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Intelligent Well Placement and Completion Technology and Business Models for reduced breakeven costs Massimo Virgilio Research Director Stavanger, Norway
Intelligent Well Placement and CompletionTechnology and Business Models for reduced breakeven costs
Massimo Virgilio
Research Director
Stavanger, Norway
Schlum
berger-Private
• Charter: Research on Subsurface Interpretation for reservoir characterization, reservoir management, and IOR and real time well placement
• Expertise profile 40+ professionals, including 30+ PhD, majority based in Stavanger
• DisciplinesGeology (Structural Geology, Sedimentology, Reservoir geology)Geophysics (Seismic, Electromagnetics, Potential Fields)Reservoir engineeringDrilling and measurements engineersSoftware engineeringMathematicsPhysics
AUTOMATIONDNA technology derived from Biotech
INTEGRATIONLumen for integration of Geosphere + Reservoir geoscience
EFFICIENCYArtificial Intelligencefor automatic Interpretation
Schlumberger Stavanger Research (SSR)
Schlum
berger-Private
▪ From industrial to digital
▪ Data Integration
▪ Workflow integration
▪ Discipline Integration
▪ Artificial Intelligence
▪ New relationship models between Operators & Service Companies
▪ Less transactional model
▪ More strategic environment
Outline
Disruptive Technology Evolved Business Model
Schlum
berger-Private
▪ From industrial to digital
▪ Data Integration
▪ Workflow integration
▪ Discipline Integration
▪ Artificial Intelligence
▪ New relationship models between Operators & Service Companies
▪ Less transactional model
▪ More strategic environment
Outline
Disruptive Technology Evolved Business Model
Schlum
berger-Private
The need of reducing Breakeven (B/E) costs of operating Brownfields
5
-22%
-12%
-14%
-37%
-19%
-24%
NAM
INT
Total
E&P Capex surveys
as of Dec-15 & Jan-16
2015 2016
2015 and 2016 Capex cuts
2016 vs. 2014
Total -41%
Int -34%
US -58%
-33%
-19%
-23%
NAM
INT
Total
Capex budgets
updated after Q4-15 Earnings(30 largest Schlumberger customers)
GDP DEMAND OVERSUPPLY INVESTMENTS B/E COST
=
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berger-Private
6
+
-
Challenges:
- How to steer the horizontal well within the oil-
column (above the oil-water contact and as close
as possible to the top of the reservoir)?
- How to steer relative to the faults’ location and
displacement?
- How to maximize the drainage of oil
(i.e. how to target the most oil-saturated part of the
reservoir)?
Context:
- Exmouth sub-basin, offshore Western Australia.
- Discovered in 2004.
- Excellent reservoir quality but viscous biodegraded
oil: Injectors (gas and water)
- Production capacity of 96,000 barrels per day
- Crude oil is offloaded from the vessel directly to
tankers for transport to market.
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berger-Private
Integration of data, workflows, and disciplines
Reservoir:
- Depositional environment: shelf margin
- Reservoir: Lower Cretaceous sandstones
- Seal: Cretaceous marine pelagic shales.
- Dips: sand layers inside the reservoir = 3°; top
reservoir=1°.
- Oil column: 40m to 55m
- “Sub-sets” of faults within the field: sand-to-
sand contact (drill through the faults)
- Horizontal producers. Gently dipping (1°)
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berger-Private
3D Seismic Volume
(Input)1
3D Fault Cube
(Ant tracking
technology)2
3D Fault patches
extraction
(Ant tracking
technology)
3
3D Seismic Volume
displayed in the DNA
space
4
Automated 3D Seismic
Interpretation
(Extrema technology)5
Dip log prognosis from
the seismic (lithologies
dip, azimuth, etc.).
