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O&G SUBSEA
Think leak first How the oil & gas industry can mitigate catastrophic subsea leaks By Raouf Hadad, P.E., MBA, and Han Hsien Seah, MEng
Figure 1: Age and status of subsea pipelines in the Offshore Continental Shelf of Gulf of Mexico, as of June 2020 (Source: BSEE Data Center, www.data.bsee.gov)
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he Gulf of Mexico’s aging oil & gas production infrastructure represents both an opportunity for bringing on low-cost subsea developments, and an ever-growing threat of catastrophic leaks, along with all its associated environmental, financial and reputational damage. Evolving technologies, actionable metrics and improved control room operator competencies provide new insights and promise. The Gulf of Mexico (GOM) is home to some of the most extensive submarine pipeline networks in the world. Over 40,000 miles of pipe were installed for hydrocarbon transport and chemical utility service since 1948, with approximately 17,000 miles of this network still actively used to transport as much as 1.8 million barrels of oil per day from offshore GOM fields. Much of the currently active pipeline network was installed in the 1990s through the 2010s, although a significant number of these pipelines were already operating when Intel fabricated the very first microprocessors in the 1970s. The rate of new pipeline construction has been declining for over a decade, yet oil production from the GOM set record highs in 2019. This is due to new subsea tiebacks and production platforms that leverage existing infrastructure to reduce development times and improve capital efficiency. This trend of reusing existing infrastructure means that pipelines will be operated beyond their initial design life. As pipelines age, they are subject to internal and external corrosion, fatigue from pressure and thermal cycling, damage from accidental anchor drags, contact rubbing with other pipelines at crossings, and occasionally, external forces like hurricanes and mudslides. Pipeline coatings can be damaged and sacrificial anodes eventually need to be replaced. Poor
welds may also fail unexpectedly in older pipelines. Moreover, many of these subsea pipelines cannot be intelligently pigged so it is difficult to quantify their actual condition. Today, the average age of GOM pipelines is 23 years, and this will continue to increase as the existing infrastructure is called upon to produce new subsea tiebacks. Good inspection and maintenance of the aging pipeline system will be paramount for ensuring the safe production of GOM oil & gas in the decades to come. Persistence of leaks However, despite best efforts at maintaining good integrity, leaks still occur, putting oil on the water. Every year, hundreds of offshore oil spills are reported to the National Response Center. Most of these spills are small, but every few years a major spill incident releases thousands of barrels of oil into the Gulf of Mexico, garnering nationwide attention and outrage. Most recently, subsea leaks in May 2016 and October 2017 resulted in 2,100 and 16,000 barrels of oil spilled, respectively. While the root cause of these two failures differed, they did share two common gaps — a lack of subsea leak detection (SSLD) alarming systems and inadequate operator training in leak detection. This lack of leak awareness directly led to significant delays in shutting down the leaking pipeline. Applying leak detection to pipelines that connect the subsea fields to the host platform has been particularly challenging as these fields are often located in deeper waters, so the hydrostatic pressure of the water column reduces the effectiveness of traditional leak detection methods such as the flowline pressure safety low (PSL). These pipelines also carry full well stream multiphase fluids, which exhibit complex flow behavior. Flowmeter instrumentation on subsea wells and flowlines is also limited, precluding use of mass-balance leak detection methods. Even when subsea multiphase flowmeters are available, for a variety of reasons, the measurements may not agree with the topside flowmeters. Following the GOM subsea leak incidents in 2016 and 2017, and at the request of the BSEE GOM region director, a subsea leak detection OIL&GAS ENGINEERING OCTOBER 2020 • 5
O&G SUBSEA
Figure 2: Subsea Leak Detection Methods – Detection Speed and Sensitivity Comparison (Source: OOC Subsea Leak Detection Working Group, Advanced Monitoring Subcommittee)
subcommittee was formed under the offshore operators committee (OOC), where representatives from BSEE and the oil & gas industry met to discuss concerns about the lack of effective subsea leak detection strategies and solutions. Through this collaborative forum, the OOC committee guidelines focused on a two-pronged approach: Advanced subsea leak detection monitoring: recommendations on the use of existing process data and control systems to monitor and detect a large leak and shut down in a timely manner. Training and competency: building and sustaining SSLD competency within the control room operators (CROs) and changing the offshore culture regarding leak detection to “Think leak first.” The operator’s controls philosophy and the condition of the existing control system on a given facility will drive whether the SSLD logic is implemented within: a) the subsea master control station, b) topsides process control system or c) as a standalone SSLD surveillance station. Depending upon the available subsea instrumentation, a combination of these protection methods can be incorporated within the control system to cover the full spectrum of SSLD during steady state, transient, and shut-in conditions: • Conditional rate of change (C-ROC) • Rate of change (ROC) • Static pressure comparison with hydrostatic • Meter in, meter out (MIMO)
