-light system to identify underperforming vessels and those making the most energy savings.

“We are implementing real-time data streams and focusing on having edge computing and analytics on board our ships,” said Mr Paleokrassas.

Thome Group technical manager Rajiv Malhotra explained the benefits of engine performance monitoring and analysis. He said the focus of the group was ensuring “engine availability and reliability” on its managed ships, which number more than 200 vessels worldwide.

Engines are monitored “to minimise downtime, for energy efficiency and emissions control,” he said. Monitoring is also used to prove compliance with forthcoming environmental regulations.

Mr Malhotra went on to explain the importance of using accurate data “to achieve those objectives”. To improve operational data accuracy, shipmanagers can use validation in the system, manual screening in the office and train crew and vessel managers in its use.

Also important is installing measuring equipment such as torsion, energy and flow meters, in-line sensors and automatic data loggers to minimise human intervention in data collation.

FML Ship Management director and general manager Sunil Kapoor said monitoring vessel speed and fuel consumption ensures vessel operators can keep to their commercial contractual and efficiency requirements.

While ship operators “can avoid breakdowns” by detecting potential problems and “rectifying issues”, they can also reduce emissions, he said.

FML has developed a portal for 24/7 vessel performance monitoring. It combines data from the ship and weather information. “We can monitor and compare performance with sister ships or vessels of a similar design,” said Mr Kapoor.

He provided case studies demonstrating how this information enables FML to detect operational issues, under-performance and ways to optimise trim to improve efficiency.

IoT connectivity

Analytics, trending and monitoring tactics require fast and reliable ship-to-shore communications, increasingly through very small aperture terminals (VSAT) and IoT connectivity. One dedicated communications service is KVH Watch IoT over Ku-band networks. During Q1 2021, KVH added several data analytics and remote intervention service providers to its pack, including the Smart Ship Hub platform. This provides performance advisory and predictive diagnostics for vessel performance optimisation. It also delivers remote video-based maintenance, surveys and a wide range of related services that rely on real-time data feeds.

“IoT is a real game-changer for the industry,” says KVH Industries senior director of business development Sven-Eric Brooks. “It needs dedicated connectivity channels, isolated from crew and ship operations, to capture the benefits.”

Other application providers on KVH Watch include TechBinder’s smart vessel optimiser, Tile Marine, GreenSteam’s analytics, Kilo Marine’s V-Node platform and IoCurrent’s MarineInsight platform for IoT data acquisition, remote monitoring and real-time vessel analytics.

Mr Brooks says these services and technologies “can result in operational efficiencies, cost savings, and increased sustainability for fleets.”

GreenSteam chief executive Simon Whitford says more shipping companies are using this type of connectivity for remote monitoring and data transfers for cloud-based or onshore analytics. “There is value to be exploited and insights from the data,” he says.

Kilo Marine will use Watch to provide on-demand remote expert intervention with its technicians supporting vessel owners through IoT data acquisition and monitoring.

TechBinder will offer remote expert interventions, allowing technical troubleshooting and remote maintenance support. IoCurrents’ MarineInsight uses machine-learning algorithms to support maritime maintenance and fuel optimisation.

They join Kongsberg Digital’s Vessel Insight in KVH’s growing partnership programme. Kongsberg has its own collaboration strategy and has started offering OrbitMI’s maritime intelligence, compliance, vessel tracking and vessel performance applications to its Vessel Insight customers via the Kognifai Marketplace.

Vessel Insight collects and contextualises data from vessels enabling shipowners and operators to begin their digitalisation process. Kongsberg also added Kyma’s specialised vessel monitoring applications on Kognifai for Vessel Insight customers.

Inmarsat has built a certified application provider (CAP) catalogue for its Fleet Connect dedicated bandwidth for IoT services. Its latest addition is OneOcean, which can transmit its voyage planning software and updates over Inmarsat’s Fleet Xpress communications. OneOcean can deploy route planning services, updates and improve ship-to-shore integration of navigation information.

In March, Brightree joined Inmarsat’s CAP programme. It will use Fleet Connect to offer its marine bunker and fuel consumption monitoring application and remote engine monitoring services.

It uses Coriolis mass flow meters to accurately measure marine engine fuel consumption and bunkering transfer. Brightree’s Dandelion cloud-based remote controller transmits real-time consumption data over Fleet Connect, for fuel efficiency.

Fleet Connect ensures safety-critical navigational tools remain up to date, says Inmarsat Maritime president Ronald Spithout. “By using Fleet Connect, vessels can update mission-critical software easily and cost effectively without installing new hardware, at a time when Covid-19 continues to make ship visits especially challenging.”

This connectivity is needed as stakeholders demand more operational data. Inmarsat director for strategy and business development Alberto Perez says the “average volume of data downloaded per ship has doubled in less than six months”. It was 4 GB in April 2020 and by October 2020 it had risen to 8 GB. “A lot of this growth has been driven by welfare, but also the increase in digitalisation,” he says. Inmarsat has invested in new satellites for its Global Xpress network to support this capacity growth.

Data challenges

Despite technology developments, there remain challenges to using data effectively. Nautilus Labs senior director for strategy and insights Ross Millard explains how stakeholders have different interests. “They each have different angles,” he says. “Owners control the data, but how do they use the data and share it with other stakeholders?”

He thinks the shipping industry needs to find ways to share information across multiple parties as this will be increasingly required for emissions reporting.

“We need some type of standard if we are to run efficiently as there will be emissions pressures and others will be getting involved,” says Mr Millard. “As the industry moves forward, there will be incentives to reduce emissions.” He says there needs to be a common structure for owners, charterers and managers to work together and “align their goals with the realities of the industry”.

Digital Container Shipping Association (DCSA) provides that through its guides and standardisation. Its latest development is track and trace (T&T) standards enabling most of its member carriers to offer data access through standard application programming interfaces (APIs). These provide a streamlined way for shippers to receive real-time, cross-carrier data regarding the whereabouts of their containers.

DCSA says widespread adoption of its standards will advance the industry in terms of visibility and real-time responsiveness, resulting in greater reliability and a better customer experience. DCSA T&T comprises a downloadable information model and interface standard. Container lines are behind DCSA’s initiatives.

MSC global chief digital and information officer and chairman of the DCSA supervisory board André Simha says “While a variety of digital innovations exist in the maritime industry, MSC believes new solutions will only be fit for purpose if they can be operated across multiple carriers, service providers and geographies.”

While CMA CGM executive vice president IT, digital, SSC and transformation Nicolas Sekkaki says “DCSA digital standards will not only enable this interoperability, they will make it easier for carriers to achieve customer excellence and operational efficiency.

“But adopting standards and collaborating across the industry requires more than standards alone, it requires a cultural change in the industry which will hopefully start now.”

Yang Ming chief information officer Steven Tsao says, “With the T&T standards-based API in place, shippers will have real-time information about a container’s location and receive notification of delays.”

This blogpost is originally from Aquantico | Challenge, innovate & deliver value

Riviera Maritime | BY Martyn Wingrove

Combine engine condition monitoring with ship performance analytics to optimise the whole vessel, using data analytics to lower fuel costs, reduce emissions, minimise propulsion issues and prevent vessel downtime

These were the key conclusions from a panel of technical experts at Riviera’s How operators use data to optimise engine performance webinar. Sponsored by Aquametro Oil & Marine and Propulsion Analytics, the webinar was held on 3 March 2021 during Riviera’s Vessel Optimisation Webinar Week.

On the panel were FML Ship Management director and general manager Sunil Kapoor, Thome Group technical manager Rajiv Malhotra, Aquametro Oil & Marine international sales manager Thomson John, Propulsion Analytics engine performance manager Sokratis Demesoukas and Propulsion Analytics communications and marketing executive Zoe Lygizou-Karlou.

