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COMMENT

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2 • DECEMBER 2018

Energy, info make the world go ‘round and ‘round

T

he men and women who are “powering and digitalizing the global economy are engaged in a noble mission,” said Jean-Pascal Tricoire, chairman and CEO, Schneider Electric. “Energy is a fundamental human right and connectivity is the foundation of a decent life in the 21st century.” Tricoire spoke at Schneider’s Digital Innovation Summit held mid-November at the Hilton Atlanta. In North America Schneider includes 30,000 employees at 250 sites and accounts for 29% of the company’s global revenues, which in 2017 were $30 billion. Schneider solutions straddle energy management and industrial automation. Primary drivers of the tremendous change both are undergoing, Tricoire said, include the usual suspects: Industrial Internet of Things, cloud, and analytics. “By 2030, IT will be the single greatest source of global energy demand,” he said. Technologically speaking, event discussions were dominated by Schneider Electric EcoStruxure, an architecture or platform for digital transformation that includes connected products, edge control, and applications and analytics. “Products are not enough,” Tricoire said, “expert services are needed. We have a community of 20,000 software engineers assigned to industries.” The primacy of services provision is become a dominant theme in the industrial internet age. Just in from Houston Specific to the oil & gas industry, Doug Kushnerick, senior scientific advisor, Exxon Mobil Research & Engineering, spoke at the event on that company’s ongoing digitalization efforts and its long-standing partnership with Schneider. “Global energy needs will increase 25% by 2040. It is a tough challenge to lift people in the developing world into the middle class,” he said. Digitalization in the oil & gas industry has been underway for decades, Kushnerick

OIL&GAS ENGINEERING

KEVIN PARKER SENIOR CONTRIBUTING EDITOR

said. About 15 years ago introduction of process-equipment digital twins and chemical-process modeling delivered benefits quantified in the hundreds of millions of dollars. A number can’t yet be put on the benefits following from the layer of infrastructure and applications being installed today, but the expectation is of greater speed in analysis and decision making. “Some projects pay out very fast. Others may be painfully slow, but we need to make the infrastructure investment,” he said. A voice of reason Later in the day, Kushnerick participated in a panel on industrial automation moderated by Robert Sloan, a research director from The Wall Street Journal. “Connectivity increases calculation intensity,” Kushnerick said. “The connection of people in the field to the data around them empowers them to make decisions.” Of primary interest, he said, is the opportunity to look across an entire fleet of assets to derive benchmarks across facilities for insights into production machines and units. The potential role of artificial intelligence (AI) remains a $64,000 question. “My impression is we’re still on the cusp with that. What we do see is advanced machine learning and modeling,” Kushnerick said. “Some AI is very good but integrating that into decision making is still a challenge. When someone shows us a new product and says, ‘There are five AIs in there.’ We say, ‘What do you mean by that?’” A further important frontier is the opportunity to increase safety, through wearables and augmented reality, for example, Kushnerick said. “Many current projects are software projects disguised as hardware projects. That’s what’s changing the workforce.” OG

I NSIDE

Cover image courtesy: Industrial Technology Research Institute

FEATURES 4

Machine learning streamlines tubular-connection analysis

4

Questions answered through an application example

8

Upgrade from pump packing to mechanical seals Sealing conserves water, improves energy efficiency, and minimizes environmental impact

12

Award winners recognized

8

Equipment, software solutions get the thumbs up

15

High-performance materials support optimal metal end cap seals How elastomers are tested for suitability

17

Machine learning-based gas recognition detects minor leakage Seven image characteristics, singly and combined,

15

used to recognize amorphous gas clouds

18

Case study showcase

OIL&GAS ENGINEERING DECEMBER 2018 • 3

AI IN THE OIL & GAS INDUSTRIES

Machine learning streamlines tubular connection analysis Questions answered through an application example

A

By Brennan Domec, PhD. PE

Figure 1: Torque-turn graph. All graphics courtesy: Frank’s International

quick internet search will reveal a trove of definitions and detailed information on machine learning. Amidst it all, readers will find a common thread. That is, machine learning is a branch of artificial intelligence which, when employed, learns from experience and uses the knowledge gained to predict future system states. It is a broad technology that consists of many methods well suited to solving problems of regression, classification, and anomaly detection. As a result, it has found use in applications ranging from credit card fraud detection to analysis of corroding systems. In a recent example, machine learning was applied to the make-up analysis of tubular connections. In this operation, data are collected from sensors embedded in the make-up machinery. These data often are presented to the operating technician in graphical form as a plot of torque versus revolutions, or turns, of the pin member of the connection. An example of such a graph is presented in Figure 1.

Upon completion of the make-up process, the technician is tasked with reviewing key features of the graph and comparing them to acceptance criteria to establish disposition of the connection. In the event of a rejection, the technician must diagnose the objectionable condition to determine if the connection can be salvaged, and how. To do so, he or she relies mostly upon training and experience. This is where machine learning can provide benefit. It is generally accepted that computers are at least as capable of recalling information and associated relationships amongst data as are humans. Therefore, machine learning, when applied to the example under discussion, should be able to effectively classify and diagnose tubular connections with little or no human intervention. Machine learning workflow To explore this hypothesis, the workflow shown in Figure 2 was followed. As a first step, the objectives of this project were defined as: • Develop an intelligent system for automatically classifying torque-turn graphs. • Use the intelligent system for anomaly detection and prediction to reduce or eliminate damage to connections before it occurs. Understanding your data In the second step, the goal was to understand the data available for training the model. This includes answering questions such as: • What variables or features are important to solving the problem?

