Applications of Advanced Analytics at Saudi Aramco: A Practitioners

Subscriber access provided by EDINBURGH UNIVERSITY LIBRARY | @ http://www.lib.ed.ac.uk

Process Systems Engineering

Applications of Advanced Analytics at Saudi Aramco: A Practitioners’ Perspective Rohit Patwardhan, Hamza A. Hamadah, Kalpesh M. Patel, Rayan H. Hafiz, and Majid M. Al-Gwaiz Ind. Eng. Chem. Res., Just Accepted Manuscript • DOI: 10.1021/acs.iecr.8b06205 • Publication Date (Web): 20 Mar 2019 Downloaded from http://pubs.acs.org on March 26, 2019

Just Accepted “Just Accepted” manuscripts have been peer-reviewed and accepted for publication. They are posted online prior to technical editing, formatting for publication and author proofing. The American Chemical Society provides “Just Accepted” as a service to the research community to expedite the dissemination of scientific material as soon as possible after acceptance. “Just Accepted” manuscripts appear in full in PDF format accompanied by an HTML abstract. “Just Accepted” manuscripts have been fully peer reviewed, but should not be considered the official version of record. They are citable by the Digital Object Identifier (DOI®). “Just Accepted” is an optional service offered to authors. Therefore, the “Just Accepted” Web site may not include all articles that will be published in the journal. After a manuscript is technically edited and formatted, it will be removed from the “Just Accepted” Web site and published as an ASAP article. Note that technical editing may introduce minor changes to the manuscript text and/or graphics which could affect content, and all legal disclaimers and ethical guidelines that apply to the journal pertain. ACS cannot be held responsible for errors or consequences arising from the use of information contained in these “Just Accepted” manuscripts.

is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Published by American Chemical Society. Copyright © American Chemical Society. However, no copyright claim is made to original U.S. Government works, or works produced by employees of any Commonwealth realm Crown government in the course of their duties.

Page 1 of 48 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Industrial & Engineering Chemistry Research

Applications of Advanced Analytics at Saudi Aramco: A Practitioners’ Perspective Rohit S. Patwardhan*, Hamza A. Hamadah, Kalpesh M. Patel, Rayan H. Hafiz and Majid M. AlGwaiz Process & Control Systems Department, Saudi Aramco, Dhahran, 31311

KEYWORDS: Advanced Analytics, Control Performance Monitoring, Alarm Analytics, Image Analytics, Big Data Analytics, Machine Learning, Artificial Intelligence.

ACS Paragon Plus Environment

1

Industrial & Engineering Chemistry Research 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 2 of 48

ABSTRACT

The well instrumented process industry collects vast amounts of structured and unstructured data from its assets in real time. Some of this data gets stored as conventional time series data while some is processed to generate alarms, alerts and other types of unstructured data. Managing this big data which is rich in diversity, volume, veracity, and velocity, to generate actionable insights is a challenge that is best tackled through the use of advanced analytics. The area of advanced analytics has been expanding with the rapid rise of artificial intelligence (AI) tools that are capable of processing complex data types such as video and audio, in real time.

In this article, applications involving operational data and advanced analytics tools that are used to generate predictive insights, are discussed. The case studies illustrate the different data types present in industry – time series data, alarm and event data and image data – and the machine learning methods used to analyze them in order to generate insights. The applications discussed cover a spectrum of advanced analytics techniques ranging from conventional time series analysis, spectral analysis, clustering, convolutional neural networks and text analytics. In conclusion, some perspectives on the future role of advanced analytics and AI technologies in the process industry are shared.


2

Page 3 of 48 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


1. Introduction The last decade has seen significant innovations in the digital realm1 leading to what is widely considered as the fourth industrial revolution (IR 4.0). Artificial intelligence, robotics, unmanned aerial vehicles, augmented and virtual reality (AR/VR), mobility, 3D printing are all examples of disruptive technologies which are having a game changing impact on manufacturing2. Industry 4.0, a term first coined in Germany3, is driving changes at a fast pace in the industry, especially in the manufacturing sector. Artificial intelligence (AI) and machine learning (ML) algorithms and methods are becoming increasingly powerful at extracting relevant insights from vast amounts of diverse data. Recent advances in AI have led to algorithms that can process unstructured data such as images, sounds and language with increasing accuracy often matching or outdoing their human counterparts6. The process industry is awash in data. Modern Distributed Control Systems (DCS) and Supervisory Control and Data Acquisition (SCADA) systems have made thousands of sensor readings available in real time to operators, engineers and management. There is increasing emphasis on effectively utilizing this data to improve safety, reliability and efficiency of the operating assets and facilities. The use of data and models is not new to industry. Both are used at different time scales ranging from seconds to months in conjunction with a variety of models to drive decision making in plants. For example a simple Proportional Integral Derivative (PID) control loop runs typically every second to do closed loop control of a process variable such as flow or pressure. The tuning of this PID loop is increasingly a model based exercise wherein a simple time series model describing the input-output relationship is extracted from suitable data. More sophisticated multivariable model predictive control (MPC) schemes run every minute to decide optimal values of many manipulated variables to drive an operating unit to its most efficient


3


Page 4 of 48

operating point. Multivariable data based time series models form the foundation of these applications. This technology has been successfully applied in the industry for more than three decades4 and can be considered to be a form of prescriptive analytics5. First principles or physicsbased rigorous models are used for design, troubleshooting and optimization purposes. These are steady state models in contrast with the dynamic time series models used for control. At an even higher level, steady state gain-based models are used to drive the planning and scheduling processes, these models that combined multiple process areas with financial and market data, are used on the time scale of weeks and months to generate optimal plans and schedules to meet production objectives of the organization. The models used here are based on mass balances for entire plants and facilities. Data from sensors as well as lab measurements along with financial information, is used in combination with the models to drive the decision making processes. Thus, data and models are at the heart of the critical business processes at an operating facility and supply chain level. However, with the recent advances is artificial intelligence and machine learning a number of possibilities have opened up with regards to the use of unconventional data such as a video, audio and text6. Audio signals from sensors that are listening to this equipment could be analyzed in conjunction with conventional measurements, through AI, and incipient failures detected. Human operators interact with the plant through the DCS which communicates with them via alarms and events. The alarms and events are essentially text (and sometimes audio) messages which are used to alert the operator to certain plant conditions and elicit responses from the operators to guide the plant to a safe operating envelope. The process industry has long been a user of traditional multivariate statistical techniques such as principal components analysis (PCA), Partial Least Squares (PLS) for developing inferentials or soft sensors as well as monitoring processes7-12. The success of these techniques has been in


4

Page 5 of 48 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


part due to their simplicity along with their ability to deal with large amounts of correlated measurements. In contrast Artificial Neural Networks (ANNs) have not been used as extensively in the industry. This can be attributed in part to inherent complexity of the ANN structure and the fact that they do not lend themselves naturally to explaining causal relationships that are well understood in chemical processes. Other applications such as Control Performance Monitoring (CPM) are a type of analytics application, which employ time series analysis of operating data to estimate models of closed loop behavior, to provide insights into control loop behavior13-14. The purpose of this paper is to explore the application of so-called advanced analytics techniques to some of the conventional as well as unconventional data types found in industry – process measurements, video camera feeds in the plant, text messages such as alarms. The next section provides an overview of advanced analytics and the underlying techniques. Given the rapid changes happening in this area, it is important to understand the advanced analytics domain and its relationship to AI in particular. This is followed by case studies involving different types of analytics applications – control performance monitoring, alarm analytics, image analytics, predictive maintenance and use of clustering for model development. The last section concludes with some perspectives on the future applications of analytics and AI in the process industry.

