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In Chapter 5, we looked at instrument reliability, instrument measurement errors, and calibration methodologies. In this chapter, we will take a more detailed look at data preparation and preprocessing as well as quality-control (QC) procedures before conclusions are drawn or data is used in model-based interpretive and predictive techniques.

Data generally refers to a collection of organized information, usually the result of experience, observation, experiment, or a set of premises. In this context, data is not a collection of random numbers but connected by features corresponding to a physical system response.

Multiple steps must be followed after static and/or dynamic data is collected by any of a variety of means such as wireline or tubing-conveyed tools or permanently installed instrumentation, or even laboratory measurements. These steps are critical because the data are meaningless without conversion to relevant knowledge that may be used to either make or change a decision.

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