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The most infamous cyberattacks on industrial systems
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ttacks take place on industrial control systems (ICS) so as to undermine the integrity of processes that may lead to a malicious functional impact, according to a recent paper, “Stuxnet to CRASHOVERRIDE to TRISIS,” written by Joe Slowik of Hanover, MD-based Dragos. In these attacks, purpose-built software was leveraged as part of multi-stage attacks that not only sought to undermine system integrity and disrupt the process, but that were also meant to cause destruction or bring coercion to bear. To start, in 2010, Stuxnet was a deliberate attack on Iran’s nuclear enrichment activities, performed with complex malware. But rather than simply make centrifuges destroy themselves, Stuxnet caused infected Siemens PLCs to ensure operational degradation, while hiding the cause of the degradation. The malware increased production defect rate even as it decreased centrifuge operational life. Two distinct variants were evidenced. One made it difficult to detect over-pressure conditions in impacted centrifuges. The other alternated centrifuge rotating speeds between extremes. Eliminating the loss of view through the ICS degraded confidence in the centrifuges, whose erratic operational vectors at first remained unexplained. Crash without burn CRASHOVERRIDE was a purpose-built, semimodular malware framework used during a 2016 Ukraine power event. The event targeted electric transmission operations and produced its ICS effects by encoding process manipulation in purpose-built software. Although not exploited to its full potential, the tools involved could have caused a multi-stage event meant to cause physical destruction via a loss of protection on the impacted systems. SCADA disablement produced loss of view and control in addition to inhibiting recovery. The attackers deployed a denial of service against Siemens SIPROTEC protective relays, unleashing several layers of uncertainty. One motive for this might have been to damage
4 • FEBRUARY 2020 OIL&GAS ENGINEERING
KEVIN PARKER EDITOR
rotating equipment. However, CRASHOVERRIDE failed to work as intended. TRISIS, also known as Triton, first emerged in 2017 as a safety-focused event at a Saudi Arabia oil & gas refinery. Execution of the exploit was the final step in a long-term, multi-stage intrusion that first had to achieve access and attain information prior to enabling the ICS attack. TRISIS represents a direct effort to build an in-memory backdoor or rootkit-level functionality to allow an attacker to gain unfettered, undetected control over a Schneider Electric Triconex safety-instrumented system (SIS). That TRISIS tripped the Triconex devices within the refinery environment, seems, to Dragos, to have been unintended. TRISIS’ true goal was to “enable surreptitious access to the SIS devices while enabling arbitrary modification of SIS functionality after installation,” says Slowik. This is more complex and, for the hacker at least, more interesting functionality than simply disrupting a safety system. It could allow modifying SIS parameters to reduce response to unsafe conditions. Dragos believes it likely that intruders also had access to the distributed control system environment. Dedicated malevolence As with CRASHOVERRIDE, TRISIS represents a worrying escalation in attacker capabilities and ambitions, even though in practice those ambitions were never truly realized, says Dragos. The limted success of these attacks may cause some to question their efficacy and seriousness, but except for one or more elements failing to act as intended, the consequences could have been serious. These failures were mainly due to immature attacker understanding of ICS environments. Bad actors have subsequently demonstrated sustained commitment to alleviating these shortfalls, Slowik concludes. OG
I NSIDE
Cover: Natural gas well with piping. Image courtesy: Endress & Hauser
EDITOR’S COMMENT 4
The most infamous cyber-attacks on industrial systems
6
FEATURES 6
Detecting water carryover in natural gas Instrumentation improves control of water removal process from natural gas
10
Insidious corrosion of fixed equipment detected Pipes and vessels monitored to detect and mitigate corrosion using wireless instrumentation
14
Intelligent use of Cloud sharpens operational insight
10
Cloud-based wireless sensing enables safety, reliability and profits through widespread asset monitoring
17
Five best practices for predictive operations at scale Case study example illustrates challenges and opportunities
20
Jelec chooses lever-actuated terminal blocks Wire actuation variants suited to unique experiences
20
OIL&GAS ENGINEERING FEBRUARY 2020 • 5
PRODUCED WATER MANAGEMENT
Detecting water carryover in natural gas Instrumentation improves control of the water removal process from natural gas By Adam Booth
Figure 1: Natural gas well with piping. All images courtesy: Endress & Hauser
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atural gas, as it is produced from a well, contains water. Before transferring the gas into a pipeline for distribution, as much water as possible must be removed to eliminate carryover, with operators being alerted to any remaining water detected (See Figure 1). Let’s discuss methods used to remove water from gas and how modern instrumentation can accurately measure gas transferred into a pipeline and help detect any water carryover present after the removal process. Natural gas comes from oil, gas and condensate wells. Natural gas that comes from oil wells can exist separately from oil or be dissolved in the crude oil. Gas wells typically produce raw natural gas, while condensate wells produce free natural gas along with a semi-liquid hydrocarbon condensate. Raw natural gas contains water vapor, hydrogen sulfide, carbon dioxide, helium, nitrogen and other compounds. Crude oil, raw natural gas and free natural gas can be processed at the well site, but it’s normally piped to a central processing station that takes inputs from dozens — sometimes hundreds — of wells, and then produces clean natural gas for transfer to a pipeline. Natural gas processing consists of separating all of the various hydrocarbons and fluids from the pure natural gas (See Figure 2), to produce what is known as “‘pipeline quality” dry natural gas — that is, with no entrained water. Some of the water associated with extracted natural gas is removed by simple separation methods, at or near the wellhead. However, removal of the water vapor that