4 • DECEMBER 2018

OIL&GAS ENGINEERING

• How are the data stored? In what form? • Is the scope of the data broad enough? The impact of a given variable may not always be known with certainty at this point in the process. If a variable does not strongly affect the result, it will be apparent when model performance is assessed in subsequent steps. It is therefore acceptable to include these data, but caution should be taken to not include too many variables for which the impact is not well understood. This avoids wasting efforts in building a model only to find that it fails in verification testing. It is also important to ensure that the data set available is broad enough to encompass the range of input variables expected to be encountered in deployed operations. Trained algorithms perform best when exposed to data like those used to train them. If there is significant deviation from the scope of the training set, the model will fail to provide accurate responses when queried. Data preparation Data preparation is critical to any machine learning problem. During this step, data are first extracted for training. This may include the full data population or a random sampling of the data if the population is quite large. Cleansing of the data follows, to standardize data and formatting. Last, the data are verified for accuracy. As these data will be used for training the model, it is imperative that they are accurate. Failure to properly prepare the data for training can result in a model that may provide erroneous or misleading results. Model building and training At this point the objectives are defined, and the data selected and prepared for use. The next step is to begin building and training a machine learning model that meets the project goals. Dozens of machine learning algorithms are in common use; a detailed discussion of which would be beyond the scope of this article. However, it is worth noting that these algorithms generally can be grouped by the type of problem that they best solve, narrowing down the list of appropriate algorithms once the problem is clearly defined. Multiple methods are used to train a machine learning model. Two methods commonly

employed are those of supervised and unsupervised learning. In supervised learning, input data are provided to the model with corresponding outcomes. This method is often used for regression and classification problems. The alternative method is unsupervised learning, which finds common use in anomaly detection problems. It differs from supervised learning in that there are no corresponding outcomes provided to the system against which the model is trained. In both methods, the model is said to be trained when an acceptable level of maximum error has been achieved. Training is often an iterative process, the results of which are best summarized in the form of a confusion matrix. For example, the results from the first training iteration, as tested on new data (i.e., data not used for training) are shown in Table 1. Deployment Upon completion of performance verification and final tuning, the trained model is ready to be deployed for use. Deployments may take many forms, including local hosting on a single computer, networked access, or web deployment for access from anywhere in the world. Because the bulk of the computing horsepower is required during the training operation, the deployed model often may be ported to much less powerful platforms, even mobile devices. Even after the model is deployed, the development process does not end. Model performance should be monitored, at least periodically, to ensure that it is meeting real-world demands. It is not uncommon for model adjustments to be required after deployment. This is not necessarily a sign of a poorly performing model, but instead part of the continuous improvement process. As the model is exposed to more and more data, it will become “smarter” and “learn” from its new experiences, ultimately improving performance.

Figure 2: Machine learning lifecycle.

Table 1: Confusion matrix, first iteration.

OIL&GAS ENGINEERING DECEMBER 2018 • 5

AI IN THE OIL & GAS INDUSTRIES The model described as an example for connection make-up analysis was successfully deployed by Frank’s International as the intelligent connection analyzed make-up (iCAM) technology (see Figure 3). Deployment occurred after final optimization, fine tuning, and performance verification of the model to provide the requisite levels of accuracy and capability. Although not described herein, additional features have been and will continue to be added to this technology through future updates.

Figure 3: Intelligent connection, analyzed make-up technology screen and display.

6 • DECEMBER 2018

Benefits As the example shows, machine learning can be used effectively to analyze the connection make-up process. Doing so offers many benefits. First, the use of machine learning removes human subjectivity from the analysis, providing more consistent and accurate results. While most human operator training occurs under globally standardized programs, the experience gained by each operator will vary greatly and is limited in scope and size. Conversely, a properly trained machine learning model incurs the benefit of learning from the collective experience of all recorded connection make-ups and recalling that information when required. Thus, the experience of a trained machine learning model equates to many lifetime’s worth of experience for a human operator. The implementation of machine learning technology for connection evaluation also has advantages over rules-based systems in use today. Establishing rules for some conditions is straightforward (e.g., minimum torque, maximum torque, and others), but not so for others (e.g., dope squeeze, high interference, and poor sealing). To develop the necessary rules to automatically evaluate a given connection, we must create a rule or series of rules for each acceptable and rejectable condition. Those rules must be based off the prior knowledge of

