The Future of Production Data Management from Meter to Auditor

The Future of Production Data Management From Meter to Auditor

Executive Summary Implementation of business controls to manage the path of production data from measurement devices through to validated production accounting numbers is an essential requirement in today’s regulatory controlled world. Basic data characteristics like the source meter, accuracy, precision, timing, calibration conditions, stream conditions and units of measure are fundamental to effective use of the values, but retention, auditability, versioning, change management, security, traceability and recovery are all becoming equally important to make information defendable to third party auditors.

Effective Production Data Management is also a key factor in meeting operational excellence business imperatives. It can provide rapid access to validated information allowing confident timely decision making; it can provide the traceability necessary to meet regulatory requirements; it can capture best practices and ensure a reproducible response and follow - up to operational events.

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Table of Contents

Meters Are Always Wrong ...... 4

Do We Really Need to Worry? ...... 5

Instrumentation Business Controls ...... 6

Production Data Management Model ...... 9

Future of Production Data Management ...... 13

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Table of Figures

Figure 1: Meter Visualization ...... 4

Figure 2: Accuracy and Precision Plots ...... 5

Figure 3: Fuel Combustion Sampling and Analysis Frequency ...... 6

Figure 4: Operation Facilities Structure...... 8

Figure 5: Version Controls Chart ...... 12

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Introduction

Before trying to figure out the future it is usually a good idea to figure out the present. How well are we managing production data these days and is it important anyway? Addressing importance first; here are some things to think about:

 You will never figure out how well you are doing if you don’t have good measurements  Not knowing how well you are doing means you will never know if you are doing better, or worse and can therefore not improve  Any decisions you have made based on uncertain numbers are at best uncertain  Your CEO can be sent to jail for misrepresenting data to shareholders

Essentially good business sense says you need good information to make good decisions. Up to a point you can still make good decisions with good people and dubious data. Beyond that point however regulations begin to catch up with you and demand certain minimum standards or else. There is also an upside to consider; if you could get access to really good data very quickly you could steer a much more optimum course taking advantage of opportunities and/or mitigating potential disasters.

Meters Are Always Wrong The starting point for a lot of production data are meters located out in the plant measuring flows, temperatures, pressures etc. These meters have differing accuracies and precisions which means they are all incorrect to some greater or lesser extent.

This is a fact of measurement that has to be managed. If a meter says it is accurate to +/- 2% that means the correct value is likely to be within 2% of the measurement but there is no way of knowing in which direction. So what it also means is that if you h ave a meter with a full scale of 100 and 2% accuracy relative to full scale, and a measurement of 50 then any number between 48 and 52 is quite acceptable.

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An accurate meter by definition should scatter its values around the correct value so , statistics would say if you took several measurements you would end up with values such as 47, 51, etc.

Repeatability or precision of a meter dictates the clustering of data points. A high precision meter with low accuracy will provide a lot of similar wrong numbers.

The process of calibration of a flow meter attempts to account for systematic bias in measurements but cannot do better than the inherent meter accuracies dictated by the physics of flow and geometry.

This is however not the end of the story. There are a lot of other reasons a measurement may be wrong. These will be related to:

 Design and Installation: Meter is in the wrong service or is installed incorrectly  Calibration: Measurement values drift due to system atic biases introduced from the fluid properties or flowing conditions.  Standards and Corrections: Does the data value have the correct units of measure, is it a compensated value, an uncompensated value etc.  Aggregation: Is the data value an instantaneous value or is it already an average. If so what is the period of the average?  Timing: Measurements need to be appropriately identified in time. These kinds of errors generally result from reporting problems, but also can result from incorrect handling of time zones or daylight savings.  Location: Is the measurement being measured in the location where it is expected to be measured.  Completeness: Missing measurements or transactions are a significant cause of information quality degradation.  Currency: Is the value current or it is out-of-date?  Operating mode: Measurement may be correct but with the process operating in an alternate mode it needs to be interpreted differently.  Error: Pure human error in data entry or interpretation.

Pure meter accuracy is determined by physics, the above list however is determined by operational procedures. The impact of problems generated by this list can be significantly higher than meter accuracies, so they need to be addressed in a systematic manner. What this boils down to is a set of good instrumentation business controls. Perhaps before we address the controls there is one more question.