6 • FEBRUARY 2020 OIL&GAS ENGINEERING
exists in solution in natural gas requires a more complex treatment. This treatment consists of dehydrating the natural gas, which usually involves one of two processes: absorption or adsorption. Absorption occurs when the water vapor is removed by a dehydrating agent. Adsorption occurs when the water vapor is condensed and collected on the surface. Separators can go by many names such as liquid separators, oil separators, water and oil separators, etc. — but the most common separators are a two-phase unit that separates gas from liquid — or a three-phase unit that separates gas, oil, and water. Controlling separators Instrumentation (See Figure 3) and control systems need to measure and control various processes in a separator. Temperature and pressure in a separator are complementary variables and are required to correct for mass flow measurements. A separator requires strict pressure/ temperature monitoring because of process operating and safety conditions. Temperature monitoring is always required to guarantee the right conditions for the separation process. The system must control incoming flow to ensure the gas and liquid flow rates are low enough so that gravity segregation and vaporliquid equilibrium can occur. The temperature also needs to be controlled, because if the temperature of pipeline walls or storage tanks decreases below the dew point of the water vapors present in the gas, the water starts to condense on those cold surfaces. If so, natural gas in combination with liquid water can form methane hydrate that may plug valves, fittings or even pipelines. No matter the type of separator operating in an oil or gas processing facility, proper control of the fluid levels is crucial to assure stable, efficient and safe operations. To achieve this goal, repeatable level measurements are required, regardless of
the variability of the process and the characteristics of the fluids. This includes undesired conditions, such as high viscosity, presence of foam and emulsion layers thicker than 2 inches (5 cm) or dragged solids (sand or rocks). Ideally, the gas and liquids reach a state of equilibrium at the correct pressure and temperature within the vessel. Industry standards for liquid retention time of solution gas break-out are within a range of 1 to 3 minutes in horizontal separators. Although a separator is highly instrumented, controlling flow, pressure and temperature to achieve maximum production of water-free, pipeline quality natural gas is a difficult task. In many separators, data from instrumentation is sent to a flow computer, where its job is to analyze all the data and determine what variables — if any — need to be adjusted. Although a separator is meant to remove all water from the natural gas, carryover of water sometimes occurs, and this condition must be quickly detected to alert operators. A flowmeter for it Endress+Hauser recently developed a new ultrasonic flowmeter (See figure 4) to measure the flow of natural gas and other process media. The Prosonic Flow G 300/500 has built-in pressure and temperature sensors. The input from these sensors is combined with measured sound velocities to calculate the compensated flow, and the pressure and temperature measurements can be transmitted separately via one of two digital communication links: HART imposed on the flowmeter’s 4-20 mA mass flow process variable output or Modbus RS485. The flowmeter is offered with an “advanced gas analysis” software package option to calculate additional parameters and process variables. Some examples are volume flow, corrected volume flow, energy flow, calorific value, Wobbe index, gas type, molar mass, methane content (%), density, and viscosity. The Wobbe Index, incidentally, is a measure of the interchangeability of fuel gases and their relative ability to deliver energy. The software can also detect water carryover through the analysis of sensor test points such as signal strength, flow asymmetry and acceptance rate. Once detected, this condition can alert operators in one of two ways. The first is via
a built-in relay, and the second is via the digital communication link. Many separators have a mist eliminator at the gas outlet, which is essentially a wire mesh filterlike device. The purpose of it is to capture liquid (all liquids, not just water droplets) and drain it or drip the liquid into the holding part of the separator. These devices are sized based on the flow rate and liquid properties. The eliminator must be sized correctly so that it efficiently captures most of the liquid, without sacrificing flow and separator retention time. By detecting liquid carryover with the ultrasonic flowmeter described, we accomplish a few things:
Figure 2: Horizontal oil-gaswater separator.
Figure 3: This oil-gaswater separator processes crude oil. Water and oil are removed to produce pipelinequality gas.
OIL&GAS ENGINEERING FEBRUARY 2020 • 7
PRODUCED WATER MANAGEMENT The flowmeter provides much of the data and computations supplied by a dedicated flow computer, making it easier for process engineers to understand separator performance and control it more efficiently.
Figure 4: Ultrasonic flowmeter measures flow, temperature, pressure, mass flow and methane content.
1. Understand how efficient the mist eliminator is functioning in relation to gas flow rate
2. If a step change in the “moisture diagnostic variable” occurs it could indicate that the mist eliminator is damaged
3. Proper gas-liquid separation isn’t taking place and the separator performance should be evaluated. The control system can adjust dump cycle time and retention time, or adjust back pressure at the gas outlet.