OIL&GAS ENGINEERING

each condition’s signature and contributing factors. Machine learning overcomes this challenge through its ability to identify patterns and relationships that may be imperceptible to human analysis. As a result, the trained model generally can identify and evaluate more conditions than a rules-based system and can do so more accurately and efficiently. Machine learning, as implemented here, also provides the additional benefits of predictive analysis and anomaly detection, preventing damage to threaded connections before it occurs. This translates into both time and cost savings for the tubular running operation. From an operational perspective, implementation of automated connection analysis can reduce personnel on the rig floor. This may be achieved in two scenarios. First, using technology, such as Frank’s iCAM, allows for the computer to make a final determination of connection integrity without human intervention, removing operating technicians from the process all together. In an alternative scenario, the human operator may be removed from the rig floor and instead monitor multiple jobs from a centralized shore location, intervening when and if necessary. This is made possible by real-time data communication technologies, such as Frank’s DISPLAY system (Figure 3), that facilitate monitoring of the connection make-up process from anywhere in the world. Ever-increasing role As the oil & gas industry continues seeking gains in efficiency and well integrity, technologies such as machine learning are expected to play ever-increasing roles in normal operations. In a real-world application example, machine learning has been successfully applied to the evaluation of tubular connection make-ups. The resulting benefits include accurate and consistent evaluation, real-time prediction and anomaly detection to prevent connection damage, and removing personnel from the rig floor. When combined, these translate into cost savings and improvements to both well integrity and personnel safety. OG Brennan Domec, PhD. PE is director, strategic technology, Frank’s International, LLC.

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SEALS, GASKETS, AND PACKINGS

Upgrade from pump packing to mechanical seals Sealing conserves water, improves energy efficiency, and minimizes environmental impact By Mark Savage and Sam Ajram

Figure 1: Packing seal chamber. All images courtesy: John Crane Inc.

8 • DECEMBER 2018

T

he environmental performance of products and processes in all industrial sectors increasingly is cause for critical inspection, with sustainability, conservation of natural resources, and reduced environmental contamination concerns influencing equipment design and selection. Many industrial processes can be addressed to improve sustainability and minimize environmental impact, while at the same time maintaining or reducing operating costs. Implementing energy-efficient and environmentally friendly processes and technologies should be embraced as a priority at the component, process, and system levels. One aspect of these processes is missioncritical rotating equipment, and specifically centrifugal pumps, which represent a significant proportion of the equipment found in industrial operations. One vital component of a centrifugal pump is the seal around the rotating shaft that passes through a stationary pressure casing or housing. The seal contains the liquid or gas from escaping to the environment. Sealing systems help maintain acceptable pump efficiency, reliability, energy consumption, water usage, and emissions control. These factors can materially facilitate achieving total-lifecycle cost-reduction and sustainability objectives. Sealing performance can be improved for centrifugal pump applications by

OIL&GAS ENGINEERING

upgrading from traditional compression packing to mechanical seal technology. When sealing a centrifugal pump, the objective is to allow the rotating shaft to enter the wet area of the pump without large volumes of pressurized fluid escaping. The pump discharge pressure forces the fluid back behind the impeller, where it is induced to exit by way of the rotating drive shaft. To minimize leakage, a seal is needed between the shaft and pump housing to contain the pressure of the process being pumped and withstand friction caused by shaft rotation. Compression packing is the traditional means to seal centrifugal pumps, going back more than 100 years. Also referred to as gland packing, it is a braided, rope-like, and lubricated material packed around the shaft in rings, physically stuffing the gap between the shaft and the pump housing, within a stuffing box. Water leakage and consumption For compression packing to work, some leakage must be maintained to lubricate and cool the packing material. Therefore, packing rings allow for an adjustable, close-clearance leak path parallel to the shaft axis. As the packing is used, however, some of the lubricant that is embedded into the packing is lost, reducing the packing ring’s volume. The pressure squeezing the rings together is also reduced, increasing leakage. Periodic adjustment of the packing follower brings the pressure back into specification and controls the excess leakage. In today’s world, however, this maintenance is not always being done at required intervals or adjusted correctly. As the number of centrifugal pumps incorporating the use of compression packing decreases, training for and understanding of packing maintenance has waned. Consequently, under-tightening and overtightening of packing rings is a prevalent and growing misapplication of centrifugal pump

maintenance, with critical consequences to both water consumption and energy draw. Under-tightening results in too much leakage. Already, when properly adjusted, packing leakage can amount to gallons of liquid leaked per minute. This can be either aqueous solutions comprised of varied benign or caustic chemical compositions, or particles in suspension or slurry, depending on the process. The heavier the suspension or slurry content in the pumped liquid, the more water is needed to get packing to work reliably. Typically, a clean external flush is piped into the stuffing box through a lantern ring, which keeps the packing lubricated and cool while flushing abrasives and chemicals. Normally, some portion of the leakage is released continually into the atmosphere. Under-tightening of the packing rings and use of external flushes increase this atmospheric release proportionately, along with environmental impact potential. Friction and parasitic energy draw Friction is always present in centrifugal pumps with compression packing, due to the large surface area of the packing rings in contact with the shaft. Over-tightening packing rings restricts leakage flow, increases friction between packing and shaft, and generates excessive heat, which degrades the packing. Increased friction also wears the shaft prematurely. From an energy consumption perspective, the additional friction of the packing gripping the shaft creates increased drag, requiring more drive power to turn the shaft. It is that drag that leads to additional, significant parasitic energy draw. Thus, the friction-induced energy draw is critical to the energy efficiency of the compression packing. Moreover, friction is not the only factor influencing energy usage associated with compression packing. When examining the energy draw component of a total lifecycle-cost analysis related to compression packing use in centrifugal pumps, another consideration factor is the external flush piped into the stuffing box, as this pressurized water or fluid needs to be moved from a source location to the packing, requiring a pump that draws electricity. Also in some industries where compression packing is more commonly used, water added via the