They discussed how owners and operators can harness engine and fuel-flow monitoring data to optimise engine power and performance.

Condition monitoring

Mr Kapoor said monitoring vessel speed and fuel consumption ensures vessel operators can keep to their commercial contractual and efficiency requirements. While ship operators “can avoid breakdowns” by detecting potential problems and “rectifying issues”, they can also reduce emissions, he said.

FML has developed a portal for 24/7 vessel performance monitoring. It combines data from the ship and weather information. “We can monitor and compare performance with sister ships or vessels of similar design,” said Mr Kapoor. He provided case studies demonstrating how these information sources enable FML to detect operational issues, under-performance and ways to optimise trim to improve efficiency.

Mr Malhotra explained the benefits of engine performance monitoring and analysis. He said the main focus was ensuring “engine availability and reliability” on managed ships. Thome technically manages more than 200 ships for owners worldwide.

Engines should be monitored “to minimise downtime, for energy efficiency and emissions control” said Mr Malhotra. Monitoring is also used to prove compliance with forthcoming environmental regulations.

He went on to explain the importance of using accurate data “to achieve those objectives”. To improve operational data accuracy, shipmanagers can use validation in the system, manual screening in the office and train crew and vessel managers in its use. Installing measuring equipment such as torsion, energy and flow meters, in-line sensors and automatic data loggers minimises human intervention in data collation.

Mr John presented the Aquametro range of onboard sensors and meters including Controil flowmeters for measuring the actual fuel consumption of the engines, power meters “and sensors for monitoring and analysing the recorded information of fuel, power and other engine parameters”. Aquametro also provides Viscomaster for fuel viscosity measurement and Homogenizer for fuel treatment to improve the fuel combustion.

“The use of high-quality sensors along with real-time monitoring and analysis strategies will provide an excellent opportunity to improve the efficiency and safety of ships and related equipment,” said Mr John. “Collecting high-quality ship data with reliable sensors will open up new ways to optimise and extend the lifecycle of the vessel according to the highest standards of operation,” he added.

Mr Demesoukas said sensors and information from bridge systems can be analysed with weather information to evaluate whole vessel performance in different conditions. “All data should be collected with high frequency, perhaps every five minutes,” he said. “Data can come from the navigation signal, such as speed over water, position and rudder angle, and from engine revolutions, power management and fuel consumption, with weather data coming from sensors on vessels.” This data is then uploaded to a cloud-based database for analysis by software.

Propulsion analytics

Ms Lygizou-Karlou introduced Propulsion Analytics’ new analysis product VesselQuad, a combination of an engine performance management suite and Quad vessel performance evaluation software. “This fusion is the most accurate vessel and engine performance assessment software,” she said. “It combines engine monitoring, machine learning, vessel performance and data analytics.”

There was general agreement from those attending the webinar that speed and consumption performance optimisation is significantly enhanced through using high-frequency auto-logged data collection using in-line sensors, such as flowmeters, shaft power meters, anemometers and speed loggers. Of those who responded to the poll question, 51% strongly agreed and 34% agreed, while just 4% disagreed and 11% did not have an opinion.

When the audience was asked which methods they considered to be the most reliable for fuel consumption measurement, 82% said fuel-flow meters, 16% said tank soundings and just 2% bunker delivery notes.

Around 94% of attendees then agreed continuous power and torque measurement of an engine were critical for optimising vessel performance.

They were then asked which of the following was the most important feature in a vessel performance monitoring system, with half (50%) of the responses for reliable measurement sensors, while 31% said it was real-time data, 12% thought it was predictions and actionable insights based on artificial intelligence and machine learning, and 7% thought user-friendly interfaces.

In another poll question, attendees were asked whether advanced engine performance monitoring technologies were able to compensate for shortcomings in crew competence in engine performance evaluation. 45% agreed with this statement, 25% strongly agreed, while 9% disagreed, 6% strongly disagreed and 15% remained on the fence.

Attendees were then asked their opinion on operational and environmental issues. They were asked which factor showed the greatest potential for improving the Energy Efficiency Operating Index (EEOI) to achieve the IMO 2030 greenhouse gas targets for carbon emission intensity.

Based on their operational experience with managed vessels, 54% said engine performance optimisation, 23% hull and propeller performance optimisation, 18% thought speed optimisation and just 5% commercial operations optimisation and better fleet utilisation.

On another question, 71% of those who responded thought EEOI was the more appropriate carbon-intensity index for assessing the carbon footprint of a vessel and 29% said annual emission ratios.

Attendees were then asked, besides engine power limitation, which other solutions did they foresee contributing the most to enable existing vessels to meet the required Energy Efficiency Existing Ship Index levels set by IMO.

45% said engine upgrades, such as modifications to turbochargers, fuel injection components, exhaust valves and control systems, for greater efficiency.

28% said propulsion improvement devices, including post-swirl and pre-swirl devices or rudder and propeller modifications, 16% thought using waste-heat recovery systems, 9% said installing shaft generators and just 2% said air lubrication.How operators use data to optimise engine performance webinar panel

Riviera’s How operators use data to optimise engine performance webinar panel were (left to right): Aquametro Oil & Marine international sales manager Thomson John, FML Ship Management director and general manager Sunil Kapoor, Thome Group technical manager Rajiv Malhotra, Propulsion Analytics communications and marketing executive Zoe Lygizou-Karlou and Propulsion Analytics engine performance manager Sokratis Demesoukas

This blogpost is originally from Aquantico | Challenge, innovate & deliver value

techxplore.com | Vandana Suresh, Texas A&M University

Texas A&M University researchers have designed a reinforcement learning algorithm that automates the process of predicting the properties of the underground environment, facilitating the accurate forecasting of oil and gas reserves.

Within the Earth’s crust, layers of rock hold bountiful reservoirs of groundwater, oil and natural gas. Now, using machine learning, researchers at Texas A&M University have developed an algorithm that automates the process of determining key features of the Earth’s subterranean environment. They said this research might help with accurate forecasting of our natural reserves.

Specifically, the researchers’ algorithm is designed on the principle of reinforcement or reward learning. Here, the computer algorithm converges on the correct description of the underground environment based on rewards it accrues for making correct predictions of the pressure and flow expected from boreholes.

“Subsurface systems that are typically a mile below our feet are completely opaque. At that depth we cannot see anything and have to use instruments to measure quantities, like pressure and rates of flow,” said Siddharth Misra, associate professor in the Harold Vance Department of Petroleum Engineering and the Department of Geology and Geophysics. “Although my current study is a first step, my goal is to have a completely automated way of using that information to accurately characterize the properties of the subsurface.”

The algorithm is described in the December issue of the journal Applied Energy.

Simulating the geology of the underground environment can greatly facilitate forecasting of oil and gas reserves, predicting groundwater systems and anticipating seismic hazards. Depending on the intended application, boreholes serve as exit sites for oil, gas and water or entry sites for excess atmospheric carbon dioxide that need to be trapped underground.

Along the length of the boreholes, drilling operators can ascertain the pressures and flow rates of liquids or gas by placing sensors. Conventionally, these sensor measurements are plugged into elaborate mathematical formulations, or reservoir models, that predict the properties of the subsurface such as the porosity and permeability of rocks.

But reservoir models are mathematically cumbersome, require extensive human intervention, and at times, even give a flawed picture of the underground geology. Misra said there has been an ongoing effort to construct algorithms that are free from human involvement yet accurate.

Machine learning algorithm

For their study, Misra and his team chose a type of machine-learning algorithm based on the concept of reinforcement learning. Simply put, the software learns to make a series of decisions based on feedback from its computational environment.