Do We Really Need to Worry? The answer is you need to worry about some of the measurements but not so much about some others. Unless you understand your business and the dependencies between measurements then you may need to worry about which things to worry about and that may be quite worrisome.

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A starting point for figuring out where to concentrate effort is to look at the Regulatory environment. This will tell you what you have to do as a minimum. The driving forces in regulatory come from several directions, e.g.

 Financial e.g. Sarbanes Oxley  Safety e.g. ABSA, OSHA, ASME, ANSI  Quality e.g. FDA, ISO, EPA  Environmental e.g. EPA, AENV, Environment Canada  Federal, State/Provincial and Local agencies.

The requirements of the regulations can vary from high level directives to specific instructions. For example Sarbanes Oxley mandates that senior executives take personal responsibility and that a company have a framework in place such that assertions can be made on the:

 Completeness  Accuracy  Traceable Authority  Security of the information in the corporate reports. Recent EPA regulations directed at Greenhouse Gas mandate:

 Instrumentation be installed according to industry and manufacturers standards  Calibration frequencies  Calibration methods  Documentation requirements  Job titles and responsibilities for those involved in collection of data  Frequencies of analyses  Procedures for replacement of missing data  Accuracy estimates – Some local agencies in California specify specific instrumentation accuracy requirements  “… provisions to ensure the accuracy of emissions data through monitoring, recordkeeping and verification requirements…”  Requirements for when adjustments and corrections need to be made.  Requirements for traceability and verification

With these regulations and considering carbon dioxide emissions penalties currently in place, every meter involved in Greenhouse Gas calculations has effectively become a custody transfer meter. You are paying real money based on its measurements.

The message is clear: control of instrumentation and the processes of delivery of information are under ever expanding scrutiny and need to be well controlled. Secondly, the validation processes themselves need to be able to be easily verified by internal and potentially third party auditors.

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Instrumentation Business Controls Instrumentation business controls need to start at the meter with some fundamental questions. For example:

Design and Installation  Is this meter the correct meter for the service?  Is the meter installed correctly?  Is it ranged appropriately for the service?  Is it effective for all modes of plant operation?  Does it meet regulated accuracy requirements?

The best way to get a handle on this is to use a multi-disciplinary audit team containing operations, engineering, environmental and accounting to:  Review the metering with respect to regulatory requirements and identify gaps  Ensure documentation is in place that reflects the current install  Ensure change management processes are in place to keep the documentation up to date

Calibration Calibration as a process attempts to remove systematic bias from instrumentation measurements by periodically validating agai nst a set of known conditions. The calibration process itself can introduce error so it must be executed with appropriately trained staff. Recordkeeping is important to capture the results of the calibration but also to document that appropriate methodologies were followed. Consistent data capture of calibration information enables statistics to be generated for fine tuning of calibra tion maintenance activities. At times problems may be found with standards used for calibrations that require calibrations to be reworked. Again effective recordkeeping is key.

Standards and Corrections At this point we have a value coming from the measurement system with a relatively predictable uncertainty. The value then dives in to a plant DCS and onward to a plant historian for archiving. At each point along this route there is opportunity for error. Meter factors need to be verified from the transmitter to the DCS and through to the Historian. Units of measure need to be verified across each interface. This does not just have to be done once, it has to be maintained. Changes in configuration in the DCS need to be p ropagated through to the Historian and beyond. The “and beyond” can be quite challenging when values are scattered into spreadsheets, calculations and reports.

This is where configuration management and meta-data control become important. Every value coming from a measurement system has some characteristics that define its content and application. The aspects of this characterization are:

 physical property being measured e.g. liquid volume flow  method of measurement e.g. orifice meter  conditions of the measurement e.g. corrected to 15 deg C  type of data point e.g. instantaneous value, average, maximum, total  averaging duration of the measurement e.g. daily average  units of Measure e.g. meter3/hour

If any of these characteristics change then the downstream usage of that value has to be re-evaluated. The significance of errors here can be considerable. NASA lost a $125 million dollar Polar Orbiter crashing it in to the surface of Mars after it had flown 415 million miles taking nine months due to a mismatch in units of measure.

