Page 226 - Intelligent Digital Oil And Gas Fields

178 Intelligent Digital Oil and Gas Fields

prediction based on analytical models, and finally (6) generate actions, les-
sons learned, and recommendations to improve field operation.

5.4.1 Business Model
Smart production surveillance workflows should be built to focus on: (1)
controlling, mitigating, and reducing those factors that influence production
downtime and total production losses and (2) improving team productivity
and process efficiency. The surveillance dashboard enables a management-
by-exception approach that significantly improves performance. Main fac-
tors that can impact net present value (NPV) and internal rate of return
(IRR) include underperforming wells, ESP, GL, PCP, compression, and
other facilities that are down, poor data quality, and nonproductive time
(NPT) team member. Figs. 5.15 and 5.16 show field-level KPIs and
well-level KPIs that can be monitored and improved by smart surveillance
to improve business model performance.

5.4.2 Main Components of Smart Production Surveillance
The main components of smart production surveillance include the
following:
• Sensors: Surface sensors measuring pressure and temperature are required.
Production rate or multiphase flow metering are desired, if economics
allow; however, a VFM should be an essential tool for each well.
Down-hole equipment (ESP/GL/ICV) could help to record flowing
bottom-hole pressure and temperature in real time.

Production Metric # wells under # wells # wells with
Field target Field today # wells High wc%
production production losses & equip shut-in IPR with NO or GOR poor
Gains downtime performance Issues forecast
Fig. 5.15 Field-level KPIs that contribute to business model performance.

Fig. 5.16 Well-level KPIs used as the main metrics to measure business model
performance.