2. Advanced Analytics Overview 2.1. Analytics Maturity Levels Analytics is the extensive use of data, statistical and quantitative analysis, explanatory and predictive models, and fact-based management to drive decisions and actions15. There are four levels of advanced analytics maturity - descriptive, diagnostic, predictive, and prescriptive16: •

Descriptive (What happened): This type allow users to represent a data set in terms of summary statistical parameters or KPIs and a graphical display of patterns or trends that


5


Page 6 of 48

are in the data set – operational intelligence dashboards summarizing performance of specific assets for example. •

Diagnostic (Why did it happen): This type of analysis involves looking at historical data to determine the root causes of an incident using statistical techniques. For example detailed data mining and statistical analysis to quantify the contributors to corrosion in a pipeline is a diagnostic analytics exercise.

•

Predictive (What will happen): This type of analysis includes techniques to extract important variables and develop model where the value of one variable can be predicted from the values of other variables – Predicting crude cut points for example or coke buildup in a crude column pre-heater.

•

Prescriptive (What steps to take): This type of analytics determines the actions that are necessary, to reach a desirable outcome. This is the most valuable kind of analytics and usually results in rules and recommendations for actions. For example what step should be taken to improve the efficiency of a pump or a turbine could be the outcome of prescriptive analytics. This can take the form of open loop advisory systems or closed loop systems such as Advanced Process Control (APC).

The first level of analytics (descriptive) can be classified as a mature application in general. Enterprise wide monitoring systems are increasingly deployed at Saudi Aramco and other companies, with the purpose of benchmarking fleet wide assets and driving continuous improvement processes. Except descriptive analytics, which usually involves KPIs that are aggregated over a large number of assets, the other type of analytics – diagnostic, predictive and prescriptive – involve the use of some type of models. The choice of the model type and complexity is governed by the needs


6

Page 7 of 48 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


of each specific problem. The area of artificial intelligence, which includes machine learning as a subset, offers an increasingly large choice of modeling algorithms.

2.2. Data types Data Acquisition and Historian Systems (DAHS), such as PI (OSIsoft), PHD (Honeywell) and IP.21 (AspenTech), have enabled significant growth in the availability of process data. Moreover developments in OLE for Process Control (OPC) interfaces has allowed integration between several network layers and applications. OPC Unified Architecture (UA), which can provide contextual information in addition to the sensor values, is the next generation of OPC that is expected to be the underpinning of connectivity for IR 4.018,19. The term Big Data is often used in relation to Analytics and relates to the volume, velocity, variety and veracity of data17. There are many types of data in industry, and below is a list of the most common data types: •

Time series: Time series are the simplest form of data, and most common in Saudi Aramco facilities. A time series is a sequence of real numbers that represent the measurements of a real variable at equal time intervals (e.g. flow, temperature, pressure, level, quality of a hydrocarbon product).

•

Categorical data: Is used for observed data whose value is one of a fixed number of nominal categories, often, categorical data are summarized in the form of a contingency table. Alarm data is an example of this type where in an alarm can be categorized into no alarm, high alarm, high-high alarm, low alarm, low-low alarm states for example.

•

Binary data: A binary attribute is an attribute that has exactly two possible values, such as true or false, open or close. Shutdown status of a pump is an example of binary data.

•

Unstructured data: Unstructured data can be in the form of audio, video, operating logs, maintenance records, financial records, logistics information such as deliveries etc.


7


Page 8 of 48

2.3. Analytics Methodology and Technologies Standard work flows such as CRISP-DM20 have been defined for carrying out data mining and analytics related projects. An analytics based approach typically comprises of the following tasks: •

Data Collection and Preparation: This is a task that often consumes a significant portion of the overall project time. As an example, one may be a looking at a data set from a reactor consisting of 150 variables and 1 years’ data sampled at 1 minute frequency. One must ensure that the data is: o Of the right quality – uncompressed, no bad data, gaps etc. o Meaningful – contains information relevant to the defined problem o Complete – not missing any key measurements in the problem boundary

•

Model Development and Analysis: Once an appropriate data set is available, the next task is to determine relationships or patterns in the data that relate to the incident or event one is trying to analyze or model. Typically a variety of tools/algorithms are available for analysis and modeling purposes. The end goal of the model/analysis may be to uncover previously unknown relationships in the data and/or to develop a predictive model to forecast future changes in process conditions. Once a model is developed it must be analyzed and validated against different data sets before it is deemed fit for final use.

•

Deploy, Use and Maintain: The developed model/analysis may be used either in an offline or online manner. Offline deployment would be used to discover new knowledge regarding the process. Online deployment may be relevant where real time predictive and prescriptive capabilities are needed. Real time deployment involves addressing integration challenges and can often influence the decision on whether to use an off-the shelf tool vs. a custom built one.


8

Page 9 of 48 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


There exist many free and commercial software programs that can provide data analytics capability to varying degrees. Open source tools such as R and Python are increasingly preferred by data scientists for performing advanced analytics to varying degrees. Powerful deep learning frameworks such as TensorFlow and Keras are available via the Python environment71. There are many commercial technologies on offer in the analytics area as well. The offerings range from enterprise wide data science platforms to off the shelf predictive analytics applications that focus on monitoring of specific equipment such as pumps or compressors. There are data science platforms from a number of companies such as SAS, IBM, Microsoft RapidMiner, Mathworks etc. which offer a large variety of machine learning tools. More traditional times series modeling tools, that focus on real time closed loop applications for model predictive control (MPC) and inferentials, are available from a number of vendors such as AspenTech, Honeywell, Yokogawa21. MPC can be considered as a form of predictive and prescriptive analytics5 and is a mature technology in use in industry for the last three decades4. Modern data science platforms offer a range of machine learning algorithms. These machine learning algorithms can be classified into supervised, semi-supervised and unsupervised learning. Supervised learning involves a target or labeled variable which is used to train one or more models. The model developed using the training data set can be used to infer the target variable on a new data set to generate predictions. Supervised learning can be applied to the following problem types: •

Classification: When the data is being used to predict a categorical variable. This is the case when assigning a label or indicator, for example the state of a compressor – running or shutdown. When there are only two labels, it is called a binary classification problem. When there are more than two categories, it is called multi-class or multinomial classification.


9


•

Page 10 of 48

Regression: When the target variable is a continuous variable, the problem becomes a regression problem. An example is the generation of the shift vectors using simulation data that are used for linear programs (LPs) in short range planning of process operations.

Either of these approaches could be used to make predictions about the future based on current and past data. An example is future prediction of crude end points on a crude column using historical data and process models. Unsupervised learning involves completely unlabeled data. In this case the machine learning algorithm is asked to discover patterns present in the underlying data, such as clusters of similar data points, or a lower dimensional underlying structure. •

Clustering: Grouping sets of similarly behaving variables according to some criteria. This is often used to segment the whole dataset into several groups. Further analysis can be performed within individual groups to find intrinsic patterns.

•

Dimensionality Reduction: Reducing the number of variable under consideration. For example, in many cases due to abundant sensor measurements, the raw data may have high dimensionality features as a result of measurement redundancy. Reducing the dimensionality helps find the true underlying relationships which are governed by physical laws – mass and energy balances. Principal Component Analysis (PCA) is an example of an unsupervised dimensionality reduction technique. The statistical techniques have been used in the process industry for some time, whether for inferential building or processing monitoring7-12.