Figure 5: The Prosonic Flow G has built-in pressure and temperature sensors.
Detecting liquid carryover can allow a producer to adjust the separation process. Downstream plants are designed for certain gas properties and wet gas could result in higher operating cost at the plant due to reprocessing and wasted energy.
Flowmeter details The flowmeter (See Figures 4 and 5) measures both dry and wet gases with ±0.5% accuracy, even when process and ambient conditions fluctuate significantly. It operates at process temperatures up to 302°F and pressures up to 1450 psi. All wetted parts are made of stainless steel and are compliant to the stringent requirements of NACE MR0175/MR0103. The ultrasound transducers are also available in titanium Grade 2. The flowmeter’s measuring system has been developed in accordance with IEC 61508 (SIL). It is also preferred for use in safety-related applications. It also has Endress+Hauser’s Heartbeat Technology integrated into the flowmeter, which enables permanent self-diagnostics with the highest diagnostic coverage. It also enables device verification certified by TÜV in accordance with DIN EN ISO 9001:2008 without process interruption. The flowmeter’s transmitter includes a web server as standard, enabling direct data access in the field from any device capable of hosting a web browser, such as a laptop, smartphone, or tablet. Data storage via HistoROM ensures maximum data security. Controlling an oil/gas/water separator to extract water from natural gas is difficult because the control system and flow computer can only infer water content. The new flowmeter from Endress+Hauser produces a wealth of information that instrument engineers can use to detect water carryover, and to better understand how a particular separator is working and how to improve its control. OG Adam Booth is the flow product marketing manager for Endress+Hauser. He graduated from Purdue University in 2016 with a degree in mechanical engineering technology. After graduation he joined Endress+Hauser’s rotational engineering development program in 2016. Prior to his current role, Booth was a technical support engineer for Endress+Hauser. While in those roles, he developed expertise with Endress+Hauser’s flow portfolio and gained hands-on experience in the field.
8 • DECEMBER 2019 OIL&GAS ENGINEERING
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PREDICTIVE MAINTENANCE
Insidious corrosion of fixed equipment detected Pipes and vessels monitored to detect and mitigate corrosion using wireless instrumentation By Jake Davies
Figure 1: Manual ultrasonic thickness measurements from a single point over 30-year period illustrate the type of variability often found when monitoring corrosion in piping and vessels. Image courtesy: Chevron
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iscussions of predictive maintenance in the oil & gas industry usually focus on rotating equipment such as pumps and turbines. Those are certainly valid areas of concern, but the result of a failure is usually limited to the equipment itself. On the other hand, static equipment such as piping, vessels and similar equipment is not as maintenance intensive, but a failure can be catastrophic. This equipment should also receive predictive maintenance attention. Major loss of hydrocarbon containment can result in fatalities while damaging equipment, the environment and a company’s reputation. The economic impact will often be felt long after the initial clean-up and repair is completed. Two well-documented events tell the tale: BP Cherry Point in February of 2012 and Chevron Richmond in August of 2012 both experienced containment losses caused by corrosion damage from inside piping, and major fires ensued. The threat of internal corrosion and erosion exists throughout the hydrocarbon production chain, from the wellhead, through midstream, to refinery and distribution. To avoid unplanned outages or disaster, equipment must be repaired or replaced before the metal thickness reaches a critical minimum limit. But how can operators determine when that point is approaching?
10 • FEBRUARY 2020 OIL&GAS ENGINEERING
Monitoring metal loss Traditional approaches to this challenge send technicians into the plant to take manual thickness measurements. Since sections can’t be cut open, the most common method is ultrasonic thickness measurement. This technique is nonintrusive, so it can be performed with the plant in operation, with no effect on the process or safety risk. Naturally, manual inspections incur costs for the technician to gain access to the desired measurement location, which may involve erecting scaffolding, removing insulation and so forth. Despite these costs, a refinery will typically have several thousand locations scheduled for inspection at periodic intervals ranging from every few weeks in high-risk locations to once every five years in other less-critical areas. These inspections produce volumes of data, manually entered into inspection management software for analysis. Even with thousands of measurements generated each year, this inspection data is inadequate to understand plant health in real time. Plant personnel can’t see how the plant is coping with the ever-changing corrosion and erosion sources or use data to predict when metal thickness will reach its retirement point. Why is manual measurement so ineffective? Most consecutive periodic measurements are performed in slightly different locations on the pipe, by different technicians, often armed with different inspection equipment. Such variances in measurements (See figure 1) add up to data noise, rendering the information largely useless. Variability of ±1 mm (.040 in.) is expected, but if a pipe wall is 5 mm (.200 in.) thick, the engineer evaluating the data will lack confidence when trying to determine when that pipe will reach its retirement thickness. Predicting damage rates is challenging, especially in areas where the corrosivity or erosivity of the process fluid varies frequently. Nowhere
in the production chain is this experienced more than refineries, which therefore have the highest variability in corrosion and erosion load. Traditional inspection methods just discussed simply don’t provide adequate quality or sufficient measurement frequency to drive predictive maintenance able to keep equipment running safely. From periodic to continuous monitoring Permanently installed, ultrasonic wall thickness monitoring sensors (See figure 2) on a wireless network are meant to work in harsh environments. The installation cost of ultrasonic sensors is low because they are non-intrusive and can therefore be mounted just about anywhere. Data retrieval via a new or existing WirelessHART network reduces installation and operating costs. Internal power modules last up to nine years, so no maintenance is required between turnarounds. If a WirelessHART gateway needs to be added, it is usually installed near the process unit. It collects data from all nearby WirelessHART transmitters and sends it via a wired backhaul to the plant’s operations and information technology systems. Software processes the corrosion data, stores it for historical analysis and makes it available for viewing. Advanced processing software can compare previously recorded ultrasonic waveforms with new data to improve measurement resilience when the internal meta-surface morphology is very rough. This problem is one of the main causes of variability among manual ultrasonic wall thickness measurements. Emerson’s Rosemount corrosion monitoring systems (See figure 3), formerly known as Permasense corrosion systems, which use the adaptive cross-correlation processing technique, further enhance measurement analyses, so even small levels of corrosion or erosion are detected in a matter of days. The advanced signal processing method used by this software, along with its data visualization and analysis features, makes data interpretation significantly easier and quicker.
