1. Digital Innovation in Oil & Gas

2. Reduced downtime/failures Artificial lift automation
Digital drives oilfield efficiency in different ways Digital investments can focus on surface equipment, wells, or the reservoir itself
Surface Operations
Equipment sensors
Reduced downtime/failures
Improved safety
To Target
Wells
Downhole sensors
Artificial lift automation
Reduced lifting cost
Daily optimization
To Target
The Reservoir
Reduced lifting cost
Optimized CAPEX
Improved recovery
Digital Oil Recovery™
To Target

3. Tubing Pres_slope >= -0.094
Case Study – Operations on the Surface A classification model used sensor data to predict spill events at a well and pad-level
Objective
Sensor data and tree-based modeling was used to develop a classification model to predict spills up to 7 days in advance
Use well and pad-level sensor data to predict and avoid future events.
Start with hundreds of spill events from dozens of wells and sensor data captured on the day of spill
N= 1000
Tubing Pres_first.quantile >- 59
Oil
Water
Chemical
N= 354
N= 646
Sensors used in models included casing, flowline, and tubing pressures
Casing Pres_Peaks < 11
Tubing Pres_slope >=
N= 472
N= 174
Tubing Pres_third.quantile < 63
Casing Pres_var >= 1.6
Operations
Drilling
Other
Events were classified based on key behaviors observed in sensor data
Development
N= 410
Casing Pres_first.quantile >= 8.1
Technical Failure
Undesired Behavior
Other
Results are used to develop classifier model to predict future spills based on sensor data
Equipment & Tools
N= 257
N= 97
N= 299
N= 111
N= 62
N= 118
N= 56

4. 1-Day Ahead Well-Level Results 7-Day Ahead Well-Level Results
Case Study – Operations on the Surface Model accurately predicted future events
The model predicted spills based on sensor data with an accuracy ranging from 77 – 88%
1-Day Ahead Well-Level Results
7-Day Ahead Well-Level Results
Predicted Spill?
Predicted Spill? No
Yes No
Yes No 64 11 No 72 6
Actual Spill?
Actual Spill?
Greg Mitchell
Yes 3 22
Yes 5 17
True Positive Rate: When there is actually a spill, how often does the classifier predict yes?
TP/actual + = 22/25 = 88%
True Positive Rate: When there is actually a spill, how often does the classifier predict yes?
TP/actual + = 17/22 = 77%

5. Reduced downtime/failures Artificial lift automation
Digital drives oilfield efficiency in different ways Digital investments can focus on surface equipment, wells, or the reservoir itself
Surface Operations
Equipment sensors
Reduced downtime/failures
Improved safety
To Target
Wells
Downhole sensors
Artificial lift automation
Reduced lifting cost
Daily optimization
To Target
The Reservoir
Reduced lifting cost
Optimized CAPEX
Improved recovery
Digital Oil Recovery™
To Target

6. Behavioral Forecast Model:
Digital Oil RecoveryTM Powered by FOROIL Machine learning, production data, and reservoir and well physics are used to construct a behavioral forecast model that accurately forecasts future production
Gather measured production data
Combine with equations of reservoir and well physics to generate a set of forecast models
Scan the space using machine learning to find the best forecast model
Best Forecast Model
Model 1
Behavioral Forecast Model:
+/- 5%
Model 2
Model n

7. Digital Oil RecoveryTM Powered by FOROIL
The behavioral forecast model is combined with massive parallel computing to run 15 million potential field development plans overnight to optimize the reservoir
Optimize across millions of development plans to find the optimal plan for the operator’s objective.
Case Studies

8. Currently implementing
Future of Digital Technology in Oil & Gas
Currently implementing
Aspiring to long-term
Exploring mid-term
Well optimization
3D reservoir modelling
Seismic data modeling and interpretation
Digital twins
Predictive maintenance
Virtual reality surveillance
Advanced process control
Machine learning
Prescriptive maintenance
Integrated earth models
Integrated remote operations centers
Remote controlled robots
GIS tracking for optimizing field workforce
Automated sales marketing
Autonomous drilling
Automated well designs
Well lifecycle analytics
Digital procurement
Integrated cross supplier ecosystem
Digital-driven value pricing
Automated 3D printing of parts
Blockchain and smart contracts

9. Final Thoughts
Still in the early-days of using new digital technologies to uncover opportunities for operations improvement
There is a temptation to drive a digital agenda by data availability or computing power alone
When opportunities are driven by business needs, targeted analytic techniques yield results that can be deployed
Low CAPEX and high potential payoff means industry should increase pilots for targeted digital technologies in the field
Workflows will change; organizational/operating model shifts will be needed to exploit the full value of digital

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