Artificial intelligence is a rapidly evolving area with new algorithms appearing at increasing frequency. Deep learning, reinforcement learning, automated machine learning are recent trends that are being increasingly applied to a variety of applications such as natural language processing


10

Page 11 of 48 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


(NLP), image processing and game playing for example6,26. Convolutional Neural Networks (CNN) 27 and Recurrent Neural Networks (RNNs) 28 are types of deep neural networks. CNNs have been found to work well in processing images, video, speech, audio29 while RNNs have been successful at processing sequential data such as text and speech30. More recent machine learning approaches such as reinforcement learning optimizes the behavior of an agent based on feedback from the environment. Machines analyze different scenarios to discover which actions yield the greatest reward, rather than being told what actions to take. Trial-and-error and delayed reward distinguishes reinforcement learning from other techniques. Researchers are starting to look at how these techniques will apply to process control31. The process industry, which has a rich history of using first principles models at every stage in the lifecycle of a plant, is beginning to explore the uses of these techniques for more non-traditional applications. Advanced analytics adoption in industry is increasing at a rapid pace in industry with Shell, Chevron, Dow, BP22-25,79-80 and others reporting a number of AI applications in the upstream, supply chain, audio and video processing etc. Though many of the initial applications were developed using cloud based AI or Machine Learning environments, there is an increasing trend towards deploying these applications close to the device or equipment. This is referred to as edge intelligence/analytics/computing69. In the next section we will describe some advanced analytics applications at Saudi Aramco ranging from the use of time series models to learn asset behavior, to the use of machine learning and AI techniques for predictive maintenance and image analytics.

3. Advanced Analytics Applications At Saudi Aramco, the use of models and predictions is historically an integral part of the business starting from the design stage all the way to operations. These models are often physio-chemical


11


Page 12 of 48

models which are used to describe how the process equipment will behave. They are used for a variety of tasks including design of the equipment and the entire plant, planning of the production across the supply chain etc. Simpler data based time series models are used to control and optimize individual units. The advent of machine learning and artificial intelligence has forced practitioners to look at using these advances in technology to further improve operational decision making. A corporate Digital Transformation strategy and roadmap75, provides a guiding framework for the use of IR 4.0 technologies such as Artificial Intelligence, Machine Learning, Robotics, Unmanned Aerial Vehicles (UAVs) amongst others.

3.1. Control Performance Monitoring Control Performance Monitoring (CPM) technology is an example of technology making the successful transition from research into practice. Harris (1989)32 provided the early foundations of controller monitoring theory using the minimum variance benchmark for single input single output controllers. The idea was a simple yet powerful as it laid the groundwork to estimate the performance of a single loop controller using knowledge of the process time delay and routine operating data. A model of the closed loop was derived using the operating data and minimum variance benchmark could be derived from this model. Shah and Huang (1997) extended this theory to multivariate systems using the notion of the interactor matrix which is the generalization of the time delay for the multi-input multi-output (MIMO) case33-34. Patwardhan et al. (2003) describe a way of assessing constrained model predictive controllers using the objective function approach35. There has been a lot of recent activity focused on diagnosing the model plant mismatch from operating data to guide model improvements36-38.


12

Page 13 of 48 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


The first commercial software for controller monitoring started appearing in the late 1990s – ProcessDoctor (Matrikon)44 and LoopScout (Honeywell)43. This spurred a number of early implementations in the industry by the early adopters. Industry data also provided strong motivation for adoption of controller monitoring technology as a lot of control loops were not being properly utilized. Industry feedback led to further work in this area focused on controller diagnostics. Choudhary et al.39-41 developed a method to detect valve stiction from closed loop operating data using bicoherence, a higher order spectral method. Horch (1999) 42 had used a time series based approach to estimate valve stiction. The problem of multi-loop oscillation also attracted much attention from academia with researchers proposing a range of approaches to diagnose and troubleshoot this issue. Various approaches ranging from data analytics driven to combining first principle causal information to diagnose plant wide oscillations

46-51

have been

investigated to address this problem. CPM is a form of Advanced Analytics since advanced time series and statistical models are automatically estimated on a daily basis to carry out performance monitoring and diagnostics for each control loop, control valve and valve positioner. Changes in these model parameter estimates trigger alerts to the user in case of significant deviations from normal behavior. The use of CPM technology is widespread in the industry13-14. At Saudi Aramco over 15,000 control loops and valves, 500 Smart Positioners, 2000 Analyzers and over 100 APC applications are monitored on a daily basis. All of the base layer controls and smart positioners are monitored using Control Performance Monitoring (CPM) technology that has its roots in work of Shah et al. mentioned above. A companywide benchmarking program ensures that targets set for both APC and PID controller performance metrics such as controller uptime and effective utilization are met. Controller uptime or availability45 refers to the percent


13


Page 14 of 48

of time that a controller, PID or APC, is in service, provided the unit or plant is in running condition. Effective utilization refers to the percent of time a controller is on and its key manipulated variables (MVs) are not saturated, with the plant in running condition. The effective utilization metric is especially more relevant for APC as it ensures that the critical MVs have sufficient room to control and optimize, which is necessary to achieve the design benefits. Weekly alerts are issued at the plant level to plant engineers and technicians for loops that are not meeting their benchmark targets. More recently the technology has been applied to monitor smart positioners at some of the facilities. A smart positioner is essentially a control loop that takes the controller output as its command signal and manipulates the air pressure to achieve the desired valve position. The availability of valve feedback signal allows better insight into the valve performance and related diagnostics. The benchmarking program has resulted in significant improvements to the APC and base layer controller utilization as can be seen from Figure 1. This has resulted in sustainment of the APC benefits which otherwise have a tendency to erode in the absence of adequate support and monitoring. Figure 2 shows a specific example of a sticky valve being automatically detected by the CPM reports that are generated daily. These kinds of issues can often go unnoticed in plants with a large number of loops and increasingly fewer process control resources. The advent of machine learning and AI tools can be expected to enhance to the ability of CPM technologies to diagnose plant wide issues and provide more accurate diagnostics. Similarly these tools have the potential to mine the large amounts of historical data to identify better tuning parameters or even strategies to improve process performance. Next an application of text analytics to analyzing alarm and event data from a Saudi Aramco facility is discussed.


14

Page 15 of 48 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


3.2. Alarm Analytics Operating facilities collect large volumes of process and alarm data every day. Alarm and event data is a kind of text data as it is reported to operators on the DCS in the form of strings. Alarms have to be acknowledged and acted upon to ensure normal operation. Conventional alarm management software focuses on generating descriptive statistics on the generated alarms by area, operator etc. to identify the problematic alarms. Alarm standards from industry bodies such as ISA, EEMUA provide guidance on the acceptable number of alarms per hour per operator52-53. Compliance to industry standards for alarms is important from a safety standpoint as an overloaded operator is not able to respond properly to abnormal situations leading to process upsets or potential shutdowns. Alarm management applications analyze large volumes of alarm data, which is textual data, and present it in the form of descriptive statistics which can provide insights into the alarm performance at a plant54. Advanced Analytics technology provides ways of efficiently extracting valuable information from this data which is rich in volume, variety and velocity. Questions such as optimal alarm settings, correlations between different alarms, combined analysis of process and alarm data can be handled systematically through an analytics based approach. A significant amount of work has been carried out in addressing these questions using Alarm and Event Data55-60. This case study focuses on applying data analytics technology to the alarm and process data from a selected unit. According to industry standards - ISA 18.2 and EEMUA 19139-40 - an operator should not receive more than 6 alarms per hour during normal operation for his area. In most facilities however operators receive a much larger number of alarms. Many of these are nuisance alarms – either redundant or chattering. A small number of process variables or bad actors are often responsible for generating a large number of these alarms. The so-called bad actors are either poorly controlled


15


Page 16 of 48

variables leading to continuous fluctuations and alarms or their alarms themselves are poorly configured in many cases. There are number of alarm management tools on the market that can identify bad actors through analysis of alarm and event (A&E) data. These tools can also provide recommendations to address the bad actors. Though addressing bad actors is a good first step, it is not necessarily enough. To bring the alarm performance within industry accepted standards, one needs to go a little further with steps such as alarm rationalization. This requires significant investment of time from site engineers and operators and is much more time consuming. Full compliances with alarm standards often requires information from different sources such as historical process data, alarm data and detailed engineering and operational knowledge of the process. International Society for Automation (ISA) has defined alarm management standards in ISA 18.239. The key KPIs and target values from this widely accepted standard are listed in Table 1. The activities described here are typically part of the Alarm Rationalization phase and are expected to help with the systematic selection of alarm parameters such as (1) Deadbands, (2) Delay Timers, (3) Filters and (4) Hi/Lo Alarm Limits. The following aspects of Alarm Design and Analysis were considered systematically through a data analytics approach: •

Data based Optimal Alarm Design: Process data is widely available however largely underutilized in alarm design. The process data in combination with alarm information can be effectively utilized to determine optimal settings57-58 for alarm parameters such as (a) alarm limits, (b) Delay timers56, (c) Deadbands and (d) filters55.

