Page 367 - Intelligent Digital Oil And Gas Fields

308 Intelligent Digital Oil and Gas Fields

Midnight to 4 a.m.
- Automated analytics on op’s data 11 12 1
11 12 1 10 2
10 2 - Intelligent alerting 3 3 p.m. to 4 p.m.
9 3 - Gas lift and rod pump 9
8 4 - Opportunity mapping 8 7 5 4 12 1 - Afternoon meeting
7 5 6 11 - Track and document FO actions 11 12 1
6 - Actual vs target analytics 10 2 10 2
- Automated downtime and codes 9 3 - Track and document 9 3
8 4 Maintenance, WO teams 4
7 5 8
6 - Review optimization 7 5
7 a.m. to 9 a.m. opportunities 6
- Morning meetings - Update targets
11 12 1 11 12 1
10 2 - FO assignments 10 2 - Assess actions
9 3 - Opportunity assessment for PbE 9 3 - Assign night Crew FO actions
8 4 - Actual vs target assessment 8 4
7 5 7 5
6 - HSE issues 6
8 a.m. to 3 p.m. 11 12 1 4 p.m. to Midnight 11 12 1
- Continuous monitor of alarms and 10 2 - Update data base and analytics 10 2
9 3 - Close tickets 9 3
field activities 4 8 4
8 - Uplift tracking
- Monitor wells (dynacards) 7 6 5 - Automated updating of data- 7 6 5
11 12 1 - Well reviews as indicated 11 12 1
10 2 - Monitor FO activity 10 2 driven analytic models
9 3 - Communication with FO staff 9 3
8 4 8 4
7 5 - Communication with maintenance, 7 6 5
6
WO teams
- Opportunity execution
- Opportunity assessment for PbE
- Optimization strategy
Fig. 8.9 Example of activities in a 24-h DOF operational setting.
Figs. 8.10 and 8.11 show the combination of automated and manual
activities that can occur among the disciplines that interact with the raw data
and analytics, and that communicate with each other from and to a CWE,
offices,andthefield(mobile)tomakeimpactfuldecisionsthroughouttheday.
8.3.2 Change Management: High-Performance Teams
Business and industry literature consistently emphasizes the importance of
“people” over “technology.” That is, without a change in management pro-
cess focusing on team formation and dynamics with role clarity that has been
planned and implemented, technology investments (e.g., for a collaboration
center and other technical investments for well control, hardware/software,
etc.) will most likely not achieve the expected value. Fig. 8.12 shows a
model based on Tuckman (2016) and Tuckman and Jensen (1997) on the
effectiveness of team’s progress over time, assuming good leadership and
management support. Teams go through stages of formation, objective set-
ting, establishing relationships and role responsibilities, and ultimately to
high effectiveness as a team, which Tuckman refers to as forming, storming,
norming, performing, and adjourning, respectively. Over time, identifica-
tion as an individual declines in relative importance as individuals identify
more with the team, which results in improved team performance.
Lyden and Zernigue (2014) present an extensive case study from Chev-
ron for building an effective team for a DOF solution project, in part based
on the Tuckman model (Tuckman and Jensen, 1997). They summarized the