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In a production facility typically inconsistencies arise from interpretation of the reference conditions of a measurement. Has the valu e been corrected to standard conditions; has it been adjusted for differences between actual conditions and calibration conditi ons; are the units of measure correct?

Aggregation If a value is already an aggregation then its use in subsequent aggregations needs to make allowances for that. If you have a daily average then adding up the total for the day does not require integration under the curve; it only requires that one value for the day and the length of a day. Depending on how the tag is calculated, be careful it may not always be 24 hours. Daylight savings causes 25 hour days, 23 hour days and October to be 31 days and 1 hour long.

Timing Misrepresentation of timing can occur because of systems where the clock is not adequately synchronized, where there is a mismatch in time zone or perhaps where a system has switched or not to daylight savings. More often, timing shows up with manual recording of times during data entry or sampling. It is almost always better to have the correct recorded times. So the midnight sample may not be quite at midnight.

Location and Context This comes under the category of, do you know what you are talking about? Is this temperature upstream or downstream of the final exchanger? Is the flow measure before or after the recycle stream? Where is FIC- 1234 anyway? In order for information to be used effectively its’ context has to be understood. We provid e context by referencing things to a common framework. Knowing that FIC-1234 is the charge rate to the unit adds value to the numbers it produces. Without the context it is just a number.

On a production facility context is provided by referencing the physical model of the plant. This means converting tag references to object attribute references. For example, FIC-1234 is referenced by Unit-21 Diluent Feed. This is usually easier for most users to understand and also has a positive effect on the quality of the information. If a new unit is built and FIC-1234 now registers flow to two units any references to FIC-1234 may be used incorrectly. Unit 21 Diluent Feed however could be re-pointed to a calculation FIC-1234 minus FI-2234 to allow all references to remain consistent.

Converting to an object view of information implicitly provides relationships between values. The Unit-21 Diluent Feed Flow Rate and the Unit-21 Diluent Feed Temperature are now connected since they are related to the same object.

In dealing with different systems, information will not be provided at a uniform level in the organizational hierarchy. Planning systems will likely deliver targets for the overall plant, scheduling systems will be dealing with data at the unit and tank level and the measurements will be provided at the equipment level. Part of the Production Management Model must be a picture of the organization of the business to allow for aggregations to compare Plan versus Scheduled versus Actual. The same applies to the materials hierarchy. Planning will likely provide targets for aggregated materials, e.g. total crude, rather than the individual feedstocks.

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Completeness There are several aspects to completeness; here I want to cover what to do with missing measurements. If an analyzer goes down or a sample fails to be appear what should the value be? A good process engineer will have an answer.

 Use yesterdays value  Estimate it from other analyses  Set it the same as another reading  Use reference properties for the transport fluid  etc.

If you are dealing with environmental emissions data the regulations can be very specific as to what can be used to fill in values and what records have to be kept to document the event and the steps taken to prevent it happening again.

There is considerable advantage to having this combination of business rules, process experience and best practices accessible to all users of data. It means that every data consumer can take advantage of the best available expertise. It also means that responses to missing data conditions are handled consistently and reproducibly between users. And in the case of regulatory data it means that regulatory procedures are being followed in an auditable and traceable manner.

Currency All data need to have some form of associated currency. A fuel gas analysis could be a few days old and still be reasonably valid. A production rate value a few days old is useless. Data values have an expiry date that defines how long you could potentially believe the value if you didn’t receive another measurement. Users of the data can use currency information to determine their confidence in the values and when to switch over to alternate sources if the information is stale. Some data can have event driven currenc y. If a movement starts in to a tank, the analysis values of the tank contents now become suspect.

Operating Mode Operating modes may change the interpretation of a measurement value. For example, a product meter may be reading a flow rate but actually all product is being recycled during startup. Feed streams may be used for completely different services during start-up or upset conditions. Appropriate use of the information needs to include an understanding of the process conditions around it.

Human Error Manual data entry causes a suite of issues that can be more random in nature. Values can be entered wrong; transactions can be missed; dates can be incorrect; values can be attributed to the wrong place and more.

Production Data Management Model All of the context described above can be consolidated in to a Production Data Management Model to provide the meta -data and business rules surrounding any value. Adding the capability to understand the data access paths provides for immediate access to usable information.