•

Alarm Visualization: Alarm data can be more complex to visualize compared to process data. Techniques such a High Density alarm plots and alarm correlation color maps57 can be used to effectively visualize alarm performance and the underlying issues.


16

Page 17 of 48 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


•

Alarm Flood Analytics: Alarms floods present an even more complex data set. The tools used in this study are able to not only visualize alarm flood data but also identify the underlying similarities in a large number of floods in an analytical fashion60-62.

Generally an alarm flood sequence has a very selective number of alarms. A root cause event that is responsible for a flood is often connected with a specific number of process variables. Similar root causes can lead to similar flood patterns. These similarities in the flood patterns can be identified through powerful data mining and alarm analytics techniques62. This case study illustrates the application of these alarm analytics methods and approaches to a Saudi Aramco plant. Historical alarm data was collected and analyzed for this plant where alarm management activities were in progress, to provide insights into the improvement opportunities. Table 2 summarizes the alarm floods statistics using different windows and repeat filters. Figure 3 displays the alarm flood analysis reports for the 20 minute intervals respectively. As can be inferred from the figure, 33 Alarm Floods with more than 20 alarm/10 min over a 40 day period were found. A partial list of the floods ranked by the number of alarms and duration is shown in Table 3. The total number of unique alarms during each alarm flood is shown in the flood information. Each flood represents a sequence of alarms. Advanced analysis techniques61 embedded in the Alarm Designer56-60,61-62 tool allow pattern matching of different alarm sequences. Figure 4 shows the grouped alarm sequences according their similarity to each other. For example floods 25, 23, 24, 27, 29 and 22 show very strong similarity suggesting a common root cause event. Floods 22 and 29 appear nearly identical as per the flood correlation measure which is between 0 and 1. The exact alarm sequences for floods 22 and 27 are shown in figure 5, confirming their strong similarity. A number of vibration alarms are triggered in near identical sequence suggesting either


17


Page 18 of 48

an underlying machine condition or state such as startup/shutdown. The alarm flood analysis tools may be helpful during incident reviews in unearthing common causes. As a next step process data was collected for the vibration tags found in the above alarm floods and analyzed further. The vibration tags along with process variables from the selected unit were analyzed for possible correlation to understand potential root causes from the underlying equipment. As expected, the vibration variables displayed strong correlation in the time range of the alarm floods. The color coded correlation plots are displayed in figure 6. The left hand side of the figure shows the clustered correlation plot for all the process variables including the vibration measurements while the right hand side plot shows a zoomed in version of the correlation plot for vibration measurements alone. However correlation does not imply causation i.e. correlation does not necessary mean a cause and effect relationship. It simply means two variables behave similarly. Advanced data analysis techniques are available that can detect causality from data alone. One such technique known as transfer entropy63 was applied to the vibration measurements. The transfer entropy based causality analysis revealed lack of any causal connections between the different vibration measurements. This suggests that a common event such as startup/shutdown or abnormal behavior of the underlying equipment or a chronic condition could be the root cause of the observed behavior. Other techniques for causality analysis using data include Granger causality mapping64. In next section, an application of machine learning to the area of predictive maintenance is demonstrated.

3.3. Predictive Maintenance The use of analytics in support of maintenance is not new and started along with the early maintenance practices as part of the descriptive analytics used for preventive maintenance67.


18

Page 19 of 48 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Descriptive analytics i.e. statistical analysis is used to calculate the expected time to failures based on previous operating spans in order to schedule maintenance. What is relatively new is the use of predictive and prescriptive analytics to set up and guide the maintenance practices. Nowadays many Oil and Gas companies are employing the predictive maintenance solutions. Pandya et al.68 provides an example of how Shell Global Solutions developed intelligent prognostic systems utilizing machine learning methods to strengthen plants’ predictive maintenance strategy. Predictive Maintenance technologies use machine learning tools to learn asset behavior from historical data, and subsequently use these models to predict machine abnormalities and failures. Saudi Aramco has successfully implemented two predictive maintenance technologies at a number of facilities with different modeling algorithms. Out of many success stories, one was being able to predict performance degradation in some turbines through the machine learning models. The resulting corrective actions originated in significant savings for the company. This technology can be a game changer industry to transition from a schedule driven maintenance approach to a more condition based maintenance program resulting in reduced downtime. The modeled assets, at Saudi Aramco, included gas and steam turbines, compressors, motors, pumps, and gearboxes. Two prediction algorithms were tested and utilized: Similarity-BasedModeling (SBM)65 and Ordering points to identify the clustering structure (OPTiCS)66. Both algorithms are non-parametric algorithms with no assumption about the solution form and are completely data-driven. Moreover, estimates are generated based on carefully selected training data wherein historical patterns are reconstructed through the use of these data based models. Such non-parametric methods do not depend on a specific number of input variables and take into account all the information available in the data. Unlike first principle models, they are designed to work in a variety of scenarios with different operating conditions and levels of instrumentation.


19


Page 20 of 48

In a parametric model, we know the exact model structure that will fit the data. For example, the equation:

yi =β0 +β1 xi +ei,

Non-Parametric Model

represents a known shape i.e. straight line. In contrast, the

y i =f(xi) + ei

where

f(.)

can be any function and treated as a black

box. The model thus developed will not produce the mathematical experssion for f(.), but it will give predict the process response, given historical data. Similarity-based modeling (SBM) is a technique whereby the normal operation of a system is modeled in order to detect faults by analyzing their similarity to the normal system states. In addition, OPTiCS is also as a nonparametric algorithm and designed to be robust under sensor loss or bad data values. It does not require any special relationship in the underlying data, only that the equipment itself has some historical repeatability for normal operation. Figure 7 shows the system early detection of a turbine efficiency degradation. A notification of this degradation alerted the engineers to reassess the maintenance schedule of that asset and three other similar assets which eventually resulted in significant savings through optimization of the maintenance schedule and additional savings through energy consumption. The solution utilizes the process historical data from the targeted assets to produce a data driven model in addition to calculating the efficiency using first principles. Subsequently comparison of the theoretical and predicted efficiencies is used to detect any significant shifts in performance. The early warning notifications of performance degradation resulted in savings on maintenance costs that were greater than 50% of the total costs of fixing these assets if they were maintained as per the original schedule. In other words, rescheduling maintenance saved the company significant costs due to unplanned maintenance of the assets resulting from equipment failure. Predictive maintenance is one of the key advanced analytics applications that are part of the overall digital transformation strategy.


20

Page 21 of 48 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


In the following discussion an application of convolutional neural networks (CNNs) to image analysis is discussed.

