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Data Filtering and Conditioning 99

production periods or status conditions, each of which may need to be
detected; the most important periods or status conditions are “flowing”
and “steady state.” Other status conditions may be tracked, but these are
the most important. Flowing simply means that the well is open for flow.
Steady state always takes some time to develop after the well is flowing.
There may be a ramping of the choke, lift has to be established, and the
“flush flow” has to subside—or maybe the well is loaded and does not imme-
diately flow. In any case, it typically takes some time before the well is truly
“up.” Many workflows should only use the steady-state data, so it is impor-
tant to determine when this occurs after startup.

3.4 CONCLUSIONS
“It is all about the data!” We have heard this mantra throughout the
industry from managers and engineers on all types of projects and fields.
They recognize that the quality of their decisions is only as good as the data
quality, consistency, and at the required frequency for the analyses and deci-
sions they need to make. Data problems are inherent in any system and with
the advent of DOF high-frequency data, these issues can be more problem-
atic. This chapter presents a primer on how to manage sensor data streams
and to recognize data issues from the instruments, and how to treat that raw
data to generate quality, validated data. All of the techniques can be deployed
“inline” and in real time and do not need to be run in batch mode after the
data are collected. The following chapters show how to use data to make
engineering decisions in production and reservoir workflows.

REFERENCES
Al-Jasmi, A., Nasr, H., Goel, H.K., Querales, M., Rebeschini, J., Villamizar, M.A.,
Carvajal, G.A., et al., 2013a. Short-Term Production Prediction in Real Time Using
Intelligent Techniques. SPE-164813-MS. https://doi.org/10.2118/164813-MS.
Al-Jasmi, A., Nasr, H., Goel, H.K., Cullick, A.S., Villamizar, M., Velasquez, G.,
Rodriguez, J.A., Querales, M.M., Moricca, G., Carvajal, G.A., et al., 2013b. A Real-
Time Automated “Smart Flow” to Prioritize, Validate and Model Production Well Test-
ing. SPE-163813-MS. https://doi.org/10.2118/163813-MS.
Houze, O., Viturat, D., Ole, S. et al., 2017. Dynamic Data Analysis: The Theory and Practice
of Pressure Transient, Production Analysis, Well Performance Analysis, Production Log-
ging and the use of Permanent Downhole Gauge data. Paris, France V5.12.
Rebeschini, J., Querales, M., Carvajal, G., Villamizar, M., Md Adnan, F., Rodriguez, J.,
et al., 2013. Building Neural-Network-Based Models Using Nodal and Time-Series
Analysis for Short-Term Production Forecasting. SPE-167393-MS. https://doi.org/
10.2118/167393-MS.
Vetterli, M., Kovacevic, J., Goyal, V., 2014. Foundations of Signal Processing. Cambridge
University Press, Cambridge, UK.