packing flush to maintain a clean environment around the packing needs to be taken out later. Removal of this water requires energy, typically through boiling and via the pump heat soak, with energy transferred from the hot metal of the pump to the fluid within the packing chamber. These energy draws typically are not measured directly. Instead, current and voltage fluctuations used by the pump motor are assessed under varied operating conditions to determine how much power is being consumed by parasitic influences, which enables packing energy deficiencies to be identified. Mechanical seals support efficiency The mechanical seal is an alternative to compression packing that resolves many of the sustainability and environmental-impact issues inherent in compression packing. The mechanical seal requires much lower water and energy demand, with substantially reduced leakage, making it more efficient at containing volatile or hazardous fluids, aqueous solutions, and slurry suspensions. In addition, mechanical seals require no maintenance once installed. A mechanical seal is comprised of a stationary primary element fixed within the pump housing, and a rotating mating element fixed to the shaft. Precisely machined, these two components are pressed together by a flexible load element, meeting at a wear face, while the extreme tolerance precisions between the two elements minimize leakage. The wear faces are supported on an extremely thin lubricating film, typically 0.25 microns (9.8 micro inches) in thickness. Available in a wide variety of types, arrangements, and materials, mechanical seals are found in most centrifugal pumps today. Advantages include the following:

Figure 2: Cutaway of a mechanical seal.

Figure 3: Pump packing seal exhibiting excessive leakage to the environment.

OIL&GAS ENGINEERING DECEMBER 2018 • 9

SEALS, GASKETS, AND PACKINGS Minimized water consumption and leakage—mechanical seals require very little flush water to be injected into the seal chamber. Compression packing used in abrasive pumping applications requires significant water volumes to be

injected into the stuffing box. A mechanical seal in the same service requires only a small fraction of this water volume. Seals create an extremely restrictive leak path perpendicular to the axis of the shaft and between the

MAKE INTELLIGENT CONNECTIONS Frank’s iCAM™ and DISPLAY™ technologies • Reduce personnel on the rig floor • Enable remote, real-time monitoring of connection make-up • Determine connection integrity without human intervention

www.franksinternational.com

two sliding seal faces. This results in almost no leakage to the atmosphere. Reduced power consumption—the amount of power required to drive a mechanical seal is as much as 80% less when compared to compression packing, primarily because the seal faces have less frictional energy losses due to the extremely precise mating between the stationary and rotating elements. Additional energy reduction requirements take the form of reduced need for flush water to be pumped into the seal. Dual mechanical seals—designed to ensure maximum sealing safety, dual mechanical seals are typically defined as a single assembly that contains a pair of seals. A cavity is formed between the two seals within the assembly, which is filled with a barrier or buffer fluid that separates the pumped liquid from the atmosphere and environment. Dual mechanical seals allow for near complete control over the seal operating environment and the fluid film lubricating the seal faces. They provide maximum elimination of the fluid leakage being handled in centrifugal pumps. Reduced environmental impact Efforts made toward improving sustainability in industrial processes, whether by reduced water and energy use, or by eliminating harmful fluid and gas discharge, reduce both environmental impact and operational costs. Mechanical seals in centrifugal pumps, and particularly dual mechanical seals, are well-suited to reduce or eliminate volatile or hazardous fluids, and their harmful vapors, from escaping into the environment. They should be specified as the standard sealing solution, particularly when the pumped fluids present a safety, health, or environmental hazard. OG Mark Savage is metal bellows engineering product group manager and Sam Ajram is global product marketing manager at John Crane Inc.

10
DECEMBER 2018

OIL&GAS ENGINEERING

PRODUCT OF THE YEAR

Award winners recognized Equipment, software solutions get the thumbs up The Oil & Gas Engineering annual Product of the Year awards recognize significant innovation and advancement in technologies and best practices. In this December issue we announce the award winners.

IIoT & process control

∠ GOLD AWARD WINNER Bedrock Automation seeks to remake industrial automation From its inception, Bedrock Automation has been dedicated to reimagining every aspect of the installed base of automation assets. The company says it can support traditional PLC and DCS applications with a fully compliant and cybersecure IEC 61131 development environment. Bedrock’s system is architected on a patented 4GB backplane. Combined with capacious computing power in its system modules, I/O is refreshed at a fixed rate independent of system size. This kind of unprecedented performance enables ease of use, higher process yields, and more precise control. Patented I/O module circuits enable universal, software-configurable analog and discrete I/O. This enables automation solutions engineered with just three I/O module types. The OSA Remote Controller combines PLC, RTU, and edge control; intrinsic cybersecurity; and universal I/O into a compact standalone module ideal for remote monitoring and control applications. The OSA Remote is configured with standard Bedrock IEC 61131-3 engineering tools. Intrinsic cyber security reduces the need for external firewalls, intrusion detection, and other devices. In addition to securing its own controls, the OSA Remote can extend protection to other systems and devices via embedded anomaly detection.