Diagnosis and cure Subsea leaks are rare events, with a limited amount of (often proprietary) real-world process data available to train or validate leak detection methods. This is analogous to financial fraud detection, where in the vast majority of cases there is no fraud, but when fraud does occur, it is unlikely to look exactly like the last fraud case. Being too specific in defining what constitutes fraud (or a leak) carries the risk of completely missing future incidents — an unacceptable result. C-ROC overcomes this “overfitting” problem by simplifying the leak detection indicator to a single feature: the rate of change of pressure of a subsea flowline inlet. The laws of physics demand that the pipeline’s internal pressure will 6 • OCTOBER 2020 OIL&GAS ENGINEERING
rapidly shift towards the external hydrostatic pressure if integrity is lost. Unfortunately, many other normal operating modes and transients also cause rapid shifts in pipeline pressure, both upwards and downwards. This means the ROC method has high sensitivity (it will detect an actual leak with high likelihood) but poor precision (there will be many false positives). To improve its precision, several conditions are imposed for alarming. These conditions are carefully selected to identify when non-leak transient events are occurring, with a high degree of confidence, and without accidentally misclassifying an actual leak which would result in missed detection. Thus, C-ROC inherits the high sensitivity of the ROC method while also maintaining an acceptable frequency of false alarms, enabling it to be a sustainable and effective subsea leak detection method. Although C-ROC is conceptually simple, care must still be taken when implementing it on a given subsea system. Produced fluid properties, subsea system architecture, pipeline dimensions, sensor availability/reliability, data bandwidth and spare processor capacity will determine how best to design and configure C-ROC. More aggressive pipeline operations can also trigger additional false alarms, so it is not surprising to see false alarms recur every few weeks with the same crew. Just deploying C-ROC and forgetting about it is not enough because system flow behavior can change over time. Once the SSLD system is deployed, it is important for the operators to continue to monitor the system and fine-tune it. Successful implementations include incorporating robust performance monitoring metrics to quickly diagnose any deficiencies or inaccuracies, and to audit important indicators such as uptime, sensitivity and availability. Properly integrated system health monitoring makes it simple to: a) identify the cause of false alarms due to incorrect parameter settings, changing well/flowline conditions or operator actions; b) identify incorrect parameters that could have the SSLD system stuck in a reduced sensitivity state, which could lead to missed detection of real leak events and create a false sense of security; and c) demonstrate the efficacy and historical performance of the subsea leak detection system to regulatory inspectors. Operators are tasked with: a) constantly improving their control room operators’ competency, and reducing dependency on onshore
engineering personnel to identify a subsea leak versus other conditional anomalies such as slugging or transient states; b) changing the culture in the control room to “Think leak first;” and c) evaluating and documenting the CRO competency and performance. Important considerations Experience over several years researching, designing, implementing and sustaining subsea leak detection systems informs three areas as important considerations for operators seeking to install or modify SSLD systems. Carefully select the control system in which to implement the SSLD logic, considering the subsea MCS, topsides PCS/DCS, or installing a standalone surveillance SSLD station. Consider the following questions: • Does your existing control system (subsea MCS or PCS/DCS) on the offshore facility have sufficient memory and processing power to handle the SSLD logic? • Do you need to implement the SSLD logic across several offshore facilities? And if so, do they have the same control system? • Do your offshore facilities have aging/obsolete control systems with limited available support? • Does your control system OEM/integrator have experience in implementing the SSLD logic? These questions will help guide selection of the appropriate SSLD system. Where the Subsea MCS or the DCS/PCS has the necessary memory and processing power, where the same hardware design is implemented on a significant majority of the offshore facilities, and where the OEM/integrator has the requisite experience and capacity to support the SSLD system, then an integrated implementation within the MCS or DCS/PCS is a practical solution. In contrast, a recent case for a GOM independent operator illustrates a not-uncommon, hybridized environment, in this instance, a deployment of SSLD logic to six disparate facilities: • The facilities considered were all acquired from different operators and had different levels of complexity. • Some of the facilities had control systems that were aging/obsolete and had limited resources and support.