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For example a request for the total feed of Diluent to Unit-21 in a day, should be able to determine that the source for the data is the plant historian tag 3456, and that the tag is an instantaneous rate tag measured in meter3/hr, so the value needs to be averaged over the day and the units of measure converted to barrels/day. It will also know that the historian is accessible through an OPC driver and send an appropriate request to the driver to get the value. The model should also know that the flow rate is an uncorrected Gross flow, so if you are asking for Net Standard volume, the value needs to be corrected for temperature and the water removed by accessing the associated BS&W reading.

But so far we may know what any value is intended to represent and how to get to it but we have no idea if the value is good.

Real-time Data Cleansing In dealing with real-time data, some streams of data can be quite “noisy”, containing spikes and outliers that distort the underlying measurement value. Application of statistical data cleansing algorithms can produce a higher quality data stream. This processed stream needs to be managed as a separate data thread from the original that can be used where appropriate.

Data Quality How do we identify bad data? Usually a single data point is hard to validate on its’ own. It needs to be analyzed as a component of a large data set where it can be compared to other values for consistency. Effective comparison of values makes use of the Production Data Management Model described above to ensure comparisons are valid and consistent.

Comparison Process Given the data the following can be used to determine internal consistency:

 validation  range checks  rules  mathematical operations  comparisons  balancing  statistical methods  data reconciliation  comparison to models  first principles  empirical  visualization  trends, charts, comparison to history

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Validation Range checks and other heuristics are usually a first level of defense for bad data. If it is a flow is it positive; if it is a percentage is it between 0 and 100; if it is a gross volume is it greater than or equal to the net volume. Most of these kinds o f checks can be automatically executed to flag bad data.

Most real-time historians can give you an associated data quality flag that can alert to bad values. These checks need to be done as close to the source as possible to hopefully eliminate gross errors before they begin to propagate through the system.

Mathematical Operations More complex mathematical operations can be applied to the data to detect internal inconsistencies. On a production facility a mass balance is one such technique. Do all the inputs minus the outputs add up to the accumulation within a balance envelope? If not, you may have a bad input, bad output; bad inventory readings or you have missed an input or output. The process of determining th e exact problem usually involves some detective work or the use of statistical reconciliation.

Statistical Methods Statistical reconciliation takes a complete plant material balance network with all the metered flows and inventories and adjusts the values within their accepted error ranges as described earlier. If a 100% balance can be achieved keeping all instrumentation values within their error range then the raw data are good. If not, some values will have had to slip beyond their declared error ranges. These values are statistically in error and need investigation.

Comparison to Models Simulation models of a process provide an additional level of consistency checking. Can the values fit a first principles or empirical model using the tuning parameters available?

Visualization Graphical visualization is a good method to identify anomalies. The anomalies can be generated by the process or can be problems in data. Either way they need to be followed up.

Versions Analysis of the data inevitably will identify problems with certain numbers, and these values will need to be corrected. Often it is not possible to correct the original data source especially if it is in a high scan rate DCS or historian. What is required is a manual override for the electronic version of the number.

If we say the operating version of the data are the raw values from the instrumentation or lab analyses, then one can expect to have six to eight additional versions of the data that need to be managed.

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Daily production numbers require aggregations from the operating data. These can form a Daily Basis version that contains the daily aggregations but also manual adjustments of those numbers to account for errors or missing values. This Daily Basis will be i terated upon until it is deemed acceptable and then it can be locked away as the best estimate for what happened on the day. At the end of the month aggregations are done across all the daily versions to come up with monthly numbers. These aggregated numbers may be adjusted to match monthly accounts without going back to the daily numbers and adjusting them. At some point the monthly numbers will be deemed good and locked as the best version. In addition there could be specific sets of data that get sent to differe nt regulatory bodies. All of these numbers will need to be saved for record purposes and to form a basis for adjustment if changes are needed later.

To meet regulatory requirements there needs to be a clear audit trail through the different versions of the data, the differe nt versions need to have role based security to ensure any changes are made by personnel with appropriate authority and once created the values are protected from change often from everyone including those who created the numbers.

Delivery Delivery of information to external systems and users is much easier if the data are already formalized in a model. There will always be some conversion in semantics from system to system and from standard to standard. Understanding what you have is key to delivering what someone else requires.