https://bedrockautomation.com

∠ SILVER AWARD WINNER Well lifecycle reporting based on software, services, and support

∠ BRONZE AWARD WINNER Falcon Electric says industrial UPS system is way to protect yourself

A two-time 2018 Product of the Year honoree, Quorum Software’s dynamic response to a quickly evolving market is described on the facing page. WellEz On Demand, its well lifecycle reporting application delivers value based on cloud technology, 24 hours by 7 days a week support, and payas-you-go pricing. Two outstanding features are the ability to generate well-bore diagrams from the field that evolve as the well moves from drilling to completions to workovers. An XML feed automates transfers between applications for authorization-for-expenditure management, production, accounting, and data visualization. With WellEz On Demand, users can implement a prepackaged solution to reduce IT overhead and scale with their needs quickly. WellEz On Demand features an easy-to-use interface for capturing drilling, completion, workover, and facility information; automatically distributed daily reports and trend analysis; the ability to upload and share any file type from the rig site to the office; and automated data transfer between operational data and other in-house applications.

Many types of uninterruptible power supply (UPS) products are available. UPS, also known as a battery back-up, provides varying degrees of power protection against blackouts and other power disturbances, depending on UPS type or topology. Standby and line-interactive UPS is appropriate for non-critical applications; line-interactive UPS is similar to standby UPS except it is able to provide an acceptable output voltage to connected equipment during “brownout” conditions. An online UPS provides higher levels of protection but also an electrical firewall between the incoming utility power and electrical equipment. Falcon’s SSG universal UPS with a hot-swap Lithium Iron Phosphate (LiFePO4) battery provides longer service life, longer backup times, lower weight, and higher safety than those with lead-acid batteries. The SSG UPS also lowers total cost of ownership by dramatically reducing costly battery replacements and downtime. Other key features of the SSG UPS with LiFePO4 battery include a small footprint, continuous operation from -20 to 55°C with no performance degradation, no thermal runaway issues that are inherent in Lithium-ion chemistry, and a service life of more than 10 years.

www.quorumsoftware.com

www.falconups.com

12 • DECEMBER 2018

OIL&GAS ENGINEERING

Data & analytics

∠ GOLD AWARD WINNER Quorom Software is on the move Things are changing at Quorum Software. In August, its acquisition by Thoma Bravo, LLC, the private equity investment firm was announced. The firm is doing so because “Quorum is a pioneer and leader in the energy software market, offering a broad portfolio of mission-critical technology solutions,” said Scott Crabill, managing partner, Thoma Bravo. In November, J. Charles Goodman was named executive chairman of the board of directors at Quorom, the provider of finance, operations, and accounting software to the oil & gas industry, with a significant presence in the largest public energy companies, LNG exporters, and natural gas processors. Quorum was recognized for its latest release, Entero Mosaic v2018, a unified application for petroleum economics, decline analysis, and reserves management. It features highly configurable, push-button reconciliation for both regulatory resources & reserves and capital and operating budgeting; a composite workbench to quickly filter, sort, and search on inputs, economic detail, and result variables; and presentations and layouts for detailed economics and comparing indicators between scenarios.

www.quorumsoftware.com

∠ SILVER AWARD WINNER

Understand End Applications Of New Technologies, Data Analytics, IIoT & Edge Devices

In-Depth Conference Workshops: JAN 30, 2019 Two Day Conference: JAN 31 & FEB 01, 2019 HOUSTON, TEXAS

Thoroughly Researched & Redeveloped To Reflect The Industry’s Current Business Needs, Priorities And Challenges

Take Your Automation Program To The Next Level

Delivering Actual Success Stories & Implementation Strategies For… Leveraging Traditional Automation Technologies, Advanced Data Analytics And Cutting-Edge IIoT & Powerful Edge Devices i Achieve More Consistent, Improved Efficiencies i Increase Production Automation & Optimize Output i Make Faster, Smarter Decisions Closer To The Well Site

Fox Thermal found in U.S. shale fields For more than 20 years, Fox Thermal has manufactured thermal gas mass flowmeters, based on constant temperature differential technology to measure mass flow rate of air and gases, using proprietary sensor technology. Measurement is in standard volumetric or mass units. No additional temperature or pressure compensation is required, and there are no moving parts. Thermal mass flowmeters have many uses in the oil & gas industries, including in U.S. shale fields. Examples include the measure of tank vent and flash gas during storage operations as well as tank leakage. The Model FT4X thermal gas mass flow meter features a data logger to record flow rate, totals, and other events and alarms. The logs provide information about the flow meter’s settings and functionality, including gas or gas mix composition, configuration, and calibration validation historical test data. Other advanced features of the Model FT4X include a second generation non-cantilevered DDC-Sensor, expended Gas-SelectX menus, CAL-V, RS485 Modbus RTU or HART, standard USB port, and Ft4X View software.

www.foxthermal.com OIL&GAS ENGINEERING DECEMBER 2018 • 13

SNAPSHOT OF SPEAKER LINE-UP Rogier Pouwer Automation Engineering Manager Anadarko Petroleum