“Imagine a bird in a cage. The bird will interact with the boundaries of the cage where it can sit or swing or where there is food and water. It keeps getting feedback from its environment, which helps it decide which places in the cage it would rather be at a given time,” Misra said. “Algorithms based on reinforcement learning are based on a similar idea. They too interact with an environment, but it’s a computational environment, to reach a decision or a solution to a given problem.”

So, these algorithms are rewarded for favorable predictions and are penalized for unfavorable ones. Over time, reinforcement-based algorithms arrive at the correct solution by maximizing their accrued reward.

einforcement-based algorithms

Another technical advantage of reinforcement-based algorithms is that they do not make any presuppositions about the pattern of data. For example, Misra’s algorithm does not assume that the pressure measured at a certain time and depth is related to what the pressure was at the same depth in the past. This property makes his algorithm less biased, thereby reducing the chances of error at predicting the subterranean environment.

When initiated, Misra’s algorithm begins by randomly guessing a value for porosity and permeability of the rocks constituting the subsurface. Based on these values, the algorithm calculates a flow rate and pressure that it expects from a borehole. If these values do not match the actual values obtained from field measurements, also known as historical data, the algorithm is penalized. Consequently, it is forced to correct its next guess for the porosity and permeability. However, if its guesses were somewhat correct, the algorithm is rewarded and makes further guesses along that direction.

The researchers found that within 10 iterations of reinforcement learning the algorithm was able to correctly and very quickly predict the properties of simple subsurface scenarios.

Misra noted that although the subsurface simulated in their study was simplistic, their work is still a proof of concept that reinforcement algorithms can be used successfully in automated reservoir-property predictions, also referred as automated history matching.

“A subsurface system can have 10 or 20 boreholes spread over a two- to five-mile radius. If we understand the subsurface clearly, we can plan and predict a lot of things in advance, for example, we would be able to anticipate subsurface environments if we go a bit deeper or the flow rate of gas at that depth,” Misra said. “In this study, we have turned history matching into a sequential decision-making problem, which has the potential to reduce engineers’ efforts, mitigate human bias and remove the need of large sets of labeled training data.”

He said future work will focus on simulating more complex reservoirs and improving the computational efficiency of the algorithm.

This blogpost is originally from Aquantico | Challenge, innovate & deliver value

Mining.com | BY Martin Provencher, OSIsoft

With the coming age of Industry 4.0, it’s no longer prudent, strategic or economically smart to wait until a critical mining asset has broken down to fix the machine. Equipment breakdowns are often costly on many levels — downtime means productivity is affected, parts can be expensive and then there are the wasted outlays on labor and energy.

Data analytics paired with predictive maintenance can be a virtual goldmine for mining operations, with initial cost reduction and productivity gains of an estimated 10% to 20%.

The five stages of mining maintenance

The evolution of maintenance in the mining industry has come a long way in the last decade or so, aided by the availability of real-time data. There are five common maintenance approaches that can be applied to mine assets: reactive, preventative, condition-based, predictive and prescriptive.

Moving from reactive to preventative maintenance will help improve asset reliability, but it might not be effective as there will still be unplanned downtime and costly repairs that could have been avoided. Calendar-based maintenance often proves to be inefficient because 82% of machine failures occur at random patterns.

Condition-based monitoring, the monitoring of machines when they are still running, is the first step toward adopting a forward-looking maintenance strategy. Data can be collected online through network connectivity to sensors or offline through operator rounds or other means, depending on the criticality of the machine.

Predictive maintenance advances the condition-based approach further by using model-based anomaly detection. It relies on the online collation of sensing data and uses data analytics to predict machine reliability.

The ultimate level of maintenance, prescriptive maintenance, involves the integration of big data, analytics, machine learning and artificial intelligence. It takes predictive maintenance a step further by implementing an action to solve an impending issue, rather than simply recommending an action. 

The most important factor in the digital transformation of your maintenance strategy is access to real-time operational data. Reaching the fourth level of maintenance (predictive) is achievable now, and requires the application of advanced analytics.

Establish an operational data infrastructure

The first step is establishing an enterprise operational data infrastructure that can capture real-time data coming from sensors, manufacturing equipment and other devices, and transform it into rich, real-time insights, connecting sensor-based data to systems and people.

This first step is fundamental to providing insights for later analysis. Not only will a real-time operational data infrastructure help improve asset reliability, but having a single infrastructure in place will improve process productivity, energy and water management, environment, health and safety, quality, as well as KPI and reporting.

Enhance and contextualize data

This step refers to how data is stored and enhanced (in other words, providing context) to become information. For example, even though data is collected from a sensor, analysts need to know if the equipment is running or has stopped either due to a failure or activation of an emergency stop button. Without this context, data doesn’t have much value. Also, recognizing what data is important and relevant to an organization is equally as vital.

Implement condition-based maintenance

Implementing data involves prioritizing certain assets, identifying the conditions that lead to eventual failure and implementing those conditions on specific assets within a real-time operational data infrastructure to automate condition-based monitoring. For example, when a bearing temperature starts increasing outside of its normal operating temperature, it means the bearing will eventually fail.

Reliability engineers already know a lot of these failure patterns, which are often found through reliability-centered maintenance analysis following a failure. All of these known patterns should then be implemented as CBM inside a real-time enterprise operational data infrastructure.

Implement Predictive Maintenance 4.0

Your chosen enterprise operational data infrastructure — in conjunction with advanced analytics and pattern recognition tools — will provide real-time, actionable intelligence that empowers your business to optimize operations. Used together, these tools will automatically determine the patterns that lead to an eventual failure.

Perhaps the best way to see the benefits of condition-based and predictive maintenance is through real-life circumstances.

For example, Barrick Gold’s Pueblo Viejo, the largest producer of gold in the Caribbean, wanted to improve the asset health monitoring system of their haul truck fleet to improve maintenance efficiency and costs. The challenge involved providing real-time information of 34 haul trucks using installed systems and at minimum cost.

Prior to using condition-based maintenance, reliability and maintenance managers relied on incomplete or delayed information to make decisions.

“We used to use the in-vehicle sensors to investigate why a truck failure had happened,” Ted Olsen-Tank, a senior metallurgist for Barrick Gold, said. “Now we can be one step ahead of a failure and be more proactive.”

SINCE ITS INCEPTION IN 2018, THE PREDICTIVE MAINTENANCE PROJECT AT BARRICK’S CORTEZ MINE HAS BEGUN TO PAY OFF AS MORE THAN HALF A DOZEN MAJOR EQUIPMENT FAILURES HAVE BEEN AVOIDED

Barrick Gold saved $500,000 due to their newfound ability to detect and address failures as well as reduced the total number of failures from engine, brake or suspension issues by 30%.

Barrick is using predictive maintenance at their Cortez Mine in Nevada. The machinery involved in the gold refining process at that location is expensive, and so is downtime caused by mechanical failure. Barrick Gold invested in predictive maintenance at Cortez Mine by using sensors and machine learning to detect potential equipment problems before they escalated into failure. The machine learning algorithms use frequently collected sensor data to generate an equipment health score, which can be tracked for declines that might indicate a potential problem.

Since its inception in 2018, the predictive maintenance project at Cortez has begun to pay off quickly as more than half a dozen major equipment failures have been avoided. To put that into perceptive, a single early fault detection for one piece of equipment alone saves the company $600,000.

There are powerful economic incentives to leverage real-time data and predictive maintenance. Companies can benefit from reduced costs, open new revenue streams, extend equipment life and increase production capacity.  

(Martin Provencher is industry principal of mining, metals and materials)

This blogpost is originally from Aquantico | Challenge, innovate & deliver value

Enterprise AI | BY Todd R. Weiss

Shell, C3.ai, Microsoft and Baker Hughes are collaborating on an Open AI Energy Initiative (OAI) that aims to grow AI use across the energy and process manufacturing industries.