• The control systems on the various facilities were supported by different OEMs/integrators. The ideal scenario in this instance was not to attempt retrofitting the existing installations for a single-supplier solution, but to use standalone SSLD surveillance stations with industry-standard hardware/software routines and customized interfaces to the topsides control systems. The standalone SSLD surveillance station option allowed for a swift, high-quality and economical solution. The interface management effort by the operator was limited, and all that was required of the subsea OEM or topsides DCS/PCS integrator was to provide the data addresses for the necessary values in the chosen native communication protocol. Additional considerations Ensure that the SSLD control system includes integrated key performance indicators (KPIs) to monitor the SSLD availability and sensitivity. This is crucial for the accuracy, maintenance and sustainability of the SSLD system. The KPIs will identify any deficiencies of the initial settings and detect the changing dynamics of a flowline. This should not increase the cost of the SSLD implementation, especially if carefully considered during the SSLD design. Promote culture change for control room operators: “Think Leak First.” Cost control pressures and time constraints are a given in the industry. However, once the initial CRO training is complete, there needs to be an actionable plan in place for continuous improvement. The training is not a one-time exercise, but an element of organizational change management focused on a paradigm culture shift in the control room. Competency and CRO culture are inseparable. Superior SSLD technology alone is not enough. When the technology is coupled with a highly skilled control room operator whose mindset is a “Think Leak First” philosophy, the catastrophic environmental, financial, and regulatory damage of a subsea leak can be avoided. OG Raouf Hadad, COO, Ocean Edge Services, is a professional engineer licensed in the states of Texas, Louisiana, and Ohio. Han Hsien Seah is principal engineer for Ocean Edge Services. OIL&GAS ENGINEERING OCTOBER 2020 • 7
EVENTS & AWARDS
Vote for the best oil & gas industry products of 2020 Who will win the gold in 2020? Oil & Gas Engineering announces the finalists for its 4th annual Product of the Year competition By Amanda Pelliccione, Project Manager
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nnovation and advancement in technologies and best practices introduced within the past year into the oil and gas industry are recognized by means of our annual Product of the Year awards. Companies submitted new and improved products introduced between Aug. 1, 2019, and July 31, 2020, to
be judged. Below are the finalists in each category. It is now up to Oil & Gas Engineering readers to determine which products should receive gold, silver or bronze recognition. Vote online at oilandgaseng.com/NP4E by November 6, 2020. OG
Data & Analytics Machinery protection, condition monitoring system The Orbit 60 is a next-generation platform that collects and processes data, equipping operators with the right data and analytics to determine the health of their machines. It is an intrinsically cyber-secure machinery monitoring system with a built-in data diode, which enables secure one-way data transfer from the device to Bently Nevada’s flagship machinery management software System 1 for proactive monitoring and diagnostics. Bently Nevada, a Baker Hughes Business www.bently.com
Cloud-based energy management solution The EnergyPQA.com system is an energy management cloud solution that uses artificial intelligence-based machine learning to predict usage, perform analytics, and analyze and monitor power quality, resulting in reduced costs and improved power system reliability. In addition to displaying comprehensive energy, demand and power quality data for meters in facilities, users can implement the EnergyPQA.com application to compare energy usage throughout an enterprise in order to optimize energy consumption and demand. Electro Industries/GaugeTech (EIG) www.electroind.com
Augmented reality, asset monitoring application Augmented reality (AR) for Plantweb Optics helps organizations improve safety, productivity and efficiency — capturing knowledge and using it to upskill the workforce that will operate plants in the coming decades. AR improves situational awareness with navigation tools that help users quickly locate a single asset among thousands. Users can also view the field through a mobile device to see overlays that identify assets by name and provide an easily understandable health-status report. Emerson Automation www.emerson.com