Production Data Management – Now What? So Production Data Management needs to manage the meta data associated to the values, it needs to manage the access paths to information, it needs to manage the validation processes for the data, it needs to manage the structure of the business to facilitate roll-ups and it needs to manage the multiple versions of data each with their own audit trail and security. With all of that under control information can be presented on displays and reports with confidence. The problem is there is usually much too much information for people to deal with. What Operations and Management really need is filtered information directed at managing their part of the business. What the Plant Manager really wants to know is “Are we going to meet the end of month targets?” and “Are there any issues requiring my attention?”. With the access to the data these kinds of questions can be answered by carefully constructed reports and dashboards.

Data Access through Standards With data organized and associated to its’ contextual information powerful analysis tools can begin to browse and filter the informatio n intelligently. This process is obviously made easier by the use of standards. If you have a plant historian with an OPC drive r, a generic OPC explorer can browse the tag list and values. If you have a SQL database, a data reporting tool can view the contents of the tables but not necessarily know what it is looking at. If you have Production Data there are many standards that cover different parts of the data. For example:

 WITSML (Wellsite Information Transfer Standard Markup Language) covers wellsite data  PRODML (Production Markup Language) covers upstream oil and gas,  API 689 / ISO 14224 covers the exchange of reliability and maintenance data for equipment,  MIMOSA (Machinery Information Management Open Systems Alliance) is aimed at operations and maintenance data  ISA S95 – international standard for the integration of enterprise and control systems  ISA S88 – ANSI Batch Standard  and more…

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There is one standard that aims to cooperate with all other standards. This is OPC-UA (Unified Architecture). It aims to allow object and information models defined by others (vendors, end-users, other standards ...) to be exposed without alteration by OPC-UA Servers. OPC-UA is still being developed but already production information can be browsed and compared between plant databases using OPC-UA interfaces.

Event Based Processing and Work Flow Being able to review information is good but if it doesn’t result in any action then what good is it. Combining analysis tools with workflow systems provides significant benefits. Actions or problems detected by an analysis tool need to get routed to the appropriate people or systems for follow-up. This firstly ensures that problems are followed up but secondly provides an audit trail of actions taken that can be used to verify compliance with regulatory requirements. Use of these tools progressively allows managers and operators to foc us on the issues that really need attention.

Semantic Data So far Production systems have generally been maintained in seclusion looked after in controlled technical databases. If we r eally want to take advantage of the semantics of our information why not publish it in terms appropriate for the Semantic Web. What this means is that information can be combined in a very general manner with information in any other similarly enabled systems. The advantages of doing this are that it opens up the use of a whole new suite of information processing tools and inference engines. Inference engines are driven by facts and rules to formulate new conclusions. If we want to move towards analysis tools figuring out more and m ore for us, then the ability to take advantage of the huge amount of resources already available for semantic processing will be useful.

Future of Production Data Management If we look to the industry think tanks like the Gartner Group and the Aberdeen Group their message to the industry is to:

 build an agile and innovative organization  improve critical processes and workflows  manage governance, risk and compliance  attract and retain customers  improve workforce effectiveness  maximize performance, profitability and competitiveness

A sound Production Data Management strategy can look to fulfilling these needs by:

 providing work flow automation, eliminating mundane tasks to facilitate innovation  ensuring best practices are deployed rapidly  delivering proven auditing and traceability  delivering consistent validated information to drive the business  providing the follow-up to ensure workforce follows best practices and procedures  providing rapid feedback on operations against plans

The requirements of regulatory bodies will result in a significant increase in recordkeeping and quality controls of data. Their requirements will only get more stringent and more encompassing over time. With greater control of information however comes greater opportunity to automate its processing and take advantage of active work flow systems. Adherence to standards will make life easier when communicating information across boundaries. Use of new semantic technologies will enhance the use of Production Data as a knowledge resource.

Essentially knowledge is power, and knowledge of your business gives you the power to react and take advantage of opportunities. Production data is what is driving that knowledge and must be managed to ensure its quality and reliability.

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For more information: For more information about Production Intelligence, visit our website www.honeywell.com/ps or contact your Honeywell account manager. www.matrikon.com [email protected]

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