3.4. Image Analytics The objective of this application was to use historical video image data from a plant flare camera and predict the following flare characteristics – (1) Flare height, (2) Flare Smokiness and (3) Flare angle. These quantities were chosen to complement the existing corporate flare management system which estimates the flared quantities on daily basis using the valve position and characteristic. In the first phase of this use case the historical flare video data for the three high pressure flares was used to develop models, using deep learning libraries in Python71. In the second phase of the project these models were deployed in the plant using containerization technology (Docker)70 and the ability of the models to generate the predictions with current data was evaluated. The underlying techniques used for modeling are deep learning neural networks (DNNs) which have been found to be highly successful with image analysis and machine vision29. These unstructured data sources are typically unused in most plant environments and AI based tools can provide a way of turning them into useful insights. Convolutional Neural Network (CNN)2, a deep learning algorithm, was used to develop a model using historical flare images using open source tools. The historical data shared was of day time videos of the high pressure flares. Note that as with any modeling exercise, the accuracy of the model developed is strongly dependent on the training set. Thus if the training videos do not contain night time/cloudy/sandstorm images then the developed model cannot be expected to perform with a high degree of accuracy under these conditions. Traditional computer vision algorithms require manually labelled images resulting in significant manual effort. Figure 8 illustrates how CNNs work by essentially applying a set of 2-dimensional


21


Page 22 of 48

filters on the raw image data to extract specific features from an image such as edges, colors etc. CNNs are capable of extracting complex features from the images automatically to build a model. In this application, the flare size, ration of smoke to flare and the angle of the flare are estimated via a CNN model. Historical video data was used to train the CNNs. Unlike traditional techniques, modern analytics tools such as CNNs are capable of fusing image, sensor and audio data. The ability to include different data types in the model was evaluated however the operational model only included the image data. The model can be deployed in real time to enable streaming analytics capability which is essential in a production environment. The video data was ingested in a frame by frame fashion into an Edge Intelligence platform (FoghornTM)72. The CNN models deployed in the edge platform analyzed each video frame and estimated the flare height, smoke percentage and flare angle in real time. The CNN model is able to detect and quantify the height and smokiness of the flare. A flare exhibiting a high degree of smokiness could be indicative of issues with the flare itself or the composition of the flared material. Most image analysis techniques work by drawing a bounding box around the object of interest and then identifying the image characteristic of interest. The flare height and smoke height estimate can be presented in terms of engineering units by taking into account the flare stack height information and the distance of the video camera from the flare. Currently these are presented as relative quantities (%) to size of the bounding box. The ability to fuse audio, video and sensor data was also demonstrated during this exercise in an offline manner. For example if there are audio sensors installed close to a compressor, the CNN algorithm would be able to listen to the sound of the compressor and detect when it doesn’t sound right, similar to what an expert operator or technician may be able to do.


22

Page 23 of 48 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


The last application involves the use a machine learning technique – clustering – for the purposes of deploying advanced process control applications on a large scale.

3.5. Clustering for Large Scale APC Saudi Aramco has a large coverage of APC applications across its refineries, gas plants, NGL fractionation plants, gas oil separation facilities and more recently oilfields. The number of APC applications at Aramco is expected to grow to more than 300 APC applications, with the refineries being the early adopters and the upstream areas being more recent additions. These applications generate benefits in excess of 100 million $ annually through improved yield, reduced energy consumption and improved throughput. Recently the APC rollout at a large oilfield involved APC deployment on more than 300 oil wells. Using conventional APC implementation methodology, the rollout would have taken about 3 man years of engineering effort as each well has to be individually tested and modelled. The model consists of information on relationship between change in independent variables and the resultant change in a dependent variables e.g. model gain representing the effect of a change in a valve on resultant change in flow, pressure etc. Use of advanced analytics techniques proved essential to ensuring the timely deployment of such a large scale APC project. A machine learning algorithm was used to cluster similarly behaving wells, enabling significant (80%) reduction in the engineering effort and operator involvement in developing the model for each well. This allowed the implementation to be completed in one calendar year thus realizing the APC benefits earlier than planned in addition to the engineering effort savings. First a large database (86505 samples x 26 variables) was prepared with static and dynamic data related to multiple wells. The static data for a well consists of the location (longitude and latitude) of the well at the surface and downhole, the pump type, the depth at which the pump is installed,


23


Page 24 of 48

number of stages in the pump, the install date, installation company, well production start date etc. The dynamic data for a well consists of variables measured during the life of the well like production, water cut, pressure, temperature, voltage and current for a year of operation. Applying unsupervised clustering directly to the whole database and analyzing the clusters wasn’t relevant with respect to the purpose of clustering which was to group wells based on similarity in their model gains. It was apparent that the dataset size had to be reduced while making it more meaningful for the purpose. At the feature extraction stage, the dataset size was reduced to, 237 samples x 4 key variables, while preserving the information related to modelling of the well by removing the static information and replacing many measured variables by fewer but more meaningful model gain calculations. Model gains are approximate steady gains of the model estimated from historical data. Using an advanced analytics software73, unsupervised clustering algorithms were applied to the reduced dataset which identified five clusters, with cluster quality in the good range. The cluster quality is a measure of intra cluster cohesion and inter cluster separation73. As many of the wells were clustered in two big clusters accounting for 90% of the wells, it was decided to have sub-groups in the bigger clusters, such that the wells in each sub-group within a cluster are more similar to each other than to other sub-groups in the same cluster. For this, a supervised multi-class classification problem was run with C5.1 algorithm73. The algorithm constructs a decision tree by recursively splitting data into subgroups defined by predictor fields as they relate to target or categorical variable. The five clusters identified earlier were set as the targets or categorical variables and the static data of the wells were set as the input variables. The clusters were categorized with 88% accuracy. The major contributors to the categorization, or predictor fields in the resultant decision tree, were found to be pump type and


24

Page 25 of 48 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


choke valve size. Based on the pump type and choke valve size, the five clusters were manually divided into 42 sub-groups. Within each sub-group representative wells were chosen, based on their availability for testing, and the models of these representative wells, were used for other wells within that sub-group without any significant impact on APC performance, thus saving significant engineering effort in addition to speeding up the deployment time. The key to success in applying machine learning algorithms to any problem is the domain knowledge that goes into data preparation, analysis of the results of machine learning and iterating between them until a satisfactory conclusion is reached. Machine learning should not be considered as a substitute for process knowledge. The best machine learning and AI applications in the process industry will be those where domain (human) expertise is effectively combined with machine learning/AI tools. Though this use case was an application of advanced analytics in an offline mode, there is significant potential of using advanced analytics in an online mode for process control in process industry. Big Data Approximating Control74 has been proposed in the literature for estimation and control by exploiting the use of big data pattern matching and clustering. Process control using deep reinforcement learning technique31 is in initial stages of research. Based on the significant advances being made in the field of machine learning, it is foreseeable that a machine learns the process of one or more units in an industrial plant or even a whole industrial plant by running millions of cases of the industrial plant simulator and learns how to operate and optimize it, better than any group of engineers and operators can. Apart from the applications discussed above, potential analytics applications such as corrosion prediction, remaining useful life prediction, demand forecasting, price optimization, coking prediction are being evaluated currently. As the awareness of the potential value of Advanced


25


Page 26 of 48

Analytics grows, it is expected to be an integral part of every initiative and project. A structured approach is used to identify high value applications, prove their value before proceeding to the full scale deployment, as can be seen from the examples discussed here.

4. Future Directions As advanced analytics powered by the growth in AI at an exponential pace becomes increasingly accessible, the challenge for the process industry is threefold – (1) to continually upskill in order to harness the full power of advanced analytics and AI; (2) manage a rapidly evolving, and complex technology in order to harness the maximum value; and (3) identify strategic areas where AI will reinvent roles and work processes in the future. Here we discuss some potential areas where advanced analytics could reshape the way these tasks are performed today.

4.1. The Plant of the Future The process industry is already using predictive models to forecast, control and optimize the operating plants and in some cases the supply chain as well. However as AI starts to encroach upon traditionally human areas such as complex audio and visual pattern recognition, forecasting and diagnostics of potential failures, the questions become – (a) what will the plant or factory of the future look like and (b) what roles will humans play relative to their machine or digital counterparts? Today a human operator mans the control rooms and analyzes a very large amount of complex audio/visual information to make decisions in real time that impact the safety, reliability and efficiency of assets in the plant. With this operational real time data, machine learning algorithms can go beyond economic optimization of steady state systems to account for dynamic disturbances and random faults76. Could a machine be trained to do the human operator's job in the future and drive towards an autonomous self-driving future?