The OAI is envisioned by the partners as an open ecosystem of AI technologies that will provide a framework for energy operators, service providers, equipment providers and the software vendors who serve them to create new AI and physics-based models, monitoring, diagnostics and more to help solve critical industry needs, according to the group.

Some 92 percent of all facility systems shutdowns and process upsets are unplanned, which often leads to service disruptions which cause problems for customers. That is one of the industry challenges the OAI will be targeting as it works to gather and disseminate AI technologies and information that can be freely used to prevent such disruptions, the group said.

AI & data analytics to solve complex problems

As it begins its work, the OAI will first focus on using AI to improve systems reliability, uptime and performance of energy assets and processes.

Powering the OAI’s launch is the BakerHughesC3 (BHC3) AI Suite and cloud services from Microsoft Azure, which are being used to provide a broad range of capabilities to OAI users. Included in the AI Suite is a BHC3 Reliability application, which will serve as a foundation for the first work. BHC3 Reliability is an AI-based application that provides reliability, process, and maintenance engineers with AI-enabled insights that can be used to predict process and equipment performance risks.

BHC3 AI Suite’s ability to integrate enterprise-scale data from disparate data sources and train AI reliability models that cover full plant operations while taking full advantage of Azure, Microsoft’s scalable, enterprise-class cloud infrastructure. The OAL will combine the BHC3 Reliability application and Azure resources with technologies from all four partners and other leading energy companies to offer interoperable AI models, monitoring, diagnostics, prescriptive actions and services, according to the group.

AI applications and AI algorithms marketplace

Ed Abbo, of C3.ai

“What we’re enabling is the transition and or digital transformation of the energy industry,” Ed Abbo, president and Chief Technology Officer at C3.ai, told Enterprise.AI. “What that means is that we’re allowing for a marketplace of AI applications and AI algorithms that initially will start with reliability, to reduce what’s referred to as non-productive time in the oil and gas industry and in process industries.”

The OAI is being created as an open ecosystem so that other energy companies, power companies, oil and gas vendors, refining companies and software makers can participate, subscribe to the group’s algorithms and then publish their algorithms through the marketplace, said Abbo.

“Non-productive time in oil and gas is when a refinery is down because a piece of equipment isn’t working or a pump or compressor is not working,” he said. “Where AI fits in is that these are algorithms that can anticipate or predict the need for maintenance in advance of failure, to basically make predictions based on the data from sensors and prior maintenance on things that are likely to fail in the not-so-distant future that would cause downtime.”

Armed with those predictions and warnings, plant or systems operators are alerted so they can take actions to improve the operational efficiency and uptime of the facility, said Abbo.

The four partners of the OAI provide a very solid foundation for the nascent group “because it’s representative of the ecosystem that we believe will form around this initiative,” added Abbo. “The fact that Shell is in it and is publishing their algorithms [as part of the project] is highly encouraging, because that means that other oil and gas companies will follow suit. The fact that Baker Hughes, as an equipment provider and all-field services provider, is included means that others will also participate. And software vendors like C3.ai and … Microsoft [being involved] is an industry first using AI applications. I think we’ll see this ecosystem grow.”

Driving Targeted AI Changes for the Energy Sector

Dan Brennan, of Baker Hughes

Dan Brennan, a senior vice president and general manager for energy technology company Baker Hughes, said the OAI envisions driving AI to make changes that can help resolve the challenges being faced in these industries.

“The opportunity here is leveraging AI technologies to take really a dramatically different approach, and it’s what we refer to as a ‘system of systems’ approach,” said Brennan. By having scalable technology that allows operators to ingest data including maintenance records, telemetry and more, it can then be used to identify systems problems early. “It’s the word ‘early’ that’s the important thing here for the energy industry.

If you’re able to avoid or at least know within 30 or 60 days that there is a maintenance event that has to occur, there’s a tremendous amount of logistics and scheduling that has to go in there. Is there a planned maintenance window coming up? Is there an unplanned maintenance window coming up? The opportunity here for AI is really to start to get awareness early in the process where there’s potential degradation or failures coming in.”

Those problems can include pipes that are about to crack, shaft bearings that will soon fail, vibrations that are starting to ominously grow within facilities and a myriad of other potential machine failures.

“The short answer is it could be all the above,” said Brennan. “If you take the example of a refinery or petrochemical facility and really large complex facilities, they’re generally very well-instrumented today. So, there could be data that’s coming off of a condition monitoring system. Generally speaking, most of our customers have pretty mature implementations of these operational technology systems that are out there.”

A Step Forward for AI in Industry

Kevin Prouty, an energy and manufacturing insights analyst with IDC, called the creation of the new OAI a solid move.

Kevin Prouty,, of IDC

“It’s the culmination of a series of trends in all industries, but especially in oil and gas,” he said. “It’s taking an infrastructure platform (Microsoft Azure), an AI platform (C3.ai), an industry technologist (Baker Hughes), and an industry titan (Shell) and getting them all on the same AI page.”

Through the OAI, the four partners “potentially solve a lot of sticky issues that have plagued data management and AI, namely who owns the data, the models, and the IP,” said Prouty. “C3.ai and Baker Hughes have solved many of the technical issues with AI, but having a prebuilt platform that solves many of those issues will accelerate AI adoption and the push for Industry 4.0 in energy.”

The use of AI in working to solve some of the biggest problems in the energy industry is probably the most compelling application of AI today, said Prouty.

“It’s that step towards autonomous operations that Industry 4.0 has been striving for,” he said. “It’s still a way off, but getting the operating models out in the open and having non-technical issues addressed is a big first step.”

The ideas seen in the OAI initiative could be used for other industries as well, he added. “Chemical and petrochemical [initiatives] are only a short step away from oil and gas operations,” he said. Also important is that “Shell has made a significant commitment to support the initiative with its own models and analysis,” added Prouty.

This blogpost is originally from Aquantico | Challenge, innovate & deliver value

PWE Magazine

Svendborg Brakes says its newly launched Industrial Internet of Things (IIoT) solution is empowering the mining industry to boost its operations. PWE reports.

IIOT and Big data optimisation

Businesses can upgrade their existing systems to reach the next level in advanced braking control and predictive maintenance. Users can benefit from the combination of Big Data analytics, Cloud and Edge Computing to optimise equipment’s reliability and service life while reducing the cost and time associated with maintenance.

Predictive maintenance applied to critical machinery

The reliability of brake systems for key mining components, such as conveyor drive stations, bucket wheel excavators and crane applications, is crucial to maintain peak productivity and throughput. Relying on highquality brakes is a good starting point, which can be complemented by advanced data-driven technologies for real-time remote control and condition monitoring, such as the latest offering from Svendborg Brakes, a brand of Altra Industrial Motion Corp.

The solution combines IIoT and data mining technologies to offer remote, real-time monitoring and predictive maintenance to any existing or new braking systems from Svendborg Brakes’ portfolio. These can be connected to all versions of closed loop control SOBO (Soft Braking Option) systems, including the latest SOBO iQ solution. Users can also benefit from a dedicated IIoT technology to connect with alternative control systems. In all these cases, key information on braking operations and the status of its components is collected on the brake system. Key parameters include system pressure, current state of the brake and its piston, brake fluid level and temperature.

These data are pre-processed by the controller, using Edge Computing, to address time-critical tasks and support a prompt action in the brakes, such as stopping and holding as well as generally all internal processes within brake control systems. Advanced analytics is then performed in the Cloud, in order to assess the conditions of the brakes and develop key predictions to optimise their maintenance. More precisely, actual data and models on usage and wear provide an early warning when component servicing or replacement are required.

