Page 361 - Intelligent Digital Oil And Gas Fields

304 Intelligent Digital Oil and Gas Fields

Supervisor, engineers, and pumpers would convene in the main office and
field office to review wells requiring intervention and plan the day’s routes
for field personnel to then work on the routes.
The company automated the communication and data analytics from the
contractor’s server and moved all the data folders onto a dashboard available
on all computers in the company and on mobile devices (electronic tablets).
All stakeholders were able to see the wells identified for the day’s action and
reasons for the downtime or events. This new way of working meant that
the supervisor and engineer could collaborate and issue ticket instructions to
pumpers directly over mobile phones and tablets. After the system was in
place, the asset reported a 30% improvement in efficiency for downtime
and intervention metrics. Production engineers and supervisors were able
to spend more time on higher value activity, and pumpers were focused
on the well requirements as described above (Fig. 8.6).
Rocky Mountain oil production. An oil field in the central Rocky Moun-
tains had unacceptable downtime in winter months from freezing of the
low-pressure gas lines at gathering junctions, which caused the wells to
shut-in upstream. Each morning in the field office, field personnel reviewed
the electronic field measurements (EFM) of temperature and pressure in
flow lines and compressor stations. However, the data were not integrated
and could not be analyzed together, so it was not efficient to pinpoint exact
locations of bottlenecks. This situation means that to locate bottlenecks, field
personnel had to drive to a number of potential sites over a large geographic
area using gravel roads in the mountains (like the scenario in Fig. 8.5). Under
these rugged and slow conditions, it often took 2days to get a well back
online.
The solution was a new dashboard (Fig. 8.7) with automated data inte-
gration and analytics of the EFM to identify bottlenecks in real time. Wells
were color coded by time-dependent status based on variations in flow rate,
static pressure, and pressure change analytics, including rate of change in a
sensor-measured value. Data validation and conditioning were applied on
the real-time data (as described in Chapter 3). The dashboard also displayed
compressor pressures and flow, power efficiency, and capacity and sent real-
time notifications to supervisors and field personnel to address the wells the
same day (similarly to Fig. 8.6).
San Joaquin Valley cyclic steam production of heavy oil. Eldred et al. (2015)
describes a San Joaquin Valley California cyclic steam project; hundreds
of wells were being cycled on steam on varying cycles of injection, soak,
and production. In addition, the shallow production and induced fractures