26

Page 27 of 48 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


4.2. The Role of Intelligent Machines and Humans Today a plant communicates with the human operator via a host of different signals in the form of both structured and unstructured data in the form of process measurements, alarms, video feeds, human inputs all related to each other in a complex way. Could machine intelligence enabled by cognitive computing process this highly complex information and enable a plant or factory to talk to a human operator like a virtual assistant does and perhaps even provide prescriptive directions to address the underlying issues? Any industrial facility comprises of a large of collection of machines such as compressors, pumps, turbines and process equipment such as distillation columns, reactors, heat exchangers etc. It is possible that the advances in machine intelligence can be extended to these machines, turning them into self-diagnosing, predictive assets that can even prescribe corrective actions in the future. Today significant time delays are involved in collecting information on these assets and coming up with intelligent diagnostics that can be used to avoid potentially expensive and unsafe failures in the industry. The area of machine learning has made some astounding strides in recent times as evidenced in the ease with which AlphaGo was able to beat the best players in the world. As machine intelligence continues to evolve at a rapid pace, the question becomes how will it redefine and interact with human intelligence? Will AI replace human intelligence or is it a co-evolution with room for both? What are the skills that humans in the future need to develop in order to be considered intelligent relative to their machine counterparts? In the context of the Oil and Gas industry these are very important questions, as an industry heavily reliant on significant human experience and intelligence.


27


Page 28 of 48

4.3. Hybrid Modeling First principles models are at the heart of any processing facility. A hybrid approach would involve using the first principles models in combination with data analytics based approaches to fill in the gaps where the first principles model proves inadequate77-78. This could be a more relevant approach from an industry perspective as it would make the models more explainable. Using pure data based models is challenging for industry practitioners as they do not lend themselves to explaining causal relationships. This could mean that process simulation and even APC software in the future would be complemented by data analytics and thus potentially improve the efficiencies of the model building and maintenance process.

4.4. Edge Intelligence Many of the initial advanced analytics applications were developed and deployed in the cloud. However for an industry that places a strong emphasis on security, use of cloud computing is not always the most desirable option. Instead deploying analytics applications at the edge, close to the equipment or device itself is much more desirable in terms of speed, security and bandwidth. It will be interesting to see whether this growing trend results in ultimately original equipment manufacturers (OEMs) themselves offering embedded AI either as a service or application. For example it is foreseeable that every compressor gets shipped with onboard AI equipped to diagnose it using an array of smart sensors and other acoustic/video data sources. And similar to a Tesla, could these apps be upgraded with the latest AI based algorithms on the fly? One of the recent trends in industry is the use of non-intrusive measurement devices to collect and analyze data using unmanned aerial vehicles (UAVs) and robots. Such techniques have proven to be cost effective for capturing data from hard to reach confined spaces, elevated structures and unsafe areas. For example, flare stack inspection data can be captured in the matter of minutes at


28

Page 29 of 48 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


a significant cost advantage and at a higher quality compared to conventional approaches which involves significant risk and cost. Crawler robots equipped with advanced sensors and cameras can access elevated structures and pass through confined spaces such inside narrow pipes. A significant portion of this data is currently sent to cloud based applications for processing and analytics hosted by industrial internet of things (IIoT) with state of art data management and analysis capabilities. Some of the plant data is processed internally in the plant through edge analytics solutions, which combine AI and analytics that are run locally closed to the asset or device. The edge analytics approach can be used to limit data volume sent over the network which can be a constraint for streaming video data, to provide quick response, for closed loop operation.


29


FIGURES

PID Controller Effective Utilization (%)

100 90 80 70 60 50 40 30 20 10 0 2014

2015

2016 Plan

2017

2018

2017

2018

Actual

(a) 100 90

APC Effective Utilization (%)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 30 of 48

80 70 60 50 40 30 20 10 0 2014

2015

2016 Plan

Actual

(b) Figure 1. (a) Base layer (PID) and (b) APC Effective Utilization data from 2014-18


30

Page 31 of 48 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Figure 2. Detection of Stiction Signature from operating data for a PID loop


31


Page 32 of 48

Figure 3. Alarm Flood Sequences greater than 20 Alarms/10 minute window


32

Page 33 of 48 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Figure 4. Clustering of Alarm Sequences based on similarity

Figure 5. Example of two similar alarm sequences detected by the clustering algorithm


33


Page 34 of 48

Figure 6. Correlation analysis of process data for the clustered alarm sequences

Figure 7. Actual Vs. Predicted Turbine Isentropic Efficiency (Red dots denotes significant differences)

Figure 8. Illustration of Convolutional Neural Networks


34

Page 35 of 48 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


TABLES. Table 1. ISA Standards for Alarm Metrics Metric

Target Value

Acceptable Alarms (per hour per operator)

< 6 ( 144 per day)

Maximum manageable alarms (per hour per operator) < 12 ( 288 per day) Contribution of top 10 most frequent alarms

1% to 5%

Quantity of chattering and fleeting alarms

Zero

Priority Distribution % (Low/Med/High)

80/15/5


35


Page 36 of 48

Table 2. ISA Standards for Alarm Flood Metrics Metric description

Alarm Floods

Short Term Long Term Target Target

Between 10-20 alarms/10 min

7 per day

< 5 per day

< 3 per day

More than 20 alarms/10 min

< 1 per day

< 3 per day

0 per day

More than 20 alarms/10 min with 30s < 1 per day repeat filter

< 3 per day

0 per day

Table 3. Alarm Flood statistics Flood Start Time ID

End time

Duration

Total Unique Alarms Alarms

230

6/25/2014 22:30

6/26/2014 5:58

7h-28m-5s

649

53

224

6/25/2014 4:09

6/25/2014 10:45

6h-35m-48s

577

127

346

7/4/2014 8:10

7/4/2014 13:13

5h-3m-45s

429

38

276

6/29/2014 10:28

6/29/2014 15:30

5h-1m-38s

332

47

236

6/26/2014 16:48

6/26/2014 21:37

4h-48m-56s

371

27

464

7/11/2014 10:59

7/11/2014 15:46

4h-46m-56s

309

52

255

6/27/2014 23:03

6/28/2014 3:44

4h-40m-26s

293

35

301

7/1/2014 4:58

7/1/2014 9:02

4h-3m-40s

338

51

473

7/12/2014 6:49

7/12/2014 10:52

4h-3m-27s

266

40

225

6/25/2014 11:25

6/25/2014 16:05

4h-39m-55s

342

60


36

Page 37 of 48 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


AUTHOR INFORMATION Corresponding Author *Rohit S. Patwardhan, [email protected] Author Contributions The manuscript was written through contributions of all authors. All authors have given approval to the final version of the manuscript. ACKNOWLEDGMENT The authors wish to acknowledge the support of Process and Control Systems Department, Saudi Aramco in supporting the work described in this manuscript. The authors also wish to thank the members of the Advanced Process Control team who were responsible for the process control performance benchmarking and APC deployment programs at Saudi Aramco. DISCLAIMER This article is not intended to highlight or support any particular technology or vendor. Its purpose is solely to highlight some of the advanced analytics applications at Saudi Aramco and the resulting learnings. ABBREVIATIONS AI, Artificial Intelligence; ANN, Artificial Neural Networks; APC, Advanced Process Control; CNN, Convolutional Neural Network; CPM, Control Performance Monitoring; DCS, Distributed Control Systems; KPI, Key Performance Indicator; ML, Machine Learning; MPC, Model Predictive Control; OLE, Object Linking and Embedding; OPC, OLE for Process Control; PID,


37


Page 38 of 48

Proportional Integral Derivative; RNN, Recurrent Neural Network; SCADA, Supervisory Control and Data Acquisition systems; UAV, Unmanned Aerial Vehicles.