help evaluate the effectiveness of a chemical treatment program. One European refiner used a network of wireless ultrasonic sensors installed across its crude overhead system to optimize the dosage of corrosion inhibitor additives. Prior to optimization, corrosion rates of up to 1.2 mm per year (.050 in.) were observed. Over a month-long period, the refiner increased the additive dosage in steps and tracked the effect. Once optimized, sensor data (see figure 4) showed the corrosion trend had stabilized, negating the need to replace the piping.
Figure 2: Ultrasonic corrosion sensors are permanently installed on this crude tower overhead line. Insulation is subsequently replaced around the sensor legs. Image courtesy: Emerson Automation Solutions
2. Don’t overestimate a problem: Preventive maintenance can identify what equipment does not need attention, which can be a major benefit. A deep-water production platform in the Gulf of Mexico deployed ultrasonic thickness monitoring sensors across 20 high-risk locations. Operators used data to verify a lack of corrosion activity, enabling continued safe production. Operators and engineers were confident that the current operating conditions were not adversely damaging the equipment on the platform, alleviating concerns regarding asset availability.
3. Look ahead to the next turnaround: Data from continuous corrosion sensors can help predict the timing of equipment
Figure 3: Corrosion sensors helped identify the optimal dosage for corrosion inhibitors. Image courtesy: Emerson Automation Solutions
Three brief case studies Here are three brief examples that illustrate best practices for corrosion monitoring: 1. Optimize corrosion inhibitor use: Realtime corrosion data from permanently mounted ultrasonic thickness sensors can OIL&GAS ENGINEERING FEBRUARY 2020 • 11
PREDICTIVE MAINTENANCE shows that the operator can continue to operate safely under historic operating conditions until then. The operator can include replacement equipment in the scope of the shutdown, preordering the replacement piping and vessel.
Figure 4: Data projected
replacement very accurately. One Middle Eastern oil producer examined data (See figure 5) during the preceding year from a single sensor, which showed a highly variable corrosion rate in an amine unit. It helped identify the root causes of the corrosion variability, while predicting when the vessel would need replacement. Applying lines-of-best-fit to the data (See Figure 6) enabled projections regarding minimum allowable thickness.
forward helps determine when piping will need to be replaced. Image courtesy: Emerson Automation Solutions
Figure 5: Continuous corrosion monitoring data from a single corrosion sensor installed on an amine unit vessel. Image courtesy: Emerson Automation Solu-
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The data showed that, if the plant continued to operate in the same way as the previous month, the unit would need to replace this piping at the end of 2019. If the plant operated on average as it had for the past 12 months, then the piping would need replacement six months later. The two projections gave confidence to the date range when the equipment would need replacing. This unit is due for a planned shutdown in Summer 2019 and the monitoring data Figure 6: Data projected forward helps determine when piping will need to be replaced. Image courtesy: Emerson Automation Solutions 12 • FEBRUARY 2020 OIL&GAS ENGINEERING
Final words Successful predictive maintenance for fixed equipment in refineries is essential for plant safety, availability and profitability. One of the biggest threats to the health of oil & gas assets comes from the inside in the form of corrosion and erosion. If left unchecked, these hidden threats eat away metal until leaks form, releasing hydrocarbons. Resulting disasters can cause fatalities, environmental impacts and financial losses. Fixed equipment repairs or, more frequently, replacements, are major capital investments and invariably involve shutting down parts of the plant. Actions need to be prioritized, planned and executed to minimize production impact along with immediate and opportunity costs. Therefore, management of equipment health, including its replacement, requires the best available data. Continuous corrosion and erosion monitoring systems, based on ultrasonic thickness monitoring sensors, deliver this required data and enable operators to better understand the following: • Current equipment health • How the plant is coping with corrosion and erosion demands • When equipment will need replacing. Operators with access to data of this quality and frequency can operate safer plants with enhanced availability, reduced maintenance costs and increased profitability. OG Jake Davies is global marketing director for Rosemount corrosion monitoring systems at Emerson Automation Solutions, formerly known as Permasense Ltd.