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Data & Analytics Cloud, edge SaaS solution The Lavoro Platform is a fully integrated, device-agnostic, cloud and edge SaaS solution for the oil and gas industry. It is the alternative to point solutions and custom solutions that are difficult to scale and have higher lifetime costs than expected. The Lavoro Platform of applications allows operators to leverage data to its full potential to lower operating costs and increase production without the need to program. The Lavoro Platform encompasses Lavoro Edge, Lavoro Cloud, and robust applications that solve specific customer operational problems. Lavoro Technologies https://lavorotechnologies.com
Advanced analytics software Seeq R22 enables users to quickly investigate and share analyses from operations and manufacturing data sources to find insights and answer questions. Designed specifically for analyzing process data, Seeq R22 works across all verticals with time-eries data in historians or other storage platforms. The R22 release includes item-level permissions, significant processing speed increases, and additional collaborative features. Seeq Corp. www.seeq.com
Interactive piping, instrumentation diagram software Sphera’s Interactive piping and instrumentation diagram (P&ID) solution streamlines isolation planning with quick access to engineering documentation and interactive capabilities to mark-up P&IDs. Teams can view the realtime operational status of the asset, identify sources of operational risk as well as where to control and shutdown and ensure regulatory requirements. Sphera https://sphera.com
Asset performance management software APM 360 is a cloud-based asset performance management solution that covers overall asset health, real-time condition monitoring and predictive maintenance — accurately recommending specific actions. APM 360 leverages the Industrial Internet of Things (IIoT), artificial intelligence (AI) and failure mode and effects analysis (FMEA) to account for complex, dynamic behavioral machinery patterns and contextual data relating to the manufacturing process at large. The APM 360 AI-based anomaly engine looks for irregularities while the FMEA engine and Symphony AzimaAI’s proprietary scoring know-how acts as a double filter to ensure no false anomalies. Symphony AzimaAI https://symphonyazimaai.com
Asset management software The IIoT Plant Asset Management (PAM) software is an OpreX Asset Management and Integrity brand solution that prevents costly downtime and equipment repairs while improving safety for process equipment and plant personnel. It is a unique vibration monitoring program using Yokogawa’s Sushi Sensor with long range wireless networking that applies AI and machine learning analytics for very early anomaly detection in rotating equipment. IIoT PAM improves plant efficiency by enabling rotating equipment managers to use their resources more effectively and make more informed decisions. Yokogawa Corporation of America www.yokogawa.com/us OIL&GAS ENGINEERING OCTOBER 2020 • 9
EVENTS & AWARDS
Data & Analytics Predictive asset management software Plant Resource Manager (PRM) R4.03 is a predictive asset management solution that centrally manages status and maintenance information in high volume from automation and production assets. PRM provides maintenance support including online functions for monitoring and diagnosing devices and equipment. By allowing the plant operations and maintenance staff to centrally manage and visualize plant equipment data, PRM enables significant cost savings through improved maintenance efficiency. PRM supports intelligent devices using communication protocols such as Foundation Fieldbus, HART, Profibus and ISA100.11a Field Wireless. Yokogawa Corporation of America www.yokogawa.com/us
IIoT & Process Control Industrial control system The OSA Remote +Flow industrial control system integrates Flow-Cal algorithms and PLC/PAC/ RTU functionality into a powerful, secure control module. With this single automation platform and free IEC 61131-3 engineering software, users can configure oil and gas measurement and custody transfer applications requiring 10 to 20 hard I/O; program the I/O and control strategies in the field; and connect via standard protocols such as HART, Ethernet IP and Modbus, alongside OPC UA and MQTT. Bedrock Automation https://bedrockautomation.com