End users can access the actionable insight generated by the IIoT solution via a user-friendly condition monitoring platform. As a result, they can benefit from an immediate and comprehensive overview of the braking system. Consequently, they can also schedule accurate maintenance activities and regimes.

Ultimately, businesses in the mining sector can shift from preventative to predictive maintenance. This, in turn heavily reduces the time and cost associated with on-site inspections and maintenance activities at mining facilities, which are often in isolated, outlying locations. Also, the service lives and uptime of braking components are maximised while reducing the likelihood of unexpected failures.

The IIoT solution from Svendborg Brakes is also able to flag any unauthorised access or attempt to open the cabinet door. Therefore, it offers a tool to prevent interference or tampering with the system.

https://www.linkedin.com/company/svendborg-brakes-as/

This blogpost is originally from Aquantico | Challenge, innovate & deliver value

Engineering.com| BY Zeeshan Hussain

Major players in oil and gas invest in big data analytics and predictive maintenance solutions in the wake of increasing instability in the industry.

Eni SpA, an Italian oil and gas (O&G) multinational, recently announced a partnership with Google Cloud. BP and Amazon also agreed to further collaborations. Why are supermajors in the O&G industry collaborating with tech companies, and what benefits come out of such enterprises?

There has recently been unprecedented upheaval in the global O&G industry due to various complex challenges, including upstream volatility, midstream constraints, industry consolidation and shifting customer demands based on an increasing swing toward greener alternatives. With an unprecedented drop in demand due to COVID-19 becoming the proverbial straw on the camel’s back, it has become imperative for businesses to go on a cost-cutting drive.

Contrary to popular belief, the oil business is very technologically advanced. Cutting-edge tools are used to map out and drill into complex rock formations thousands of feet below the ground.  For example, Total S.A, the French oil major, and Eni are running two of the most powerful supercomputers in the world. Total’s Pangea has clocked speeds of more than 5 quadrillion calculations a second (yes, that is extremely fast), according to The Top 500 List.

Nevertheless, the industry has fallen behind in terms of digital transformation and leveraging real-time data. In late 2014, MIT Sloane Management Review and Deloitte scored the O&G sector’s ” digital maturity ” at 4.68 out of 10. By the fall of 2019, things got even worse, with O&G scoring a mere 1.3, easily the lowest amongst all the sectors. However, this is changing due to the perfect storm of advancements in technologies, falling costs of digitalization and lower oil prices, which may never reach the high levels previously attained. PwC has reported that O&G executives see the most potential in cloud computing, energy analytics and machine learning.

To accomplish this, big oil has finally joined other industries in the digitalization drive and turned to big tech to provide.

Data Is the New Oil

An enormous amount of data is constantly being generated by the O&G industry:

• Data volume for a single well exceeds 10 TB per day, thanks to optical fibers combined with various sensors being used in wells to record different parameters, such as fluid pressure, temperature and composition.
• A single offshore drilling rig can create over one terabyte (TB) of data per day, especially due to recent innovations in drilling tools, such as logging while drilling (LWD) and measurement while drilling (MWD).
• A large refinery generates one terabyte of data daily.
• Pipeline inspections generate approximately 1.5 TB for every 600 km inspected and ultrasounds around 1.2 TB for eight hours of scanning.
• Seismic surveys collect around 10 TB each.

But what can be done with such oceans of data? This is where big tech is providing expertise, innovation and new technologies for the digitalization of big oil.

A Match Made in Cyberspace

Within the past couple of years, Microsoft has announced collaborations with Chevron, ExxonMobil, Suncor and Petrobras. These partnerships have established Microsoft Azure as their primary cloud provider in order to harness the power of cloud computing, big data and machine learning.

Halliburton is using Microsoft’s technologies, such as machine learning and augmented reality (AR), an example being DecisionSpace365 created on Azure. It enables real-time data streaming from devices in oil fields and the ability to apply deep-learning models for optimized drilling and production, consequently lowering costs for customers. Predictive deep-learning algorithms help optimize field assets and enable exploration and deep-earth models by using software to fill gaps in sensor data while reducing the number of steps and time required for rendering models.

Suncor, a Canadian energy company specializing primarily in oil sands, is applying Microsoft’s technologies in various ways. One example is its use of fully autonomous trucks at its oil sands mines. Employees at some of their facilities wear wireless badges, allowing the company to track and analyze frontline maintenance work with the goal to improve safety and productivity.

Amazon Web Services (AWS) counts BP and Shell amongst its customers. BP in particular has significantly expanded its relationship with AWS by agreeing to supply renewable energy to power Amazon’s operations. In exchange, Amazon will help BP digitize its infrastructure and operations. This includes applications migration from BP’s own European mega data centers to the AWS cloud, in addition to collaborating on different AI and machine-learning initiatives. In this way, BP can reduce energy use and emissions from its own digital infrastructure and data centers. The resulting lower operating costs are the cherry on top of the cake.

Total and Google Cloud signed an agreement to jointly develop AI solutions. These can be applied to subsurface data analysis for O&G exploration and production, most notably from seismic studies (using Computer Vision technology) and to automate the analysis of technical documents (using Natural Language Processing technology).

Eni has also linked up with Google Cloud to construct a new digital platform, Open-es, to support sustainability in the industrial supply chain. It will be open to all players in the energy sector to pool data, best practices and sustainability models. Knowledge-sharing will set in motion an upsurge in safety and efficiency across the industry.

Schlumberger, partnering with Google, is deploying its O&G software suite, including the WesternGeco Omega geophysical data processing platform and Software Integrated Solutions DELFI cognitive E&P environment on Google Cloud Platform (GCP), to perform seismic processing, interpretation and subsurface modeling.

Digital Twins

Graphics processing units (GPUs), due to their enormous processing power, offer benefits through the concept of digital twins. A digital twin is a continuously learning virtual digital copy of all assets, systems and processes. It can predict asset behavior and capacity to deliver on specific outcomes within given parameters and cost constraints.

NVIDIA is assisting Baker Hughes in employing GPU-accelerated computing to work on platforms and build AI-enabled services for major O&G operators across the globe.

Similarly, Shell has chosen Bentley’s iTwin platform, which is an Azure cloud-based platform providing interoperability across supply chain systems. This will allow Shell to manage and analyze data, integrate with existing systems, and increase collaboration across business operations.

Data Can Predict the Future

According to research by Kimberlite, just 3.65 days of unplanned downtime a year can cost O&G companies approximately $5 million. An average offshore O&G company experiences about 27 days of unplanned downtime a year, which can amount to $38 million in losses. In some cases, this number can rise to as much as $88 million.

Driven by the Internet of Things (IoT), operations and maintenance are turning more proactive and predictive as opposed to reactive and planned. Data can be leveraged from sensors (e.g. temperature, vibration, flow rate sensors, etc.) to identify any anomalies in equipment behavior and forecast failure modes within a certain timeframe.

Predictive-maintenance solutions help O&G companies forecast equipment breakdowns before they can have an adverse impact on their safety levels and bottom line. Schneider Electric reports that applying IoT-enabled predictive-maintenance solutions can help save $4 million due to early identification of rotating machinery damage, $500,000 due to early identification of coupling failures and $370,000 due to early identification of heat exchanger valve problems.

It can also improve asset utilization and increase productivity by making operations more flexible and agile. By comparing operational data across multiple pieces of equipment, IoT solutions can help estimate machine utilization, identify the periods of best performance and establish best practices to improve performance across the entire O&G supply chain—from exploration to refining and distribution.

While the O&G sector produces 29 percent of methane emissions, the greenhouse effect of methane is 86 times higher than that of carbon dioxide. In the U.S. alone, the O&G industry releases 1 million tons of methane into the environment every year due to leakages. IoT helps identify and reduce pipeline leaks, thus decreasing environmental damage.

