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76 Intelligent Digital Oil and Gas Fields

different frequencies from different sources; fit-for-purpose and some high-
frequency data may be processed through time-series averaging processes.
Additionally, user trust and acceptance of DOF systems can be severely
limited by bad or questionable data. It is imperative to manage effectively the
DOF system data, which is especially difficult because of the large number of
data sources and databases involved. Solid data management procedures are
necessary but cannot keep up with all data values at all times. Engineers and
decision makers depend on data having the highest quality; that is, data that
has been processed by data validation, filtering, and conditioning proce-
dures. The IT department, SCADA specialists, and instrumentation special-
ists are often tasked with ensuring the data quality. Rather than counting on
each database to perform its own data management appropriate to DOF, it is
best to implement a DOF-based data validation and conditioning system,
across the entire DOF implementation.
This chapter presents the major features of such a system, which includes:
(1) data processing, (2) basic error detection, conditioning, and alerting, (3)
well and equipment status detection, (4) advanced validation, and (5)
workflow-based conditioning. This chapter is a condensed tutorial on
how to validate and condition data appropriately for DOF systems. The pro-
cess flow of the chapter is summarized in Fig. 3.1, which has the main steps
for a DOF data validation and conditioning system. One can also refer to a
myriad of specialty material on signal processing (e.g., Vetterli et al., 2014)
which is not covered here.

3.1 DOF SYSTEM DATA VALIDATION
AND MANAGEMENT
All integrated systems use three primary levels of data: raw instrument
data, calculated data, and asset hierarchy data. Examples of instrument data
are temperature, pressure, and flow. Calculated data can be allocations or
forecast data. Asset hierarchy data includes the organization of wells to
routes, or facilities, water injectors to producers, etc.
The first step in a data validation and conditioning (DV&C) management
system istocheck basic data transferprotocols andindividual high-priority data
feeds for all data types. There are two main levels at which data are checked.
First, the instrument data should be checked. All critical instruments, for
example, temperature, pressure, and flow rates, should undergo a range and
freeze check. Since a DOF system typically has hundreds to thousands of
data types, also referred to as data tags, this is a large task that should be