Geodetic Framework – QA/QC System

QA/QC System

Consider a database dedicated to the storage of seismic navigation data or wells.  Simple examples of business rules to be embedded within the QA/QC workflow might include:

  • Does the data have coordinates assigned to it? Geographical and or projected.
  • Does the data have a coordinate reference system assigned to it?
  • Is there a recognized vertical datum applied?
  • What tolerance levels will be defined for each test? This must consider vintage and terrain.
  • What level of variability will be permitted against the tolerances?
  • What standards must it reach before it is deemed fit for purpose?

The quality control system adopted will use the business rules embedded in a set of procedures and workflows to establish data integrity.  It will check the variance of the data against the defined standard.

The business rules adopted within the quality system will govern the sophistication of the QC process.  This will depend upon the elements of the data that need to be assessed to determine the usefulness of the data?  The business rules will control the overall quality assurance process and aims at providing users of the data with a pledge or guarantee that the data met the quality requirements.  Where data does not meet the requirement the operator will be informed to use the data with caution.

The nature of the E&P business necessitates the integration of many different data types of varying vintages within the same project.  Data types will include seismic data, wellbore data, well logs, culture data etc.  The source and vintage of the data will typically influence the CRS to which it is referenced and the format in which it is delivered.  However, successful data integration will require a thorough understanding of the geo-spatial parameters of each dataset to ensure mis-ties and gross errors do not manifest themselves or become unnecessarily magnified.  Assurances must be made prior to data being submitted to the corporate navigation data store.  It must undergo a series of checks that will determine its suitability.   As the data is analyzed an estimate of its quality should be determined in terms of the probability of errors of a certain size of greater being present within the geo-spatial component of the data.

Often, only single data volumes will be accessible for a specific exploration project, e.g. one 2D or 3D seismic dataset.  What happens if these data are rejected during the QA/QC checks?  There will not be another dataset to fall back on.  Therefore, the QA/QC process must determine the extent to which the data failed the tests.  It is the only data available and therefore the only data the project team can use.  But if the errors are understood the data can be used by the project team with the knowledge that it has reliability issues and thus any business decisions made from the data carry a larger error budget.  For example, a proposed well location will have an error ellipse (error foot print) equal to the expected error budget.  This may be hundreds of meters in size where there are discrepancies with CRS definition.

 

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