Appendix C:
PROPERTY INDICATORS

It is important to understand the general nature of Property Indicators. They are intended to provide broad classifications for Objects. Such classifications can help the Consumer decide how to handle the Object, i.e., based on what has been done to the data of an Object decide what needs to be done next. For example, the Property Indicator that the data associated with a Channel has been filtered does not indicate what the specific filtering algorithm was. However, it may indicate that additional filtering is unnecessary.

Property Indicators are dictionary-controlled Codes (Representation Code IDENT).

Property Indicator Options	Description
AVERAGED	The data is the average of two or more other data sources.
CALIBRATED	A calibration process has been applied to the data.
CHANGED-INDEX	The data has been resampled along an index that is different from its original sampling index. For example, data that was originally sampled according to time is resampled according to depth.
COMPUTED	The data is an output of a transform, e.g., function former, based on input data from a single tool.
DEPTH-MATCHED	The data has been aligned along its depth index against a reference data source.
DERIVED	The data is an output of empirical equations or is the solution of log response equations based on input data from more than one tool.
FILTERED	A filtering process has been applied to the data.
HOLE-SIZE-CORRECTED	The data has been corrected for hole size effect based on the value of a hole size input.
INCLINOMETRY-CORRECTED	The data has been corrected to standard directional references (vertical axis and North axis).
LITHOLOGY-CORRECTED	The data has been computed or corrected based on the value of a matrix lithology parameter.
LOCAL-COMPUTATION	The data is the result of locally-defined computational expressions. Such data is normally experimental.
LOCALLY-DEFINED	The Object's Name has been created by the operator. This Name might not be dictionary-controlled and may have no semantic association with the Object's data.
MODELLED	The data is the output of a theoretical model and is not derived from any measured quantity.
MUDCAKE-CORRECTED	The data has been corrected for mud cake effect.
NORMALIZED	The data has been corrected so that its range corresponds to a prescribed norm.
OVER-SAMPLED	Interpolated data samples have been added to the original data to align this data with other data.
PATCHED	The original data values have been replaced at specific levels. This is normally done to remove spurious values (e.g., spikes).
PRESSURE-CORRECTED	The data has been corrected for hydrostatic pressure (mud weight).
RE-SAMPLED	The data has been resampled along its original index. For example, as the result of a depth correction process it is determined that the original index values are incorrect. Using a curve fitting process it is possible to determine new data values for the original index values that represent the original signal more accurately.
SALINITY-CORRECTED	The data has been corrected for salinity effect.
SAMPLED-DOWNWARD	The original sampling direction is downward.
SAMPLED-UPWARD	The original sampling direction is upward.
SPEED-CORRECTED	The data has been corrected for variations of the downhole tool speed.
SPLICED	The data has been obtained by concatenating two or more other data sources.
SQUARED	The data is the result of a squaring process, i.e., a process that converts a smooth function into a step function.
STACKED	The data is the sum of two or more other data sources.
STANDARD-DEVIATION	The data represents the estimated standard deviation, due to environmental factors or incoherence of the computational model, of another data source.
STANDOFF-CORRECTED	The data has been corrected for standoff effect.
TEMPERATURE-CORRECTED	The data has been corrected for temperature effect based on the value of a temperature source.
UNDER-SAMPLED	Some of the original data samples have been discarded to reduce the amount of data or to align this data with other data.