38

Page 39 of 48 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


REFERENCES (1) Weil, P.; Woerner, S. L. Driving in an increasingly Digital ecosystem. MIT Sloan Manag. Review 2015, 56, 27-34. (2) Lee, J.; Bagheri, B.; Kao, H.A. A cyber-physical systems architecture for industry 4.0based manufacturing systems. Manuf. Lett. 2015, 3, 18–23. (3) Kagermann, H.; Helbig, J.; Hellinger, A.; Wahlster, W. Recommendations for Implementing the Strategic Initiative INDUSTRIE 4.0: Securing the Future of German Manufacturing Industry. Final Report of the Industrie 4.0 Working Group, Acatech: München, Germany, 2013. (4) Cutler, C.R.; Ramaker, B.L. Dynamic matrix control - a computer control algorithm. In Proceedings of the joint automatic control conference, 1980, 1, 5-12. (5) Feldmann R.; Hammer M.; Somers K.; Niel J. V. Buried treasure: Advanced analytics in process industries, McKinsey Report, 2017. https://www.mckinsey.com/businessfunctions/operations/our-insights/buried-treasure-advanced-analytics-in-processindustries. (6) LeCun, Y. S. L.; Bengio, Y.; Hinton, G., Deep Learning, Nature, 2018, 521, 436-444. (7) Kresta J. V.; MacGregor, J. F.; Marlin, T. F. Multivariate statistical monitoring of process operating performance, Can. J. Chem. Eng., 1991, 69, 35-47. (8) Kourti T.; MacGregor, J. F. Process Analysis, monitoring and diagnosis using multivariate projection methods, Chemom. Intell. Lab. Syst., 1995, 28, 3-21. (9) Chiang L.; Lu B.; Castillo, I. Big Data analytics in chemical engineering, Annu. Rev. Chem. Biomol. Eng., 2017, 8, 63-85.


39


(10)

Page 40 of 48

Qin, S. J. Process data analytics in the era of big data. AIChEJ, 2014, 60, 3092–

3100. (11)

Chiang, L.H.; Russel, E.L.; Braatz, R.D. Fault Detection and Diagnosis in

Industrial Systems, Springer-Verlag, London, 2012. (12)

Imtiaz S. A.; Shah, S. L.; Patwardhan R.; Palizban, H.; Ruppenstein, J. Prediction

Diagnosis and Root Cause Analysis of Sheet-break in a Pulp and Paper Mill with Economic Impact Analysis, Canadian Journal of Chemical Engineering, 2007, 85, 512525. (13)

Bauer, M.; Horch, A.; Xie, L.; Jelali M.; Thornhill, N. The current state of control

loop performance monitoring—a survey of application in industry. J. Process Control, 2016, 38, 1–10. (14)

Starr, K. D.; Petersen, H.; Bauer, M. Control loop performance monitoring—

ABB’s experience over two decades. IFAC-PapersOnLine, 2016, 49, 526–32 (15)

Davenport, T. H. Competing on Analytics, Harvard Business Review, 2005.

(16)

Gartner. Gartner’s IT glossary, 2017. Available online.

https://www.gartner.com/it-glossary/ (17)

Laney, D. 2001, Data management: controlling data volume, velocity, and variety.

METADelta, 2001. http://blogs.gartner.com/doug-laney/files/2012/01/ad949-3D-DataManagement-Controlling-Data-Volume-Velocity-and-Variety.pdf (18)

Industrie 4.0 Communication Guideline Based on OPC UA • VDMA • Fraunhofer

IOSB-INA • 2017. https://industrie40.vdma.org/documents/4214230/20743172/Leitfaden_OPC_UA_Englisc h_1506415735965.pdf/a2181ec7-a325-44c0-99d2-7332480de281


40

Page 41 of 48 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


(19)

OPC Foundation, OPC Unified Architecture Interoperability for Industrie 4.0 and

the Internet of Things, 2017. (20)

Olson, D. L.; Delen, D.; Advanced Data Mining Techniques, Springer, London,

2008 (21)

Qin, S.J.; Badgwell, T.A.; A survey of industrial model predictive control

technology. Control Engineering Practice, 2003, 11, 733-764. (22)

Heuvel, P. V. D.; Jeavons, D. Shell's journey to Advanced Analytics, OSI Soft

User Conference, London, 2018. https://www.osisoft.com/Presentations/Shell-s-journeyto-Advanced-Analytics/ (23)

Listen up: identifying the sounds underground, BP report, 2018.

https://www.bp.com/en/global/corporate/bp-magazine/innovations/sound-undergroundsand-management-technology.html, (24)

Hetz, S.; Connor, M. H. O.R. and advanced analytics at Chevron, INFORMS

Analytics Magazine, 2018. http://analytics-magazine.org/corporate-profile-o-r-andadvanced-analytics-at-chevron/ (25)

Chiang L.; Lu B.; Castillo I. Advances in Big Data Analytics at The Dow

Chemical Company, 6th International Symposium on Advanced Control of Industrial Processes (AdCONIP), 2017. (26)

Goodfellow, I; and Bengio, Y.; Courville, A. Deep Learning, 2016, MIT Press,

London. (27)

LeCun, Y. et al. Handwritten digit recognition with a back-propagation network.

In Proc. Advances in Neural Information Processing Systems, 1990, 396–404.


41


(28)

Page 42 of 48

Hochreiter, S.; Schmidhuber, J. Long short-term memory. Neural Comput., 1997,

9, 1735–1780. (29)

Krizhevsky, A.; Sutskever, I.; Hinton, G. ImageNet classification with deep

convolutional neural networks. In Proc. Advances in Neural Information Processing Systems, 2012, 25 1090–1098. (30)

Hinton, G. et al. Deep neural networks for acoustic modeling in speech

recognition. IEEE Signal Processing Magazine, 2012, 29, 82–97. (31)

Spielberg, S.P.; Gopaluni, R.B.; Loewen, P.D. Deep Reinforcement Learning

Approaches for Process Control, In Proceedings of Advanced Control of Industrial Processes (ADCONIP), 2017, 201-206. (32)

Harris, T.J. Assessment of control loop performance, Can. J.Chem. Eng., 1989, 67

856–861. (33)

Huang, B.; Shah S.L.; Kwok, E. Z. Good, Bad or Optimal? Performance

Assessment of Multivariable Process. Automatica, 1997, 33, 1175-1183. (34)

Huang, B.; Shah, S. L. Performance Assessment of Control Loops, Springer-

Verlag, London, 1999. (35)

Patwardhan, R. S.; Shah, S.L; Qi, K. Assessing the Performance of Model

Predictive Controllers”, Can. J. Chem. Eng., 2002, 80, 954-966. (36)

Patwardhan, R. S.; Shah, S.L. Issues in performance diagnostics of model-based

controllers, Journal of Process Control, 2002 12, 413-427. (37)

Badwe A.S.; Patwardhan R.S.; Shah, S.L.; Patwardhan, S.C.; Gudi, R.D.

Quantifying the impact of model-plant mismatch on controller performance, Journal of Process Control, 2010, 20, 408-425.


42

Page 43 of 48 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


(38)

Badwe A.S.; Gudi; R.D.; Patwardhan, R.S.; Shah S.L.; Patwardhan, S.C.