Edge analytics device The AMS Asset Monitor edge analytics device digitalizes essential asset data and analytics for better operations performance and improved decision making. AMS Asset Monitor provides actionable insights into essential assets that were previously monitored only with infrequent assessments. This edge analytics device will connect with Emerson’s Plantweb Optics asset performance platform to provide key operations personnel with instant asset health details for operations and maintenance decision making. Unlike typical analytics devices that send data to an historian or the cloud to be processed later, AMS Asset Monitor provides analytics at the edge, performing calculations at the device. Emerson Automation www.emerson.com
Safety I/O module The Allen-Bradley FLEX 5000 safety input/output (I/O) module can meet the needs of producers in process and heavy industries that require fixed field-wiring terminations or both vertical and horizontal I/O mounting in distributed safety applications. The FLEX 5000 I/O module provides distributed safety I/O for the Allen-Bradley Compact GuardLogix 5380 and 5580 controllers. The safety module can be combined with FLEX 5000 standard I/O modules to achieve integrated safety and control in one distributed I/O platform. Rockwell Automation www.rockwellautomation.com
Medium-voltage VFD The Allen-Bradley PowerFlex 6000T medium-voltage variable frequency drive (VFD) shares the same control hardware, firmware and network interface software used in Rockwell Automation’s latest generation of PowerFlex 755T low-voltage drives. Using a common control platform across an entire installed base of VFDs lowers integration, operation and support costs. A common platform also reduces product-specific training requirements and spare parts inventory. Rockwell Automation www.rockwellautomation.com 10 • OCTOBER 2020 OIL&GAS ENGINEERING
IIoT & Process Control Edge computing platform The ztC Edge 100i/110i is a zero-touch, fully virtualized and self-protecting computing platform, specifically designed for industrial edge environments. With built-in remote management and user-installable in less than an hour, ztC Edge significantly reduces the IT burden for virtualized computing at the edge. Its self-protecting and self-monitoring features help reduce unplanned downtime and ensure availability of business-critical industrial applications. Stratus Technologies www.stratus.com
Process performance management application Performance 360 is a process performance and optimization solution for the process industries including petrochemical, pharmaceuticals, food and beverage, power generation and discrete manufacturing. It combines process condition insights, performance metrics and process history. Performance 360 uses IIoT and carefully curated artificial intelligence and deep learning technologies to predict how a process will perform in the future and identify potential process disruptions, quality issues and trip conditions. Symphony AzimaAI https://symphonyazimaai.com
Integrated HMI, SCADA platform Developed for plant, telemetry, or hosted systems of any size, VTScada’s unique design integrates all core SCADA components into one easy-to-use package. It replaces thirdparty add-ons with integrated features like Enterprise historian, security, reporting, alarming, alarm notification, version control and thin clients. This removes risk and stress from every stage of the software lifecycle, from pricing and licensing to development and support. Since versions are never retired, VTScada applications can be scaled and updated indefinitely. Trihedral www.vtscada.com
Fired asset management for process industries CombustionONE delivers a transformational component to the solution to deliver a step change in holistic, site-wide fired asset operation. CombustionONE applies to heaters, furnaces, steam methane reformers, crackers and boilers—essentially any asset with a flame. CombustionONE comprises hardware, software and turnkey project services in the form of tunable diode laser spectroscopy analyzers, dedicated control and safety systems, and digital twin technology. Yokogawa Corporation of America www.yokogawa.com/us
Plant operations optimizing software The Dynamic Real Time Optimizer (RT-OP) is a dynamic real time optimization solution that optimizes an asset, an entire plant or circuit within a refinery/petrochemical plant to ensure it is continuously responding to market signals and disturbances on a few minutes’ basis. The solution combines Petro-SIM models and Platform for Advanced Control & Estimation APC. Dynamic RT-OP calculates the optimal values for plant independent variables with a high level of accuracy. Yokogawa Corporation of America www.yokogawa.com/us
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EVENTS & AWARDS