Augmented/Virtual Reality (AR/VR)

O&G companies are also adopting AR/VR for subsurface studies, training and simulation, maintenance and planning. Hard hats equipped with AR can project instructions for the technician directly onto the equipment to conduct an inspection or maintain a system. Instead of being dependent on manuals, AR enables this information to be delivered graphically, thus increasing efficiency and reducing errors.

VR can be used for practical training instead of in a classroom or on location. Trainees use a VR headset to enter an environment or interact with a piece of equipment virtually. During the process, it provides invaluable hands-on training at a fraction of the cost. Likewise, VR apps connected to sensors enable engineers to monitor equipment in real-time without needing to be onsite. Geoscientists are also able to visualize seismic data through VR, and even drill virtually, so that they can better determine where to explore.

Socially Distanced Drilling and Operations

The three largest service providers in this field are Schlumberger, Halliburton and Baker Hughes. Before the pandemic, O&G companies always counted on these specialists to be onsite and control drill bits and interpret real-time data. Now, to adhere to travel restrictions and social-distancing recommendations, drillers had to work from home.

For ongoing operations, during the second quarter of the year, Baker Hughes and Schlumberger both had two-thirds of their drilling activity supported by remote work. For Schlumberger, this was up 25 percent from the first quarter. For Baker Hughes, it was up 20 percent. Halliburton, the largest U.S. fracking company, has reduced the number of onsite engineers by shifting work to real-time operation centers. All of them have cited the adoption of remote work, which allowed them to shut down many operational sites, as the leading cause of significant operating cost reductions.

For drilling new wells, due to travel restrictions, many companies might not have been able to achieve their targets without the adoption of remote technology. Chevron was able to continue directional drilling in the Permian Basin of West Texas and New Mexico by setting up a remote team based mostly out of their homes in Houston because they were able to get real-time data through Azure.

ExxonMobil was able to make its Permian Basin operations totally cloud-based, leading to secure and reliable collection of live data from the oil field assets. This has allowed for quicker and more accurate decision-making on drilling optimization, well completions and personnel deployment.

There is no doubt that the confluence of technologies is beneficial not only for the O&G industry but also the whole world. It will reduce health and safety incidents and lead to better-trained personnel by allowing remote work and simulations unless absolutely necessary. Predictive maintenance will save tons of money by streamlining maintenance plans, as well as an overall reduction in the number of catastrophic failures over time. Goldman Sachs estimates that a 1 percent reduction in the O&G industry’s capex (capital expenses), opex (operating expenses) and inventory management can result in savings of about $140 billion over a 10-year period. Last but not least, all these factors will lead to considerably minimizing adverse effects on the environment, thus protecting our precious lands, oceans and wildlife.

This blogpost is originally from Aquantico | Challenge, innovate & deliver value

Maritime Logistics | BY Matthew Tremblay, ABS

Industries are adapting to an increasingly digitalized landscape. The floating production, storage and offloading (FPSO) industry is no different. As concerted efforts are made to improve project economics Matt Tremblay, ABS Senior Vice President, Global Offshore, discusses how digitalisation and data analytics can increase safety, reduce costs, and build robust technical and operational capabilities.

No one could have predicted what a challenging year 2020 would become. Offshore production activity fluctuated dramatically with changing market economics and a global pandemic, the fall out of heightened geopolitical tensions, not to mention new IMO regulations introduced at the beginning of this year.

On the horizon, however, is positive news. Markets are beginning to evolve, and an example of this is Brazil, where both Petrobras and international oil companies are again active. Increased activity is also taking place in the North Sea, and in Australia. New entrants are making strong headway in regions such as Mexico, where Petroleum Reform is opening offshore exploration and production to foreign companies.

Evolving FPSOs through the influence of digitalisation
With these positive developments, the Floating Production Storage and Offloading (FPSO) markets and the assets themselves, continue to evolve.

With the largest fleet of classed FPSOs, ABS has supported their development in both size and complexity. At its core, an FPSO is simply a production, storage, and offloading system. While the basic design concept hasn’t fundamentally changed, what is evolving are the technologies, systems, and tools available for an FPSO to optimize its design and operation.

Influencing this change are the fundamentals of how asset management can be applied to achieve leaner, cost-effective operations and reliable exploration activities. There is a primary focus on improving maintenance scheduling and performance, reducing human factor involvement, and increasing the lifetime use of the FPSO asset, safely. The importance of digitalisation is increasingly becoming a priority on the boardroom agenda of many operators, particularly with industry-wide initiatives toward net-zero carbon.

Managing complex assets

Managing the integrity of an FPSO poses a particular set of challenges, and integrity management is still often managed using outdated, labor-intensive spreadsheets or other basic systems.

However, mindsets are changing with increasing awareness on how data can be leveraged to help provide real-time answers to common maintenance, operations and performance questions.

One of the benefits of digitalisation is the increase in performance and productivity that can be achieved with a minute-by-minute visibility of how an FPSO is operating. Giving an owner-operator the opportunity to clearly track the change in asset condition over time, from construction through to late-life, helps make more informed decisions, supported by more reliable data, making the industry safer.

Considering the challenges of FPSO operations, fewer, more focused inspections that reduce the need for tanks or equipment to be physically examined while maintaining safety standards, represent a compelling proposition. As does an optimized maintenance system to improve uptime and increase reliability. Combine this, and you have a simpler way to manage maintenance crews on board, optimize turnarounds, simplify logistics, streamline the POB, and improve operations. The end result leads to safer operations, a reduction in OPEX, and improved profitability.

The trick is how to avoid adding unacceptable risk. It’s why ABS has moved to develop solutions using data science as the basis for an informed and targeted decision-making process, using predictive analytics to guide operational decisions. Examples include analysis of early corrosion detection and coating failures using machine learning and pattern identification intelligence, and real-time monitoring and transparency into how a vessel’s operational profile and loading patterns are affecting its structural integrity, providing predictive alerts for detected anomalies to reduce the risk of unplanned downtime and improvement in maintenance strategies.

The goal in moving to a condition-based system is that you are letting the condition of the asset – such as the FPSO hull – tell you how often you need to inspect and maintain it. For example, consistent hull inspections showing no corrosion may allow you to increase your inspection interval.

It starts with collecting data that will be processed and analyzed. Most of this data is something we already have, such as 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 such as loading patterns, production profiles, failure modes, maintenance data, are also available by manual intervention and measurement, or through sensor-based monitoring systems.

When you combine all this information with diagnostic and potentially prognostic models, can then detect health and performance anomalies in the form of impending failure or performance degradation at an early stage.

This provides valuable information for corrective and preventative actions by allowing both onboard and onshore management teams to observe the condition and status of their vessels’ integrity. This information empowers operators to develop appropriate strategies for maintaining their assets, optimizing decision-making, and managing integrity and maintenance as efficiently as possible to avoid unexpected downtime and productivity loss in operations.

Creating your data ecosystem

With any offshore unit, and in any operation, there are different kinds of data generated. There is data generated from the operational side, such as oil and vibration testing. There is data generated from repairs, maintenance, warranty claims, CMMS data, as well as met-ocean conditions and environment data.

All these data configurations are very diverse and were traditionally kept in silos. Today, we’re able to combine data sets from multiple sources together with technologies that help operators make better and more informed to-the-minute decisions. Combining multiple data sets generates big data analytics, which is the concept of using different data sources to create penetrating new insights.

Auditing your data and applying digital technology will automate the translation and data analysis process.

Digital solutions can then be used to visualize the status of that asset, and monitoring tools can be used to help you focus on the big picture. For example, combining data analytics with Artificial Intelligence (AI) can investigate the ‘what if scenarios’ and provide future insights, enabling operators to begin to answer not only what happened, but also what will happen.