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ith vast experience in the oil and gas sector, Endress+Hauser understands the industry operates in challenging environments with high demands on performance. Regulations continue to tighten, and the industry faces constant pressure to reduce operating costs, improve reliability and increase ɖɡȫžƜɱɱ Ɯǂ˛žǠƜȚžˁ ʸǠʀǗ ȂƜɱɱ resources. Endress+Hauser can assist you with each challenge. Our organization has always had a dedicated customer focus. We have taken into consideration the complexity of our oil and gas customers’ processes and continue to invest, adapt and improve our offering to provide reliable instruments, services and solutions. To better serve our oil and gas customers in the Gulf Coast region, our organization has invested $38.5 million into a new 112,000 square-foot Gulf Coast Regional Campus. The new site will be located in Pearland’s lower Kirby District, just south of the Houston area. The expansion is set for completion by the end of 2020.
The campus will offer areas for calibration, repair and training, which will include a brand new state-of-the-art PTU (Process Training Unit). It will include a building and warehouse location for Endress+Hauser’s partner for Sales and Service in the Gulf Region, Vector Controls and Automation Group. The campus will house teams to support Endress+Hauser’s products, solutions and services for process automation, SpectraSensors’ gas analysis systems for the U.S. and international customers and Analytik Jena’s product lines for laboratory instrumentation. On the digital side, our recently updated website offers customers a more personalized experience. With an account on Endress.com, our customers can review transactions, request quotes, buy products and spare parts, download documentation, request technical support and connect with their local Endress+Hauser representative. We also offer B2B integration solutions that help save time and resources by establishing a connection with our customers’ business systems.
Jason Knuteson Vice President of Marketing Endress+Hauser USA
In projects, we have built on the MIV/MVV (Main Instrument Vendor/ Main Valve Vendor) concept to differentiate and augment more value to all parties and stakeholders involved in a project. After being ɖőɡʀ ȫǂ ɖɡȫǴƜžʀɱ ʸǠʀǗ ǠȚƜǂ˛žǠƜȚžǠƜɱࡊ we knew there had to be a better way to execute a project and we made it our mission to improve the process. From feed to operation, our customers work with one partner to ensure consistency, leverage purchasing from multiple suppliers, realign when the project timeline shifts, avoid ambiguities and provide a total solution for their project. We continue to make investments to help improve our customers’ prožƜɱɱƜɱ ɱʗɱʀőǠȚőŵȂˁ őȚƉ Ɯǂ˛žǠƜȚʀȂˁࡑ
Our organization has always had a dedicated customer focus.
888-ENDRESS Info.us.sc@endress.com www.us.endress.com Rendering of the Gulf Coast Regional Campus
INDUSTRIAL CLOUD COMPUTING
Intelligent use of Cloud sharpens operational insight Cloud-based wireless sensing enables safety, reliability and profits through widespread asset monitoring By Simon Rogers
Figure 1: By integrating data into the Cloud, an environment is created for crosssectional analysis, with thirdparty consultants able to perform high-precision analysis and provide suggestions for optimizing production. All figures courtesy: Yokogawa Electric Corp.
M
any assets found in oil & gas industry plants and facilities often are not connected — directly or indirectly — to a distributed control system (DCS) or other type industrial control system. While this reduces the number of assets requiring DCS inputs and outputs (I/O), it doesn’t mean they don’t require monitoring. Many assets need regular data monitoring as part of improved maintenance efforts, but not for real-time control. Examples include motors, pressure relief valves, safety showers and steam traps. In most plants and facilities, these type assets are much more numerous than those connected to the DCS, and many of them are difficult to access, either due to distance from connection points or lack of access. If traditional wired sensing methods were used to connect these assets to a DCS or other control system for monitoring, the expense would be astronomical. So, the status quo leaves many of these assets unmonitored, or minimally monitored by technicians periodically checking them during rounds. However, tightening health, safety, and environmental regulations (HSE) are forcing facilities to invest in better maintenance to improve safety, reliability and profitability. An increasing urgency exists to implement industrial internet of things (IIoT) solutions to deal with these and other issues as inevitable demo-
14 • FEBRUARY 2020 OIL&GAS ENGINEERING
graphic changes bring in younger workers, who sometimes possess less situational awareness and ability to troubleshoot these assets. The proliferation of data, and data-driven organizations, compresses timeframes for decision making and introduces digital competitors. Safety is enhanced by reducing the number of field workers in dangerous locations. Reliability is increased by applying predictive analytics to the big data generated by continuous plant monitoring. Profitability is improved by precluding the need for consulting services for plant equipment failures and plant-wide improvements. These three anticipated benefits have been the catalyst for most IIoT implementations. Address the issues Condition monitoring coupled with predictive analytics can deliver a significant transformation by improving safety, reliability and profitability. Preventing a major asset failure often can more than justify the cost of implementation. In the past, condition monitoring was accomplished by either walking around with portable devices and having the operators make ad-hoc decisions, or by installing extremely expensive condition-monitoring systems. The first alternative produces inaccurate data that often doesn’t get analyzed. The second is so expensive that only the most critical assets are monitored. What usually happens in a typical plant is a combination of the two. Rounds data is effectively useless, while condition monitoring systems that monitor only the most critical assets can miss the failure of less critical assets — those that become critical only after a failure. When walkaround monitoring is replaced by inexpensive wireless sensors, such as a system of battery-operated sensors by Yokogawa, this is disruptive technology that alters best practices and proves extremely productive. It can improve safety by reducing the amount of time workers