Alan Bryant Senior Automation Engineering Advisor Occidental Petroleum

Jim Claunch VP Operational Excellence Equinor

George Robertson Facilities Engineer & CyberSecurity Advisor Chevron

Brandon Davis Automation Lead Devon Energy

Danny Durham Director of Upstream Global Chemicals Apache Corporation

Dustin Yates Data Analyst Concho Resources

James Gallyer Senior Automation/ Measurement Technician EP Energy

www.wellsite-automation.com

PRODUCT OF THE YEAR

Data & analytics

∠ BRONZE AWARD WINNER SecurityMatters stands guard at operations technology inflection points Frost & Sullivan says SecurityMatters and its SilentDefense solution protects information technology/operations technology (IT/OT) system networks against malware and zero-day attacks. It also helps companies comply with multiple regulator standards such as NERC CIP and NIST, guaranteeing network segmentation and policy enforcement and response, per IEC 62443. The solution “leverages patented machine learning capabilities to monitor IT/OT ecosystems from three central anagles—network, protocol and semantics. It combines this machine learning with a vast industrial threat library and a comprehensive protection program to deliver timely and actionable updates regarding new threats,” said Danielle VanZandt, industry analyst for security, Frost & Sullivan. Thereby, SilentDefense empowers industrial operators with visibility, threat detection capability, and network control. Featuring a user-friendly interface and out-of-the-box detection engines, SilentDefense delivers actionable results. SilentDefense allows users to assess risks, threats, and vulnerabilities; understand current network resilience state; and pinpoint weak spots and current inefficiencies.

www.secmatters.com

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2/9/2017 2:25:34 PM

ENGINEERED MATERIALS

High-performance materials support optimal metal end cap seals How elastomers are tested for suitability By Eric Bucci

Figure 1: Metal end cap seals need to be resistant to downhole fluids such as hydrogen sulfide and drilling fluids while meeting industry specifications such as ISO 23936 and 10423. All graphics courtesy: Trelleborg Sealing Solutions

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ellhead and tubing hanger sealing materials are subjected to an array of challenges, from high temperatures to corrosive fluids, and especially, high pressures. That’s one reason metal end cap (MEC) seals traditionally have been designed by wellhead manufacturers and produced to specification by sealing experts. It seems logical that the most robust, capable MEC seals would be created by those with the most sealing experience in a wide variety of situations. A new MEC design combines high-performance materials with a state-of-the-art design, creating an optimal sealing solution. The key to a superior MEC seal begins with a high-performance elastomer that is chemically bonded to metal C-rings. The elastomer must not only meet industry standards that include ISO 23936 (formerly NORSOK) and ISO 10423 (formerly API-6A), but also provide a high level of resistance to extrusion, explosive decompression, and downhole fluids. Additionally, it must simultaneously withstand temperatures up to 350°F and pressures up to 15,000 psi. One key failure mode is elastomer destruction by rapid gas decompression (RGD). Elastomers used in wellheads and various tubing sections are tested by being subjected to high-pressure gas molecules, such as carbon dioxide, being forced into them. The molecules then force their way out of the elastomer matrix when the pressure is released, potentially causing blisters and cracks within the elastomer that can propagate to the elastomer exterior. Thus,

MEC seals should be manufactured from an elastomer that resists the majority of gas molecules trying to enter into its matrix. Testing elastomers Most industry RGD specifications assign subjective grades of zero to five to elastomers. The typical test specimen for an RGD test is an O-ring. After an RGD test, the O-ring specimen is usually cut into four equal pieces so the interior can be visually examined along with the exterior. Zero is the best rating and means no blemishes or imperfections are noticed within or on the elastomer surface. Grade one means no more than four internal cracks are seen, with each shorter than 50% of the O-ring cross section. Two means less than six internal cracks are seen, each less than 50% of the O-ring cross section, and three means less than nine internal cracks, with two of the cracks allowed up to 80% of the O-ring cross section. Grades of four and five are typically considered unacceptable and based on cracks extending to the O-ring exterior and the O-ring being split or fragmented. In addition to receiving a preferred grade of zero or one during the RGD test, the elastomer in an MEC seal must be highly resistant to downhole fluids such as hydrogen sulfide, drilling fluids, completion fluids, and the crude oil itself. Industry specifications, such as ISO 23936 and ISO 10423, also dictate specific chemical formulations that a seal material must survive in an immersion test. The seal material specimen is immersed in the chemical formulation for specified time periods, temperatures, and pressures. Once removed from the immersion test, the specimens are tested and results compared to industry-specified failure criteria. Any specimens exhibiting properties within the failure criteria are considered compliant, and those outside it are unacceptable. OIL&GAS ENGINEERING DECEMBER 2018 • 15

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Figure 2: The key to a superior metal end cap seal begins with a high-performance elastomer material bonded to metallic anti-extrusion rings.