Building a Digital Asset Framework: Digital twin

“Digital Twin” is a familiar term, but it is a term that is hard to define. For example, if you assessed 10 separate projects, each with their own Digital Twin, all 10 of them would give you a different description of what it is, what it does, and what it delivers.

This is not necessarily a bad thing, and in reality, no two projects will be the same, particularly in the design and operation of FPSOs where there are vast technical challenges that require numerous detailed process, control, safety, and flow simulations to maximize production, from subsea to offloading.

In a Digital Twin, physics-induced data are used to mirror and predict the status and life of its corresponding physical twin. This enhances the operation of an FPSO by helping to both visualize and predict the performance of that asset. As the digital twin is designed to continuously collect and process operating data from sensors and other data sources, it presents a constantly evolving picture of the FPSO’s living status at all times.

While you may not need all of the potential analysis capability available today to support a new FPSO that has just been installed, it is beneficial to deliver a new FPSO with a robust condition model to allow you to begin the full lifecycle of the model alongside the physical asset. Ultimately, it allows operators to better calculate and forecast the remaining life of their asset.

Applying Digital Twin technology as part of a “Digital Asset Framework”, allows for a single source of truth that can be shared with stakeholders, including owners, operators, project financiers, insurance providers, and regulators alike. Everybody can access the same reports, data, and insights in a way that makes sense through data visualization.

Digital-driven business outcomes

The digital solutions applied to an FPSO impact its entire value chain, from equipment and inventory to operational efficiency, including optimization of inspections and onboard activities. Coupling these digital solutions with traditional risk-based inspection and maintenance planning techniques has shown a 10:1 return on investment opportunity over the total asset life due to optimized repair and inspection planning.

In early project CapEx planning, condition models, sensor data ingestion tools, remote inspection technology, are all aspects of what should be looked at to ensure an FPSO is future-proofed, so that in 10 years from now, an operator can apply the latest predictive analytics techniques to forecast its remaining asset life.

Where operators manage a fleet of multiple offshore units, using the right digital tools and data insight offers benchmarking that helps compare one offshore unit’s performance against another in the fleet. This gives further opportunity to improve asset management activities through the efficient allocation or re-allocation of resources resulting in streamlined scheduling of fleet maintenance activities that are focused and specific.

A connected future

The transition from legacy systems to Digital Twin driven operations will be incremental, which will naturally incorporate a range of digital solutions to improve asset management and optimization.

The digital tools now entering the market allow operators to ingest, store, track, and analyze condition data in a way that was never possible before. These are technologies we are both building and implementing, focused on three primary goals; improving asset reliability, streamlining the Class process on offshore operations, and ultimately, supporting the improved profitability of the industry. Of course, this is all rooted in a framework of safety and quality spanning an end-to-end solution.

ABS has developed several innovations to support the monitoring and management of structural, machinery, and condition metrics needed for asset management and regulatory compliance.

ABS Condition Manager is just one of a suite of advanced new digital services and applications launched by ABS that offer unprecedented understanding of the status of an asset. A compartment-based, digital visualization of an asset’s condition, including inspection and repair history, as well as critical area monitoring using Class and operational data, the ABS Condition Manager application is a good example of how digital technologies can empower users with insights surrounding structural health, anomaly impacts and maintenance opportunities. The ability of Condition Manager to seamlessly integrate with onboard CMMS systems can result in up to a 50% reduction in effort to prepare annual maintenance work plans.

ABS is also exploring practical applications of AI-enabled inspection capabilities in areas such as corrosion detection, machinery performance, and monitoring, through to accumulated fatigue damage on structures based on asset-specific data, and even metocean route planning.

As we continue to move deeper into a condition-based and ultimately predictive approach, we are looking towards more sophisticated AI and using additional data from onboard sensors for advanced monitoring of vessel health, from the hull structure to whole-life integrity support with ‘live’ operation decision support.

From the design concept, through to construction, operations, and end of life, Class is connected to the asset, and the digital tools and technology that support it are transforming the future of FPSO operations.

This blogpost is originally from Aquantico | Challenge, innovate & deliver value

Plantservices.com | By Sheila Kennedy, CMRP, contributing editor

Maintenance and reliability best practices are continually improving and so are the technologies that support them. Firmly embedded in the realm of the “best” is predictive maintenance (PdM), which combines real-time monitoring of asset condition, environmental, and/or operational data with smart analytics to detect, assess, and forewarn of impending problems.

Prescriptive maintenance (RxM) takes PdM a step further by prescribing corrective actions for deteriorating conditions and including them in the alerts. RxM is a newer concept just gaining ground that is made possible by machine learning (ML), artificial intelligence (AI), and the internet of things (IoT).

“Preventing dreaded unplanned downtime provides a clear business benefit and driver for adoption of PdM,” said Ralph Rio, vice president at ARC Advisory Group. “Unplanned downtime often cascades into major losses including revenue, WIP materials, and larger equipment repair costs.”

Additionally, the costs for implementation and support of a PdM application have dramatically lowered with the IoT, cloud platforms, microservices for analytics, and wireless sensors, Rio observes. “Clear benefits and lower costs have improved financial justification and driven adoption in a wide range of industries,” he added.

The six case studies summarized below illustrate some of the numerous ways PdM and RxM are transformative.

BASF / Schneider Electric

BASF, the largest chemical company in the world, has digitalization as a corporate strategy. This includes using data to better forecast maintenance requirements and reduce unexpected shutdowns.

One such initiative involved an electrical infrastructure expansion at BASF’s Beaumont, TX, plant. Since production efficiency depends on having predictable electrical power, the plant chose to enable remote monitoring and management of its new power distribution substation’s operations and asset health with EcoStruxure Asset Advisor from Schneider Electric. The data-driven, IIoT-enabled service employs asset sensors for continuous condition monitoring along with predictive analytics to identify threats that could lead to asset failures.

BASF also has access to customized, proactive advice about how to prevent failures and improve its maintenance strategies through its partnership with Schneider Electric’s Connected Services Hub (formerly Service Bureau).

Read “How 7 companies are accelerating PdM and RxM at their plants”
The use case involved having more than 100 condition variables continually collected, measured, and computed for 63 substation assets. The asset data is monitored and analyzed from a digital dashboard that provides 24/7 insight into the substation’s global health index and specific asset statuses, from any location.

“We were surprised by some of the things that we found out. Asset Advisor is helping us to prevent catastrophic failures,” said Lee Perry, electrical design engineer at BASF. The cloud-based service “connects assets so we can look at the health of not just our electrical distribution equipment, but also the motor control centers and the motors that actually drive the process,” he explained.

“If there’s an issue, then we get an email from the Service Bureau saying, ‘We’re noticing this.’ I can log in to it remotely, look at the same information, and help troubleshoot that issue,” added Perry.

With around-the-clock access to data and expert guidance, the plant has the information it needs to make the right decisions at the right time, perform PdM to optimize asset health, and take steps to improve the efficiency of critical electrical distribution assets. These actions consequently benefit plant uptime, performance, productivity, and safety.

ALCOA / Senseye

PdM is a key part of Alcoa’s strategy to become a more stable and profitable organization. To help automate PdM and reduce downtime and maintenance costs, a proof-of-concept (POC) project involving 50 assets across two casthouse systems was conducted at its Fjar∂aál aluminum smelter in Iceland. The solution is now being expanded to at least 1,000 assets in Fjar∂aál and is scalable enterprise wide.

Senseye was chosen to unify and synchronize the equipment sensor data in the plant’s OSIsoft PI ecosystem with maintenance data in its Oracle eAM solution. From the current and historical ingested data, it automatically builds models and starts learning, without having to set up parameters or alarm levels. Isolated peaks in raw data create signatures that reveal trends and hidden failures. Predictive analytics identify what is happening and why, and can provide prognostic insights on the asset’s remaining useful life.

