# API St 2560-2003 pdf free download

API St 2560-2003 pdf free download.Reconciliation of Liquid Pipeline Quantities.

1 Introduction 1.1 In the ideal world every drop of liquid received into a pipeline system and every drop delivered out of the system, as well as all liquid inventory within the system, would be mea- sured and accounted for precisely, and a comparison of all receipts and all deliveriesÑadjusted for inventory changesÑ would be exactly the same. The system would never experi- ence a loss or a gain. Unfortunately, this ideal pipeline bal- ance seldom exists in the real world. 1.2 Most pipeline systems typically experience some degree of loss or gain over time. This represents the normal loss/gain performance for a system. From time to time, losses or gains greater than normal may occur for a variety of rea- sons. Excessive or unexplained loss/gain often leads to con- tention between participating parties, sometimes requiring monetary settlements to adjust for abnormal loss/gain. In such cases, it is necessary to be able to (1) identify abnormal loss/gain as quickly as possible, (2) determine the magnitude of abnormal loss/gain, and (3) institute corrective actions. 1.3 Sometimes losses or gains are real, and adjustments must be made to correct shipper batches and/or inventories. Most of the time, though, there are no real physical losses or gains. The loss/gain that occurs in day-to-day operation is usually small (a fraction of a percent) and is caused by small imperfections in a number of measurements in a system. 1.4 In a sense, loss/gain is a measure of the ability to mea- sure within a system. Loss/gain should be monitored for any given system at regular intervals to establish what is normal for that system and to identify any abnormal loss/gain so that corrective action can be taken.

5.4 control chart: A graphical method for evaluating whether meter proving operations are in or out of a state of statistical control. 5.5 control limits: Are limits applied to a control chart or log to indicate the need for action and/or whether or not data is in a state of statistical control. Several control limits can be applied to a single control chart or log to determine when var- ious levels of action are warranted. Terms used to describe various control limits are Òwarning,Ó Òaction,Ó and Òtoler- anceÓ limits. 5.6 mean or central value: The average or standard value of the data being plotted on a control chart, and is the reference value from which control limits are determined. 5.7 standard deviation: The root mean square deviation of the observed value from the average. It is a measure of how much the data differ from the mean value of all the data. Stan- dard deviation can also be a measure of conÞdence level. Note: For further information concerning the application of Standard Deviation, reference API MPMS Chapters 13.1 and 13.2 5.8 statistical control: The data on a control chart are in a state of statistical control if the data hover in a random fash- ion about a central mean value, and at least 99% of the data are within the three standard deviation control limits, and the data do not exhibit any trends with time.

5.9 tolerance limits: Control limits that deÞne the extremes or conformance boundaries for variations to indicate when an audit or technical review of the facility design, oper- ating variables and/or computations may need to be con- ducted to determine sources of errors and changes which may be required to reduce variations. Tolerance limits are normally based on 99% or greater conÞdence levels, and are used inter- changeablely with Upper and Lower Control Limits. 5.10 upper and lower control limits: Synonymous with tolerance limits. 5.11 warning limits: Control limits applied to a control chart to indicate when equipment, operating conditions or computations should be checked because one or more data points were outside pre-established limits. Warning limits are normally based on 90 to 95 percent conÞdence levels.