Improving asset and process management in Offshore gas production using MIDIHI industry data analytics

1. OFFSHORELYTICS
Speaker: Martijn Handels

2. Company introduction

3. 3
CENTRALIZING KNOWLEDGE & EXPERIENCE
MISSION
To maximize safety and
efficiency of offshore
operations by
centralizing their
knowledge & experience
INNOVATIVE
SERVICES
We offer dedicated
innovative end-to-
end solutions to
common challenges
for offshore industry
Offshore operations are like an airline. Remote operating assets are part of a
bigger fleet. In order to optimize this fleet you have to centralize its
knowledge and experience
PROPOSITION
COMPUTER
VISION
DATA
ANALYTICS
SMART
COMMUNICATION
MARKETS
OFFSHORE
OIL & GAS
OFFSHORE
WIND
SPECIAL
MARINE

4. SERVICING GLOBAL CLIENTS
FOCUS ON DRILLING & PRODUCTION
4

5. AS-IS scenario

6. Motivation
• The offshore oil and gas exploration and production is one of the industries that has
changed drastically in terms of technological developments and operation styles in the
past decade, esp. due to very harsh operating condition (weather, environment).
• It requires a continuous process improvement which will only be possible through
appropriate new technologies that are exploiting new opportunities for the
optimization.
• One of such sources is the data, since these systems are generating GBs of (useful)
data daily and this proposal is about an experiment how MIDIH industry data
analytics platform can be exploited for supporting innovations in this industry
segment.

7. Challenge: Data-driven Monitoring of the drilling process
• Drilling process is a complex activity:
• Long-run (continuous, without stops)
• Extreme weather conditions
• Lots of parameters influencing the process
• Need for an efficient monitoring and control:
• Automated reporting of KPIs
• Alarming in the case of anomalies / problems / faults

8. Available data: structure
• No of parameters: 46
• Dataset size: 19,3 GB
• List of parameters:
1 Time unitless
2 PIT_DRILL m3
3 PIT14 m3
4 PIT5 m3
5 PIT6 m3
6 GAINLOSSTOT m3
7 PIT17 m3

9. TO-BE scenario
PROVIDING RELIABLE KPIs WITH
ERROR PRONE DATA SOURCE

10. GENERATING REALIABLE KPI
10
1. CHALLENGE
Sensor inaccuracy and
changing operating
parameters cause
issues producing
reliable performance
metrics
2. SOLUTION
Build digital twin of
drilling phase detection
system that can
highlight data quality
issues and does not
require threshold-based
input parameters
3. APPROACH
1. Ingest data, clean
filter build digital twin
2. Analysis and indicate
sensor issue
3. Alert on issues
4. Generate daily KPI
reports
4. RESULT
Automatically curated
daily drilling KPI report:
• Allow for
benchmarking crew,
shift, rig, job
• Reduce manual task
by 90% and increase
accuracy to 95%

11. DRILLING KPI DASHBOARD
• Custom automated drilling performance
report and real time view
• Drill state based on MIDIH digital twin
• Per Crew, shift, well, region benchmarking
• Real time alerting of sensor issues
• Improving drilling performance by 5 –
15 %
11

12. Architecture and technical
details

13. OffshoreLytics
Service
Operational DATA
D2LabRig Portal
MIDIH Reference Architecture

14. REPOSITORY
CONCEPTUAL ARCHITECTURE
PROCESS MONITORING IN HARSH CONDITION AND AUTOMATED ANALYSIS STRUCTURED OPERATIONS
REPORTS
Usage of MIDIH
D2Twin service
DRILLING
PROCESS
TWIN
1. 2.
REFINEMENT
3.
OffshoreLytics
Operational
DATA

15. D2LabRig Portal: Automated reporting of KPIs
STRUCTURE
(unsupervised)

16. FIND
ANOMALIES
D2LabRig Portal: Automated reporting of KPIs

17. FIND ROOT
CAUSE
D2LabRig Portal: Automated reporting of KPIs

18. D2LabRig Portal: Automated reporting of KPIs

19. KPIs & Business impact

20. KEY PERFORMANCE INDICATORS
KPI-1: Completeness and soundness of the created process model (ProcessTwin)
Validation:
Done through the D2LabRig Portal. The model was learned on one dataset (Volve) and was tested on another
one (Bohr). The accuracy in the automated recognition of drilling phases was 100%
KPI-2: Efficiency of the model creation process
Validation
Done based on the performance tests for D2Twin. The creation of models took at maximum 10% of the
processing costs.
KPI-3: Accuracy of the created operational reports
Validation
Done through D2LabRig Portal. Several time periods were checked by the domain expert. Automated testing is
planned as well.

21. Use-Case:
Two identical drill ships operate under similar conditions. One outperforms the other
over 20%. Why?
Optimal interaction between crew and machine!
48M
Annual turnover
(avg.) per
offshore rig
26M
Operating cost
per year per
offshore rig
Save up
to 20%
Expected cost
reduction

22. ROLLOOS’ SOLUTION
“Combining domain specific services with advanced analytics and edge technology”
END-TO-END SERVICE
Dedicated services to support
operations
COMPUTER VISION
Deep learning
people detection
EDGE TECHNOLOGY
Robust high-performance edge
computing

23. OPTIMIZING PERFORMANCE
COMBINING RIG OPERATIONAL AND HUMAN
BEHAVIORAL DATA
Combining the embedded behavioral safety and
enhanced drilling productivity solutions to assist in
delivering even stronger operational efficiency.
Real-time data customers to view trends and to define
how to improve performance without endangering its
safety performance.
Using a customizable web-based dashboard, all
performance data is available both at the rig-site and
onshore.

24. 24

25. DISSEMINATION
DISSEMINATION IN COVID 19
Paper submission and virtual meeting / conferences
• IADC ART: Present at virtual IADC Advanced Rig Technology Committee (ART)
• Technical Paper: Joint paper for general submission to IADC (International Association
of Drilling Contractors) and SPE (Society of Petroleum Engineers)
• Technical session: Technical paper presentation submission Offshore technology
conference 2021, Houston Texas

26. 26

27. THANK
YOU!