Machines & Equipment Virtual reality training software For oil and gas manufacturers, the Mimic Field 3D virtual reality (VR) simulator makes training safer and more cost effective. The simulation software uses a site’s CAD drawings and digital twin architecture to create a three-dimensional model of the operating environment. In the virtual environment, operators can use VR equipment to perform the same tasks they would in the real world, gaining familiarity with assets and controls, such as pumps and compressors, without the risk or production interruption of practicing on live equipment. Emerson Automation www.emerson.com
Ultrasonic gas flowmeter The Proline Prosonic Flow G 300/500 ultrasonic gas flowmeter is ideal for demanding applications, measuring both dry and wet gases with high precision (±0.5%), unmatched repeatability and high reliability — even when process and ambient conditions fluctuate significantly. The robust industrial design makes it possible to operate the flowmeter long term without maintenance, saving time and money. The meter operates at process temperatures up to 150°C and pressures up to 100 bar and can be ordered with built-in pressure and temperature sensors. Endress+Hauser http://us.endress.com
Back pressure valve system The Latch Back Pressure Valve (BPV) System provides a safe operating environments and greater efficiencies for drill-through operations with the use of a proprietary latch in place of a thread profile. Threads on most other BVPs are at risk of being damaged when the operator runs the drill bit through the thread profile, increasing the risk of a catastrophic pressure event or nonproductive time. The unique latch design of the Latch BPV System eliminates the threat of damage and potential pressure integrity issues, protecting personnel and equipment. Weir Oil & Gas www.global.weir
Offline cementing solution The Offline Cementing Solution for 5.5-inch production casing generates substantial reductions of nonproductive time and operating costs. Engineered for Weir’s Unitized Lock-Ring wellhead, the Offline Cementing Solution sets up in 8 minutes without tools with Quick Connect. Operators do not have to make any adjustments to their BOP configuration. Additionally, the Offline Cementing Solution offers the added benefit of being able to perform testing isolation in the backside of the well for greater security and well integrity. Weir Oil & Gas www.global.weir
Frac valve seat The SPM EdgeX Valve and Carbide Seat increases seat life 6X and doubles valve life compared to conventional offerings to provide substantial cost savings and reduction of nonproductive time. Valves, seats and packing are the most significant maintenance expenses on a frac site, making operators focus on reducing costs related to fluid end maintenance and downtime. The SPM EdgeX Valve and Carbide Seat employs tungsten carbide strategically in key wear areas to deliver dramatically longer life than existing valve seats, including environments with large particles, which means less time spent pulling seats. Weir Oil & Gas www.global.weir
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AI & ADVANCED PROCESS CONTROL
Artificial intelligence identifies process abnormality causes When other methods fail, AI is a critical finding and solving tool By Dr. Hiroaki Kanokogi
Figure 1: An OODA loop calls for constant reevaluation of previous analysis efforts. All figures courtesy: Yokogawa
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il refineries and petrochemical plants are meant to run continuously, usually for years at a time, to maintain critical production obligations and achieve financial goals. Consequently, problems disruptive enough to interrupt production or impair product quality are serious and to be avoided. Disruptions can be related to equipment failure, such as a motor burning out, or stem from a process problem beyond operators’ ability to correct. Plant managers look for ways to determine when a problem is on the horizon, before it escalates into an unscheduled outage. For equipment, trouble brewing can be spotted with basic diagnostic sensors, such as bearing noise monitors, which can warn the maintenance department when a critical parameter is changing. Process problems are often more subtle but can be just as disruptive, as we’ll discuss in two examples later in the article. In these situations, some process variable begins to move into dangerous territory and can’t be detected by the automation system, leaving operators to determine corrective action. The question emerges, was there anything in the production data prior to the incident that pointed to the situation developing? Operators and process engineers may pore over data for hours looking for clues, only to throw up their hands. This challenge spurred a search for new technologies to provide a solution.