Operators are automatically notified of cases needing attention and can drill down for further details. It is “like having a thousand eyes in the plant” letting you know when data streams start to move away from normal, explained Árni Einarsson, reliability implementation manager at Alcoa in his presentation at OSIsoft PI World San Francisco 2020.

For instance, an idling current increase provided an indication of a fault in the HDC saw motor system. It was determined a belt guard had come loose and was in contact with the sawing drive, damaging the belt. Replacing the belts and fastening the belt cover during a maintenance shutdown resolved the issue, avoiding 12 hours of unplanned downtime.

A sensor failure detected in a coiler rod cropping shear motor prompted the discovery that a lower pinch roller sensor had come loose, leading to a sharp increase in shear motor torque. Re-fastening the sensor returned the torque to normal levels, avoiding three hours of unplanned downtime.

In total, Alcoa reduced unplanned downtime of the machinery by up to 20 percent and achieved full ROI in 4-6 months. “With the POC completed, we were able to scale the solution to other parts of the business,” said Einarsson. “The focus is on finding the best business cases to continue.”

Duke Energy Renewables / Seeq

Duke Energy Renewables, an owner/operator of wind and solar farms across the U.S., used advanced analytics and ML to automate profiling and detection of failing contactors on one of its wind turbines. “This was a trial run with one use case that was interesting, the data was available, and it seemed like a good candidate. We wanted to get up and running fast and to see if this even works for us,” said Abhi Hullatti, manager of performance analytics at Duke Energy Renewables, in his presentation at the ARC Industry Forum Orlando 2020.

Hullatti described how each turbine has six contactors that help to ensure that when the generator kicks in, it ramps up smoothly and synchronizes with the electrical grid. When any of the six contactors fail, the turbine goes offline for 2-10 days for diagnosis and repair. Turbine contactors at one site tended to fail more frequently than at others. Automating the prediction of impending failure would enable PdM, improve uptime, reduce maintenance costs, and allow better management of spare parts inventory.

Using an automated profiling tool from Seeq, a model was trained to look for the contactor fault error code and plausible leading-indicator signals (reactive power, active power, current, wind speed, rotor speed, and generator speed), to recognize what normal behavior looks like, and to provide one-hour advance notification of a fault.

The model ran against 2.5 years of cleansed signal data for the turbine and it found 12 occurrences of the error code, mostly within the few months preceding a failure. There were no false positives leading up to that outage, and no further error codes or predictions followed once all the contactors were replaced. The model can run continuously once it is validated and it will give automatic notification when something is going wrong, said Hullatti.

“The results are extremely promising, so we want to next expand this model to the rest of our turbines at that site, and also, more interestingly, look at other failure modes. That will be a lot more complex but also far more financially rewarding,” observed Hullatti. The return on the ability to catch a generator that is about to fail, and act proactively instead of reactively, is in the hundreds of thousands of dollars per event, he explained.

Global Mining Company / Uptake

A leading global mining company needed to predict and prevent failures that can cause unplanned downtime in its material transport operations. The company owns and operates a network of private railway tracks, rolling stock, and signals that move iron ore from its mining sites to the seaport. Any faults, delays, or breakdowns in the process of moving the high-value material is not only costly from an equipment maintenance and operational performance perspective, but it can also be potentially catastrophic.

The trains and rail infrastructure already produce immense amounts of data from wayside telematics devices. If harnessed, the Wheel Impact Load Detection (WILD), Wheel Condition Monitoring (WCM), Hot-Box Temperature Detection (HBD), and Bearing Acoustic Measurement (BAM) data could provide operational and condition insights into anomalies with sufficient time to take corrective actions.

The mining company chose Uptake, an industrial AI software company, to provide the necessary visibility into degrading asset conditions across the network and advanced analytics to prescribe recommended solutions. The software is now analyzing the four telematics data sources for indications of faulty wheels, wheel bearings, and axles on 10,000 rail cars.

Events are ranked according to criticality. Each high-severity wheel or bearing downtime event for a rail car costs the company at least $6,000 and requires immediate attention to return it to operation. Previously unforeseen, the mining company now has roughly a weeks’ advance notice on high-severity wheel alerts, 11 days’ notice of high-severity bearing alerts, two weeks on medium-severity alerts, and a month on opportunistic events.

With the advanced notice of issues and prescriptive alerts, the company can proactively bundle its maintenance activity, significantly reducing downtime and avoiding rail car failure. As a result, it is able to reduce its single-car unscheduled maintenance events by an estimated 50 percent, from 1,850+ per year to 925+ per year, which represents approximately $34 million in savings over five years.

Cement Plant / Aspentech

A cement plant that had struggled with cyclone blockage in its cement kilns was one of several PdM case studies shared by Chris Williams, global director of asset performance management (APM) services for AspenTech, at his ARC Industry Forum Orlando 2020 workshop. Frequent blockages in filters and cyclones limit production and increase maintenance costs, he explained. Subtle changes in the preheater process can eventually lead to blockage.

To reduce downtime and optimize maintenance, the plant sought a solution to predict and avoid cyclone blockages through early and accurate detection. Having the added benefit of RxM capabilities to resolve emerging problems quicker would minimize production losses.

The cement plant chose a PdM and RxM solution with AI and ML capabilities from AspenTech. Maestro for Aspen Mtell was used to build enhanced agents capable of predicting cyclone blockages. The failure agents trained on past blockages to identify the failure signatures that precede degradation, breakdowns, and process disruptions, and learned to recognize the pattern.

The multivariate (MV) models helped the plant to better understand the root cause of the condition and provided prescriptive guidance on feed composition and kiln operating conditions to the operators, enabling them to quickly bring the process back to normal operation. Within weeks, the solution was able to provide several days of early warning. “Eliminating just 25 percent of blockages generated over $1 million in savings,” said Williams.

Wärtsilä / Pega

Wärtsilä, a Finnish technology and service provider for energy and marine markets around the world, needed to improve its ability to process and analyze asset condition data received daily from thousands of equipment installations. An IoT engine upgrade was needed to help preempt performance issues with its customers’ power plants and vessels, and to automate feedback to each customer. Its legacy system could not sufficiently scale to handle the desired automation.

Many of Wärtsilä’s installations have multiple engines per site, and each engine has attached equipment and hundreds of sensors generating condition-based data. To better harness the global data, the company needed to map it to a normalized data structure for quick processing and apply a rules framework to assess the sensor data for anomalies. This would enable PdM practices to preempt equipment failures and also help to optimize service support.

Read “RxM: What is prescriptive maintenance, and how soon will you need it?”
The company worked previously with Pega on other projects and chose the Pega Platform to extend its existing IoT capabilities. The digital transformation platform for Digital Prescriptive Maintenance is designed to help optimize the cost and duty-cycle of devices and systems.

With the new IoT-powered solution, Wärtsilä now compiles all incoming sensor data in a data lake in the cloud, where it is normalized and fed into the Pega Platform. Using a complex rules framework, the data is processed to identify exceptions that may indicate a developing risk of engine failure or a reduction in equipment performance. Daily feedback reports document the findings and prescribe recommended actions, enabling the company to save time and money by avoiding unplanned downtime and production problems.

Benefits to the customers include improved environmental and economic performance and long-term predictability of their vessels or power plants. Wärtsilä is also benefitting from optimized equipment maintenance, improved reliability and performance, and increased sales of proactive maintenance contracts.

Each of these industry leaders is enjoying the rewards of using maintenance and reliability best practices. With the benefits of PdM and RxM being realized, the old ways of doing business seem antiquated and companies like these are not looking back.

This blogpost is originally from Aquantico | Challenge, innovate & deliver value