are required to be in potentially dangerous areas of the plant. Wireless monitoring also frees workers for more value-added activities, and the much larger number of sensors that can be installed permits ubiquitous and wide-scale monitoring throughout the plant or facility. The data gathered by these wireless sensors empowers online conditionmonitoring diagnostics for a much greater number of assets, producing predictive analytics when this data is translated into actionable insights with guaranteed outcomes. The best way to deal with this kind of data uses the Cloud. As is well known, cloud computing involves the practice of using a network of remote servers hosted on the Internet to store, manage and process data, rather than a local server or a personal computer. Because the plant data is in cyberspace, it can be interrogated from anywhere (See figure 1). The Cloud combines accessibility and convenience with enhanced plant security, as well as additional benefits. The data can be seen by anyone using an approved smart device, and it can be remotely monitored and analyzed for condition monitoring and performance improvement by experts. Because the data is in the Cloud, a simple onestop solution is provided for data management. DaaS in the Cloud The sensor series includes devices for monitoring vibration, temperature and pressure. These sensors and the Cloud service provided constitute a data-as-a-service (DaaS) offering. DaaS appeals to operators of oil & gas industry plants and facilities that do not want to manage and operate numerous data collection, transformation and sharing solutions — all of which would require granting access to their internal OT and IT networks. DaaS addresses these and other issues by providing one dedicated point of contact, along with one approved and trusted company that is granted access behind the operator’s firewall. The required multiple-managed and -supported connections and visualizations are therefore handled by a third-party, separating these services from the operator’s core business activities. For DaaS with visualizations, operators receive exposed data from existing automation and asset management systems. This enables the use of tools for engineers, managers and other
personnel to perform work through a browser. And of course, this work can be done from anywhere on any device capable of hosting a browser, such as a PC, smartphone or tablet. DaaS is therefore an enabler of digitalization activities for operators. Digitalization forces good data practices, helping to simplify DaaS service implementations, along with the quality delivery of services to operators to promote their digital transformation activities. Cloud case studies It is no longer efficient to have workers doing rounds of equipment and assets, checking for potential failure. Those operators could be doing higher value-added work instead of filling in boxes on rounds reports attached to clipboards. It is just too easy for them to accidentally overlook a significant sign of abnormality, and failures often occur despite rounds. For example, one plant was outsourcing vibration measurements for 200 items, with data collected monthly. This cost was approximately $48,000 per year, but frequent failures still occurred because the data was not digitalized, and the customer could not utilize the data for predictive maintenance. The Yokogawa Group consulting company positioned dozens of sensor devices throughout the plant, with each transmitting data to the Cloud. Cloud-based data management tools provided visualization and trend monitoring to indicate abnormal signs of incipient failure. The consultant provides the information to plant personnel so they can act. The plant gets real-time equipment status reports. Automatic warnings are provided to plant technicians when failure can be predicted. Because the data is
Figure 2: A sensor system monitored the acceleration of pumps and detected signs of abnormality before failure.
OIL&GAS ENGINEERING FEBRUARY 2020 • 15
INDUSTRIAL CLOUD COMPUTING already digitalized, this methodology enables digital transformation of the plant. Another plant installed wireless sensing devices on pumps, monitoring the acceleration for six months. In many cases, signs of abnormality occurred and were detected (See Figure 2). These potential failures were most often traced to broken balls in the bearing assembly. Early detection allowed predictive maintenance to be performed on the pumps, keeping them in service and reducing the costs of unplanned downtime. Final words Simple wireless sensors are easy to install, relocate and connect to the Cloud. Data in the Cloud provides a force multiplier for consultants, and for maintenance managers, operators and other plant personnel. For many oil & gas plants and facilities, this is the quickest path to initial IIoT implementations. Many sensors sending data to the Cloud provides ubiquitous and real-time field information
that can be analyzed and acted upon to prevent failures and downtime. It becomes possible to optimize production by integrating utility equipment data not associated with the DCS. The data can be used as input to a digital twin in the Cloud mimicking plant operations, permitting the plant personnel and consultants to tune plant performance. The Cloud-based data can be analyzed by experts located anywhere in the world using approved smart devices. Sensing, with data storage in the Cloud, therefore becomes “sense making� because it opens the way to digitalization of the plant. Digitalization leads to performance improvement and plant optimization. OG Simon Rogers is the head of the Advanced Solutions Division at Yokogawa Electric Corp. He has more than thirty years of global experience in the use of information and control technology to improve the safety, sustainability and efficiency of the process industries. Rogers holds a BEng degree in Chemical Engineering from Imperial College, London.