Help from AI Artificial intelligence (AI) is the topic of many discussions these days with many suggestions as to how it will improve manufacturing in the future. A more practical approach is exploring what AI is doing right now to solve problems, as described below. Human operators rely on the automation system to show them the overall state of the process. This should help them identify when there are signs of abnormalities developing and the cause. In reality, the automation system shows operators a wide range of variables, leaving them to make their best interpretation. If a problem occurs, operators must respond quickly and correctly to forestall an incident. But what they really want is a system capable of indicating the state of the process and identifying which sensor to focus on when an error occurs. AI can identify the main factors contributing to abnormal situations and point out the specific sensors indicating the causes. This allows operators to concentrate on a small number of manageable elements to solve the problem, rather than trying to deal with a much large number of sensors, most of which do not relate to the immediate problem. This process begins with operators identifying a specific problem. Data scientists work with domain experts — the process engineers — to build a system incorporating a learning model (Figure 1). This combination of artificial and human intelligence (AI+HI) works with an observe-orientdecide-act (OODA) loop as an effective decisionmaking procedure: 1. Observe — intelligent sensing 2. Orient — advanced analytics 3. Decide — real-time and astute decision making 4. Act — agile actions for value creation. Observe includes identifying the problem and setting the goal, defined as the process state in which the problem will be solved. This calls for OIL&GAS ENGINEERING OCTOBER 2020 • 13
AI & ADVANCED PROCESS CONTROL
Figure 2: When graphed, the catalyst health index shows when deterioration is reaching the point where product quality will be affected, indicated by the red arrows. (The gray bands are where the process is shut down for catalyst replacement.)
narrowing down the process data and maintenance information necessary for analysis, and then translating the problem into specific tasks to set issues. Orient determines which direction the analysis proceeds to solve the defined tasks. It combines AI technology, domain knowledge, and data scientists’ expertise to dig into the data for analysis. Decide examines what actions are suggested by the analysis results. If directions seem to be going off on a tangent, plant personnel go back to the first step and see if the problem is defined correctly. The participants must agree on whether or not to implement a given plan. Act puts the consensus plan into effect. This may involve adding capabilities such as edge computers, cloud computing, and data storage. If it proves necessary to redefine the tasks, plant personnel must return to the first step and reconsider the overall approach. Applying AI An ethylene producer contacted Yokogawa and asked for assistance in solving a list of reoccurring process-related problems. Working with the company’s internal problem-solving team, all the participants engaged in a workshop to become familiar with the methodology and understand how the project would proceed. Together, the team identified possible causes for each problem from hundreds of sensor parameters. The parameters were used to monitor the operational state of the equipment and create an AI model to detect anomaly in the equipment and understand the plant status. This methodology was applied to eight projects, we will look at two. CASE 1: Benzene Production Reactor An ethylene plant produces cracked gasoline, which is converted to benzene by adding hydrogen in a reactor with a catalyst. This also removes impurities. To maintain a stable reaction, it is necessary to modulate hydrogen flow and reaction temperature to match raw materials. The catalyst deteriorates gradually, resulting in lower catalytic performance. However, since
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there is no method to quantify activity, catalysts are periodically activated or replaced following a schedule based on run time or a calendar. This means that some catalysts are replaced even though they are still serviceable, creating extra maintenance. Conversely, unexpectedly rapid deterioration of the catalyst ahead of replacement time causes an increase in impurity levels, resulting in defective and unsellable product. This creates several major problems: • Reduced production with missed targets • Loss of raw materials • Increased cost of disposal • Shutdown for emergency maintenance. The observe phase concluded the operators needed KPIs for catalyst activity because without them they couldn’t tell when conditions called for a catalyst change prior to costly production problems. This knowledge would optimize maintenance and avoid product losses. The orient phase examined production data for the previous two years, during which time there were three catalyst changes: two following the normal schedule, and one emergency replacement due to production