ENGINEERED MATERIALS It should be noted that even though a seal material is demonstrated to be compliant to an industry specification, it does not mean the seal material is wholly acceptable in an application. Additional testing to specific application requirements is needed. Finite element analysis Once MEC elastomer materials are tested to industry specifications and shown to be compliant, design work can begin on the elastomer and metal end caps. MEC seals are large diameter, and testing in lab fixtures is complex and expensive. Finite element analysis (FEA) can therefore support product proving. MEC seal designs can be virtu-

ally modeled using FEA under a myriad of conditions to predict seal behavior before spending significant resources on lab fixtures. The FEA results guide engineers to optimize the hardware and seal design as a system, to mitigate potential failure modes. The FEA results also can graphically demonstrate what forces and stress the seal encounters under arduous conditions. Having this knowledge allows engineers to have the highest confidence possible before spending resources on lab testing. At this point it should be eminently clear to the oil & gas industry that equipment and seal manufacturers have collaborated to analyze and engineer the best MEC product possible. MEC seals are expected to have a multi-year life span, and proper functioning is critical to ensure well integrity and overall safety. OG Eric Bucci is oil & gas segment manager, Trelleborg Sealing Solutions.

2018

Oil & Gas Engineering Research SPONSORED BY:

Turning Research into Insights Makes for Better Business Decisions This study was performed by Oil & Gas Engineering to identify engineering professionals’ insights regarding oil industry integration, including challenges, best practices, opinions, and type solutions. Fifty-nine percent of engineering professionals’ companies or clients have experienced substantial cost and/or scheduling overruns when executing capital projects. The top causes of cost and scheduling overruns are poor or rushed planning, overoptimistic assessments, and unclear responsibility for strategic or operations decisions. Forty-two percent of engineers agree that front-end engineering and design is one of the most problematic or challenging capital project transitions, followed by forging initial design specifications and the transition from design to development. Access the full Oil & Gas Engineering Research with additional findings and insights at www.oilandgaseng.com/2018Research.

COVER STORY

Machine learning-based gas recognition detects minor leakage Seven image characteristics, singly and combined, used to recognize amorphous gas clouds

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By Kevin Parker

To make gas leakage detection automatic, ITRI developed s part of the Taiwan-based Industrial Technology image auto-recognition technology. Because IR image feaResearch Institute’s (ITRI) efforts to commercialtures for gas leakage are diverse and conventional image ize its recently introduced gas leakage automatic recognition techniques unsuitable, GLART analyzes numerrecognition technology (GLART), Senior Principal ous IR leakage images compiled by ITRI. ITRI found gas Researcher Dr. Ming-shan Jeng in November leakages can be described using seven image characteristics toured the U.S. and had opportunity to speak derived from grey pattern and texture analysis. These seven with Oil & Gas Engineering magazine. characteristics are used in training seven machine learning GLART software is applied to infrared (IR) thermal images models individually. Outputs of the seven models are treated created by a leakage detection device. It can be combined by an ensemble and regression process to generate the final with robots, tracks, or unmanned aerial vehicles for automatoutput. ed inspections. The technology enhances leakage inspection “Unlike a face or a cat or a dog, a gas emission has no accuracy and IR thermal imager speed even when manually identifiable definite contour, so the task was to find suitable operated, Jeng said. characteristics to which ML could be applied, as well as “The infrared camera has to be able to detect gas in motion analysis. We spent seven years populating the datathe three to five micrometer range, which is lower than base with instances of gas leakmost infrared cameras proage,” Jeng said. vide,” Jeng said. In addition, to commercialize A large-scale industrial trial the technology, all the many of GLART for pipeline inspecdifferent types of pipes, joints, tion is currently underway at and flanges used to convey gas a Taiwan petrochemical plant in the petrochemical industries and the technology is available must be recorded into the datafor licensing. “Early detection base to be used to further train of small leaks is crucial for the the model. prevention of accidents; even minor leaks of a few grams of Gas leak recognition technology is applied to infrared thermal Placed in perspective hydrocarbons per hour can lead Most conventional leakage monito disastrous results if an ignition images created by leak detection devices that can be combined with unmanned aerial vehicles, aka drones, for automated inspectoring devices applied to pipeline source is present,” Jeng said. tions. Image courtesy: Industrial Technology Research Institute pressure, flow rate, or acoustic signals cannot detect small leaks. Fixed devices work well Machine learning applied only when leakage rate exceeds one percent of the total GLART uses machine learning and image processing for transport amount. Manual labor detects small leaks, but is auto-recognition of minor gas leakage, enhancing the leakfar too slow for large plants and pipelines, and often results age image and extending the lower limits of IR detection. in delayed leak discovery. When using only an IR camera for Its algorithm improves inspection effectiveness and enables manual visual inspections, operators often miss smaller leaks operation of drone-carried IR cameras, bettering long-disdue to visual fatigue. tance leakage detection accuracy. By using GLART combined with robots, tracks, or UAVs Gas leakage IR image enhancement uses image-stabilizing operated continuously, the 100,000 valves within a small compensation to solve jittering issues. Grey-scale IR images petrochemical plant could be inspected for leaks in approxirecorded by a moving IR camera are full of noisy signals. mately five calendar days, rather than the 100 to 500 working GLART solves this by identifying multiple moving features in days required for manual inspections. GLART increases the the images to determine a final motion vector. Images are possible scope and frequency of inspections in addition to analyzed pixel-by-pixel for differences among timewise conreducing inspection time. OG sequential images. OIL&GAS ENGINEERING DECEMBER 2018 • 17

Global business reduces motor downtime with ABB Ability™ smart sensors Summary: This global company has around 30,000 motors across 70 factories. These motors are often in constant operation, so their reliability and performance are critical to their success. A motor failure can slow production or cause costly, unplanned downtime. Until recently, the monitoring of a motor has been a manual process, consuming time and labor. But after piloting the ABB Ability™ Smart Sensor for motors, the company expanded their installation, and now they have invested in nearly 100 smart sensors for their production facilities in one single country.