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THE AGE OF ANALYTICS
Five best practices for predictive operations at scale Case study example illustrates challenges and opportunities
By Nikunj Mehta, Ph.D.
Figure 1: Best practices to guide digital transformation. All graphics courtesy: Falkonry
D
igitalization of production and process operations has the potential to boost profit margins by three to five percentage points — but only if people can make the new technologies work at scale. According to a McKinsey survey, nearly 30% of executives reported active pilot projects, while 71% expected significant increase in AI investment. However, the survey found that progress remained slow. Most companies don’t have infrastructure for sourcing data and scaling artificial intelligence (AI) initiatives. Experts often address AI transformation at large enterprises. However, the focus has been on vision, strategy, people and culture. While important, these overlook other, multidimensional, factors necessary to succeed at scale. Successful at scale Working with some of the largest oil & gas and other industrial companies, we at Falkonry have observed that “transformation doesn’t happen from the inside out — it grows on you from the outside in.”
Transforming upstream and downstream operations from reactive to predictive processes, where quality, equipment and process-line issues can be prevented before they occur, is the goal for many of these companies. If you are embarking upon a digital transformation initiative — or if you are already in the process of implementing one — it’s time to step back and look at lessons learned from leaders in this space. From these lessons, here are five best practices (See figure 1) to guide transformation. BEST PRACTICE 1: Mass-adoptable technology Without mass-adoptable technology, there would be no transformation. Mass-adoptable AI technology doesn’t imply simple core tech, but technology where complexity is minimized or hidden from users. Using the technology shouldn’t require data scientists, or historical and labeled data. Such features — which make the technology easy to use, repeatable, and able to deliver ROI in a shorter time — are what drive its adoption. BEST PRACTICE 2: Ardent advocates Most organizations with critical operations are risk averse and are more comfortable making incremental improvements. Transformations require executive champions who are ardent advocates and furnish the necessary mandate and resources to reduce risk for engineering and operations teams. Of the reasons cited as causes of failure in enterprise transformation, basic challenges in human and team behavior top the list. Strong advocates ensure transformations that matter succeed despite these challenges.
OIL&GAS ENGINEERING FEBRUARY 2020 • 17
THE AGE OF ANALYTICS
Figure 2: Upstream compressor failure case study.
BEST PRACTICE 3: Buy-in Enterprise buy-in occurs when the results are genuinely insightful and valuable. Stakeholders see that transformation advances their organization’s interests and share in the ownership of the transformation. Early buy-in greatly smooths deployment and measurable results. BEST PRACTICE 4: Accountability Accountability cannot be spared in organizations and being brutally honest about value creation is essential to end up on the right side of the transformation. These accountability efforts can make AI or transformation teams uncomfortable, but it’s important to demand the most return upfront, even within the first 90 days.
Figure 3: The case study model predicted compressor valve failure 6 weeks in advance of detection by traditional means.
BEST PRACTICE 5: Agile Development While AI grows exponentially more valuable over time, it does not have to take forever. Two years should suffice and both sides should agree on phase exit gates at meaningful intervals. Successful case studies indicate the right exit moments are at 90 days (for proof of concept) and one year (for pilot to production) from the start.
18 • FEBRUARY 2020 OIL&GAS ENGINEERING
Upstream case study To highlight the effectiveness of subscribing to the best practices outlined above, let’s look at how they’ve been applied in the oil & gas industry. In offshore oil & gas, rotating machinery such as compressors, pumps and turbines are mission-critical equipment. In this case study, upstream production incorporates floating production storage and offloading (FPSO) vessels, floating liquid natural gas (FLNG) tankers, and floating storage & regasification units (FSRU). The common vulnerability was compressors, which are critical to operations, often single-threaded and periodically fail. The company experienced unanticipated FPSO and FSRU compressor failures that caused complete or partial loss of production. Repair parts and crews were mobilized to these often-remote operating units. On average, the company estimated that unscheduled downtime resulted in $300,000 of lost production per incident. In addition, installation cost to replace a failed unit was significant when added up across operations. The company adhered to best practices to ensure digital transformation efforts solved these problems: • Mass adoptable technology: The team conducted thorough market
research and assessment of available predictive analytic solutions. Most predictive maintenance approaches did not leverage operations telemetry and tended to be less robust. The customer chose Falkonry based on the following criteria: o Ease of use of the software by their operations engineers o Short time to results based on automated discovery of multivariate patterns within their operational data o Extensibility to address many use cases and operations problems o Ease of integration with a data architecture to quickly deploy and operationalize the solution in their environment. • Ardent advocates: The company established a digital transformation initiative led by the office of the CIO to identify and implement solutions across its operations. FPSO and LNG fleet operations generated a lot of data and the CIO needed a solution that could scale across multiple use cases and asset types. A team was established with members from operations, engineering and IT. • Buy-in: The FPSO was an early proofof-concept success. Using the Falkonry LRS software, the engineering team predicted dry seal degradation in the compressor six weeks before the internal operations teams suspected issues. Stakeholders were educated on the benefits and bought into the idea of using machine learning technology. Following the early success, the company is expanding use to onshore operations and identifying other applications for predictive analytics. • Accountability: The company required data remain within its environment and process engineers retain full control over the data, models and findings. Also