problems. Process data during stable operation and the time just before the emergency maintenance became training data and was analyzed by AI to create a training model, which was then applied back to the data. This resulted in a catalyst health index, formed from a synthesis of multiple measured process variables identified by the AI analysis, which became the very KPI the operators needed. When the two years of data was examined using this index (Figure 2), it became clear that catalyst health could be determined, and it was possible to decide when it needed to be replaced prior to product degradation. Steps to be taken during the decide and act phases became clear. Operators now follow the catalyst health index in real time and schedule catalyst changes based on condition. This results in maximum catalyst life while avoiding product degradation and emergency shutdowns. CASE 2: Cracking Furnace Cooling Tower In ethylene plants, ethane, naphtha, and other raw materials are heated in a cracking furnace. To prevent excessive cracking, the hot gas moves to a cooling tower, where cold water is
as illustrated in these examples. AI becomes the tool to extend the capabilities of HI once the human domain experts define the root problems to be solved. AI Going Forward Today, AI in process manufacturing is limited to analysis, rather than a primary method of real-time process control. However, this is changing. Yokogawa and others have been involved in experiments to replace traditional PID-loop-based control strategies with a single comprehensive AI system able to learn how to optimize control of a single process unit or an entire refinery. After all, if AI techniques can control a self-driving car, why not an ethylene plant? The answer is, it can and will. Yokogawa has introduced machine learning as a practical technology for process manufacturing, and leveraging the results, is accumulating application cases in laboratory settings and actual facilities. This requires developing technology to ensure safety and versatility, with key abilities, including: • Safe learning methods • Robust ability to handle disturbances • Fast response to changes in set points • Continuous online learning • Applicability and transferability of models to multiple facilities.
Figure 3: The actual process data (upper graph) shows the loss of control in 2018. The index created (lower graph) shows how operators can now control the parameters to avoid the cooling capacity loss.
Dr. Hiroaki Kanokogi is director of the IA-Product & Service Business, Yokogawa headquarters. After graduating from the University of Tokyo, he developed machine learning for natural language processing at Microsoft. He has been researching on industrial use of AI technology at Yokogawa Electric since 2007. OIL&GAS ENGINEERING OCTOBER 2020 • 15
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sprayed into the stream to reduce the temperature to <35 °C. The heated water is sent to various heat exchangers in the plant and eventually recirculated. During the summer, the cooling tower seemed to lose capacity, making it difficult for operators to control the process. The cracking reaction continued and impeded separation of the desired components, causing poorer product quality and yield. For several years, this situation was reluctantly tolerated as an unavoidable seasonal effect, at least until the problem unexpectedly vanished during the summer of 2019. There were no obvious climatic reasons, so the engineering team wanted to find out what had changed, and how to keep the problem from returning. The observe phase set the two-part goal: from plant data, establish an indicator of an operating condition causing the loss of cooling effectiveness and identify a parameter closely related to this temperature rise. By changing the parameter, operators could improve operation. The orient phase examined data from the previous two years, 2018 and 2019 (Figure 3), when the temperature increased and stayed flat, respectively. Whatever happened in 2019 solved the problem, but no one could positively identify the specific change. The AI analysis used to build the training model suggested several candidate parameters, including cooling tower temperature and cooling water flow rate. The analysis found these parameters affected the temperature and flow rate of the heat exchanger adjacent to the cooling tower, and both were closely related to the temperature in the cooling tower. The training model created an index capable of predicting the effectiveness of the cooling tower. This was a synthesis of process variables, both upstream and downstream from the cooling tower itself. The higher the index, the less likely there would be conditions capable of causing loss of cooling capacity and product problems. The pleasant surprise of improved temperature control experienced in the summer of 2019 became reproducible at will. Understanding what AI is and what it can do is difficult to pinpoint because it takes so many forms. Within process manufacturing, it can help solve many types of problems because it is a methodology as much as a technology. It requires engagement between HI and AI