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The Future of Production Data Management from Meter to Auditor

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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. The Future of Production Data Management From Meter to Auditor 2 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 The Future of Production Data Management From Meter to Auditor 3 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 The Future of Production Data Management From Meter to Auditor 4 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. The Future of Production Data Management From Meter to Auditor 5 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. The Future of Production Data Management From Meter to Auditor 6 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
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