Challenge: This business has around 30,000 motors across 70 factories globally. The motors are often in constant operation, so their reliability and performance are critical to ensure smooth production.

Solution: To continuously monitor the motors in critical applications, the company installed nearly 100 smart sensors in production facilities throughout an entire country. This allowed for predictive maintenance, substantially reducing downtime and extending equipment life.

The Smart Sensor is designed to continuously and remotely monitor a motor’s status, enabling predictive maintenance, substantially reducing downtime and extending equipment life. The devices are simply attached to the motor, without the need for wiring or machining. Upon installation, one of the smart sensors indicated the motor exhibited higher than normal vibrations. The data showed vibration levels in the alert zone which rapidly reached their limit. Based on this data, the company carried out a smooth, planned replacement of the faulty motor. The sensor was shifted to the newly installed motor and the vibration levels of the newly installed motor were normal.

Result: By implementing these sensors, the company identified motors which exhibited trends higher than normal, allowing for organized and planned replacements. By preventing only one motor failure, they have already recovered the cost of installing the sensors at its factories.

The savings from preventing only one motor failure has already recovered the company’s investment in smart sensors. They are pleased to have a tool which allows them to better avoid a shutdown and, thereby, increase the reliability of their equipment.

ABB Motors and Mechanical Inc. 479-646-4711 BALDORABBCOMsABBCOM

BLM Regulations, Flaring, and the Fox Thermal Model FT4A as the Solution Summary: The BLM has jurisdiction over most onshore leasing, exploration, development, and production of oil and gas on federal lands and each lease holder is responsible for maintaining instrumentation to track “sales gas” flowing from the well site. There are also rules for flaring waste gas at well sites. Modern thermal mass flow meters are uniquely positioned to solve these flaring measurement challenges as the technology has recently seen advancement in accuracy and the broadening of features. 1. 2. 3. 4.

Direct Mass Flow Inexpensive solution Accuracy and reliability Ability to measure changing gas compositions with gas selection feature

Challenge: In new gas pipelines, debris can break loose and cause damage to the meter’s probe. Getting a replacement must be quick especially when it’s difficult to schedule a gas sample analysis to be taken.

Solution: The FT4A’s rugged DDC-Sensor™ is tolerant to debris in the pipe and Fox Thermal can expedite an order in a week. The FT4A allows the user to select a custom gas mixture at the meter’s display or a free software tool.

Result: Production facilities see little to no downtime by getting their meters quickly. Selecting the gas on-site takes pressure off timing of the gas sample analysis.

In the harsh North Dakota environment, the Fox Thermal Model FT4A Thermal Gas Mass Flow Meter has been used at upstream oilproduction sites in the Bakken. A large Oil & Gas Company operating numerous production facilities across North Dakota reports that the Fox Thermal Model FT4A has been introduced into four key areas at their production facilities: s s s s

test vessels for allocation purposes flare gas lines for waste gas or excess gas flare gas lines from tank batteries vent gas from tank batteries

Thanks to its robust DDC-Sensor™ design and innovative Gas-SelectX® feature, the Model FT4A is making an impact in the gas measurement applications in the Oil & Gas Industry while meeting the accuracy requirements for BLM 3175.

[email protected] (831) 384-4300 www.foxthermal.com

Streamlining information management to enhance asset integrity and viability Summary: A global leader in the oil industry wanted to enhance asset integrity and operational efficiency of a key Norwegian platform by improving the availability, accuracy and completeness of their asset information. To meet this objective, the company leveraged L&T Technology Services’ (LTTS) Integrated Asset Management Services (IAMS) for the data migration exercise. The project scope included cleansing, enrichment and uploading of critical asset data into the company’s Engineering Data Warehouse (EDW) platform.

Challenge: Enhancing the asset integrity and operational efficiency of a critical client asset through improvements in the availability, accuracy, and completeness of all of their asset information.

Solution: • Cleansing, enriching and migrating 185,000 information assets to the client’s EDW platform • Forging relationships between all tags and document met data to enable users to drill down through the asset hierarchy

Result: The onsite-offshore delivery model enabled the client to customize and implement EDW, enabling a single source of high-quality engineering information. 500,000 pages of physical documents were digitized. Project costs and schedules are optimized with a robust accelerator-based automated approach.

The key task was to map the asset tags with the document metadata, to enable users to ‘drill down’ through the overall asset hierarchy. Following an initial two-week gap analysis and scoping study, LTTS prepared a detailed approach plan and methodology to cleanse, enrich and migrate 185,000 information assets (tags and documents) to the EDW platform. The documents and drawings along with associated metadata information were made available in multiple systems such as Document Management System (DMS), Engineering Data System (EDS) and Enterprise Resource Planning (ERP) tool. LTTS implemented an onsite-offshore delivery model to facilitate rapid resource ramp up, consistent execution, and cost containment. The offshore team was supported by a dedicated onsite team to manage inputs, escalations, reviews and risk management.

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