ensured was easy integration within its existing data infrastructure and leveraged operational databases that worked within or without enterprise asset management systems. • Agile development: The customer success team trained the company’s engineers on how to create predictive models themselves. Engineers rapidly created models to discover patterns in time-series data, enabling identification of precursor events to the compressor failures and generation of automated alerts. The three-month proof of concept was then followed by a 12-month production subscription. Predictive operations results The company quickly moved from proof-ofconcept to full production deployment of predictive operations across multiple compressors and vessels, resulting in significant operational improvement and maintenance costs savings. The deployment enabled them to predict compressor seal failures six weeks earlier than internal operations teams suspected issues. The total benefit generated from the initial pilot of just two FPSOs was estimated to be $580,000, with the primary benefit being downtime reduction. Additional benefits included reduction in installation cost and increased compressor life. As highlighted in the above case study, applying machine learning technology in the oil & gas industries can successfully detect, predict, and explain conditions preceding equipment failures, resulting in reduced unexpected downtimes and savings of millions of dollars annually. The benefits of this type of digital transformation are farreaching but are only achieved if companies make the new technologies work at scale. To do this, they need to consider all the multidimensional factors necessary to succeed at scale and should follow these five best practices: mass adoptable technology, ardent advocates, buy-in, accountability, and agile development. OG Nikunj Mehta, Ph.D. is founder & CEO of Falkonry OIL&GAS ENGINEERING FEBRUARY 2020 • 19
PROCESS INSTRUMENTATION
Jelec chooses lever-actuated terminal blocks Wire actuation variants suited to unique experiences By Laura Dickinson
W
hen Jelec, a systems integrator and engineering company for the oil & gas industry, designed new land rigs for use inside and outside the United States, simplicity was the most important factor in selecting the connection systems. After careful deliberation, they decided on terminal blocks with lever actuation. “They were using terminal blocks to connect wires everywhere on the land rig,” John Hagar, regional sales manager for WAGO, said. “They were connecting the top drill, the engine that drills the hole into the earth, the powerhouse, the mud pump and even connecting controls in the driller’s chair.” Alexandre Bienfait is the lead engineer from Jelec who worked on the land rig design. When he started working on the project, his knowledge of WAGO was limited. “We started communicating with John Hagar to learn about our options. He said, ‘You can save time with the push-button type or the screwdriver-actuated terminal block’,” Bienfait said. Actuation variants WAGO TOPJOB S rail-mount terminal blocks are available in three different actuation variants — lever, push-button or open tool slot — which can be wired either by pushing in solid conductors or by using a screwdriver to operate the spring-pressure technology. Bienfait selected the right rail-mount terminal block for his application. While the pushbutton capability introduced Bienfait to the railmount terminal block technology, “I didn’t really like those I saw because they terminate from the top. You need a small tool or screwdriver to operate those terminals,” Bienfait explained. “The reason we have three different wire actuation variants is that each customer has unique experiences that shape the context of their decisionmaking.” Ed Naczek, WAGO product manager for interconnect explained.
Figure 1: Rail-mount terminal blocks can be wired by pushing in solid conductors
“For Bienfait and Jelec, pushbuttons and open tool slot versions were not going to work, so we were able to offer the finger-actuated levered version.” “With the lever we can terminate in the shop,” Bienfait stated, “just by lifting a lever. That to me is a huge improvement.” Using terminal blocks with levers helped Bienfait and the Jelec engineering teams operate more efficiently. “The lever side is good for small spaces because it is hard to terminate in a small space when you have a big tool,” Bienfait said. Other considerations Further solidifying the decision was that many of the end-projects were shipped across national borders. Anyone could intuitively operate a lever with the same successful results. Bienfait further explained that Jelec had been using a competitor’s screw-type terminal blocks prior to switching. “I don’t believe anyone else on the market has something as simple as these lever terminal blocks,” Bienfait said. Secure wiring connections were needed throughout the many electrical connections of the land rig. The result was that terminal blocks with levers can now be found on every termination. While working with the levers, Bienfait said the quality inspired him to explore other WAGO products. “On this project we are also using a WAGO I/O system for extreme conditions to help power the land rigs,” Bienfait said. “We learned the system has a wide tolerance of high to low temperatures, which is perfect for a land rig.” Bienfait said WAGO helped simplify their processes and empowered them to be their best by helping them make intelligent decisions on quality products. Because of this, Jelec believes they are passing these sound choices on to their customers. “We want to make it easier for the people buying our products and I think the customers are really appreciating and responding to that,” Bienfait said. OG
or by using a screwdriver to operate the spring-pressure technology. Image courtesy: WAGO
20 • FEBRUARY 2020 OIL&GAS ENGINEERING
Laura Dickinson is the public relations coordinator at WAGO.
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