Detection of model-plant mismatch in MPC applications, Journal of Process Control, 2009, 19, 1305-1313. (39)

Choudhury, M.A.A.S.; Shah, S. L.; Thornhill, N. F., Diagnosis of Poor Control

Loop Performance using Higher Order Statistics, Automatica, 2004, 40, 1719-1728. (40)

Choudhury, M. A. A. S.; Thornhill, N. F.; Shah, S. L. Modeling valve stiction,

Control Engineering Practice, 2005, 13, 641–658. (41)

Choudhury, M. A. A. S.; Shah, S. L.; Thornhill, N. F. Diagnosis of Process

Nonlinearities and Valve Stiction Data Driven Approaches, Springer, Berlin, 2008. (42)

Horch, A. A simple method for detection of stiction in control valves, Control

Engineering Practice, 1999, 7 1221–1231. (43)

Miller, R. M.; Timmons, C. F.; Desborough, L. D.; CITGO’s experience with

controller performance monitoring. In Proceedings of the NPRA computer conference, San Antonio, USA, 1998. (44)

Badmus, O.; Banks, D.; Vishnubhotla, A.; Huang, B.; Shah, S. L.; Performance

assessment: A requisite for maintaining your APC assets. In Proceedings of the IEEE workshop dynamic modeling and control applications for industry, 1998, 54–58. (45)

Forbes, M. G.; Patwardhan, R. S.; Hamadah, H.; Gopaluni, R. B.; Model

Predictive Control in Industry: Challenges and Opportunities, IFAC-Papers Online, 2015, 48(8), 531-538. (46)

Thornhill, N. F.; Cox, J. W.; Paulonis, M. A. Diagnosis of plant-wide oscillation

through data-driven analysis and process understanding, Control Engineering Practice, 2003, 11, 1481–1490.


43


(47)

Page 44 of 48

Chiang, L.H.; Braatz, R.D. Process monitoring using causal map and multivariate

statistics: fault detection and identification. Chemom. Intell. Lab.Syst., 2003, 65, 159– 178. (48)

Bauer, M.; Cox, J. W.; Caveness, M. H.; Downs, J. J.; Thornhill, N. F. Nearest

neighbors methods for root cause analysis of plantwide disturbances, Industrial and Engineering Chemistry Research, 2007, 46, 5977-5984. (49)

Jiang, H.; Patwardhan, R. S.; Shah, S.L. Root case diagnosis of plant-wide

oscillations using the concept of the adjacency matrix, Journal of Process Control, 2009, 19, 1347-1354. (50)

Thambirajah, J.; Benabbas, L.; Bauer, M.; Thornhill, N. F. Cause-and-effect

analysis in chemical processes utilizing XML, plant connectivity and quantitative process history. Comput. Chem. Eng., 2009, 33, 503–512. (51)

Yuan, T.; Qin, S. J. Root Cause Diagnosis of Plant-Wide Oscillations Using

Granger Causality, In International Symposium on Advanced Control of Chemical Processes. 8th ed., IFAC, Singapore, 2012, 160–165. (52)

ANSI/ISA-18.2-2009 Management of Alarm Systems f or the Process Industries,

International Society of Automation, 2009. (53)

EEMUA 191 Alarm Systems - A Guide to Design, Management and

Procurement, 1999. (54)

Hollifield, B.; Habibi, E. Alarm Management: A Comprehensive Guide.

ISA,Research Traingle Park, NC, 2011.


44

Page 45 of 48 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


(55)

Cheng Y.; Izadi, I.; Chen, T. Optimal alarm signal processing: filter design and

performance analysis, IEEE Transactions on Automation Science and Engineering, 2013, 10, 446-451. (56)

Adnan, N.A.; Cheng, Y.; Izadi, I. ; Chen, T. Study of generalized delay-timers in

alarm configuration, Journal of Process Control, 2013, 23, 382-395. (57)

Kondaveeti, S.R.; Izadi, I.; Shah; S.L., Black, T.; Chen, T. Graphical tools for

routine assessment of industrial alarm systems. Comput. Chem. Eng., 2012, 46, 39–47. (58)

Hu, W.; Shah, S. L.; Chen T. Framework for a smart data analytics platform

towards process monitoring and alarm management, Comput. and Chem. Eng., 2018,114, 225-244. (59)

Kondaveeti, S. R.; Izadi I.; Shah, S.L.; Shook, D.S.; Kadali, R.; Chen, T.

Quantification of alarm chatter based on run length distributions, Chemical Engineering Research and Design, 2013, 91, 2550-2558. (60)

Ahmed, K.; Izadi, I.; Chen, T.; Joe, D. Similarity analysis of industrial alarm

flood data, IEEE Transactions on Automation Science and Engineering, 2013, 10, 52457. (61)

Alarm Designer Toolbox User Guide, 2014, Advances in Alarm Management and

Design Research Group, University of Alberta, Edmonton, CA. (62)

Cheng Y.; Izadi, I.; Chen, T. Pattern matching of alarm flood sequences by a

modified Smith-Waterman algorithm, Chemical Engineering Research and Design, 2013, 91, 1085-1094.


45


(63)

Page 46 of 48

Duan, P.; Yang, F.; Chen, T.; Shah, S. L. Direct causality detection via the

transfer entropy approach, IEEE Transactions on Control Systems Technology, 2013, 21, 2052-2066. (64)

Granger, C.W. Investigating causal relations by econometric models and cross-

spectral methods. Econometrica: J. Econometric Soc., 1969, 424–438. (65)

Wegerich, Stephan. Similarity Based Modeling: A Nonparametric Approach to

Condition Monitoring. SUMMIT 06 by SmartSignal. September 19-20, 2006. (66)

Thomas, Justin. Introduction to PRiSM for Predictive Analytics. Schneider-

Electric Conference. Chicago, IL Oct 19-21, 2015. (67)

NASA. 2000. Reliability Centered Maintenance Guide for Facilities and

Collateral Equipment. National Aeronautics and Space Administration, Washington, D.C. (68)

Pandya, D. et al. Increasing Production Efficiency via Compressor Failure

Predictive Analytics Using Machine Learning. Offshore Technology Conference, Houston, TX, USA. April 30 – March 3, 2018. (69)

Ai, Y.; Peng, M.; Zhang, K.; Edge computing technologies for Internet of Things:

a primer, Digital Communications and Networks, 2018, 4, 77–86 (70)

Rad, B. B.; Bhatti, H. J.; Ahmadi, M.; An Introduction to Docker and Analysis of

its Performance, International Journal of Computer Science and Network Security, 2017, 17(3), 228. (71)

Erickson, B. J.; Korfiatis, P.; Akkus, Z.; Kline, T.; Philbrick, K.; Toolkits and

Libraries for Deep Learning, J. Digital Imaging, 2017, 30(4), 400–405. (72)

Foghorn Edge ML user’s guide, 2018, Foghorn Systems Inc.

(73)

IBM SPSS Modeller user’s guide, 2014, IBM Company.


46

Page 47 of 48 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


(74)

Stanley G. M. Big Data Approximating Control (BDAC)-A new model-free

estimation and control paradigm based on pattern matching and approximation. Journal of Process Control, 2018, 67, 141-157. (75)

Saudi Aramco Digital Transformation Strategy and Roadmap, Internal Report,

Saudi Aramco, 2018. (76)

Olivier, L.E.; Craiga, I.K. Should I shut down my processing plant? An analysis

in the presence of faults, Journal of Process Control, 2017, 56, 35–47 (77)

von Stosch, M; Oliveira, R; Peres, J; Feyo de Azevedo, S. Hybrid semi-

parametric modeling in process systems engineering: Past, present and future, Computers and Chemical Engineering, 2014, 60, 86-101 (78)

Zendehboudi, S.; Rezaei, N.; Lohi, A. Applications of hybrid models in chemical,

petroleum, and energy systems: A systematic review, 2018, 228, 2539-2566 (79)

Reis, M. S.; Gins, G. Industrial Process Monitoring in the Big Data/Industry 4.0

Era: From Detection, to Diagnosis to Prognosis, 2017, Processes, 35(5), 1-16. (80)

Ge, Z.; Song, Z.; Gao, F. Review of recent research on data-based process

monitoring. Ind. Eng. Chem. Res., 2013, 52, 3543–3562.


47


Page 48 of 48

For Table of Contents Only


48

Applications of Advanced Analytics at Saudi Aramco: A Practitioners

Recommend Documents