SSR’s prototype
8
Mapping od DHIs
(potential drilling
hazards) with DNA
technology
3D Surface Oil-Water
contact (Extrema and
DNA technologies)
6
6
GeoSphere section9
Planned Well 7
Efficiency for Upstream means:
1. Automation of Workflows
2. Integration of Data and Disciplines
3. Artificial Intelligence
Schlum
berger-Private
Sand Thickness
Nominal well trajectory based on Static Reservoir Model
Integration of data, workflows, and disciplines
Schlum
berger-Private
+
-
Look Around
Nominal well trajectory based on Static Reservoir Model
Sand Thickness
Integration of data, workflows, and disciplines
Look Around
Maximum Net Detected
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berger-Private
Volume of Oil
+
-
Look Around
Integration of data, workflows, and disciplines
Schlum
berger-Private
Volume of Oil
+
-
Look Ahead
Integration of data, workflows, and disciplines
Schlum
berger-Private
Volume of Oil
+
-
Look Ahead
Integration of data, workflows, and disciplines
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berger-Private
14
Blind
Integration of data, workflows, and disciplines
Static Reservoir Model &
Cross Fingers
Look Around
Borehole tools
and real time
Look Ahead
Integreation of
Data, Workflow, Disciplines
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berger-Private
15
Surface Seismic
TOP Reservoir
Look Ahead
Lumen TOP Reservoir
5 meters
3 meters
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Interpretation as pillar for Artificial Intelligence for Upstream
16
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Artificial Interpretation for fault detection
Training dataOriginal Data
Automatic Interpretation (Machine Learning)Original Interpretation (benchmark)
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Artificial Interpretation for fault detection
18
Training dataOriginal Data
Automatic Interpretation (Machine Learning)Original Interpretation (benchmark)
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berger-Private
Integration of data and discipline for efficient operations
19
Blind Look Around Look Ahead Autonomous
Drilling
Blind Spots Autonomous
DrivingCollision Warning
City Map
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Real Time Lumen Workflow
Final calibration of
Reservoir structures
consistent with LWD
measurements
Post DrillingReservoir
Model
PHASE 3FINAL Model Repository
2 Weeks (min)
Seismic/GeoSphere
Calibration
While drilling GeoSphere &
seismic integration
LWDDATA
Displacing or
Remigration
Volumetrics
Reservoir model
While drilling
Model update
Well Trajectory
for Next Session
Iterate for each session in the well
PHASE 2Lumen: look ahead geosteering
REAL TIME
WELLPATH
RES.MODEL
Seismic quality
assessment
Structural analysis
Reservoir properties
Multiple scenarios
Predrilling Reservoir
Model
RES.MODEL
Baseline G&G
for Well Placement
PHASE 1INITIAL Model Repository
2 Weeks (min)
SEISMIC
PROCESSING
WELL LOGS
PROCESSING
G&GDATA
Iterate for each well in the field
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Quality of
Reservoir Model
PHASE 3
Deliverable
3Q3
PHASE 1
Deliverable
1Q1Drilling Time
Quality of Models
1 32
FIRST
WELL2
22
2 2 2 2
2
PHASE 3
Deliverable
3Q5
Next
WELL2
22
2 2 2 2
2
Update of Model Repository Improving
Field Development Plan
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Value Proposition
Integration of Disciplines
+
Automation of workflows
+
Real Time Process
1. Reduced Turnaround Time
– Interpretation is always in the critical path
of any field development and optimization
2. Enhanced data Access
– Information in the data is now maximized
and fully extracted
3. Maximized Human activities
– Elevated role of PT professionals, free from labourius tasks
4. Reduced Subjectivity issues
– Intepretation Process is much less subjective and more analytical
5. Enhanced Scalability to global field
– Local success available for global business
through Data Analytics, Artificial Intelligence, Machine Learning
Schlum
berger-Private
▪ From industrial to digital
▪ Data Integration
▪ Workflow integration
▪ Discipline Integration
▪ Artificial Intelligence
▪ New relationship models between Operators & Service Companies
▪ Less transactional model
▪ More strategic environment
Outline
Disruptive Technology Evolved Business Model
Schlum
berger-Private
▪ From industrial to digital
▪ Data Integration
▪ Workflow integration
▪ Discipline Integration
▪ Artificial Intelligence
▪ New relationship models between Operators & Service Companies
▪ Less transactional model
▪ More strategic environment
Outline
Disruptive Technology Evolved Business Model
Schlum
berger-Private
Exploration – Traditionally a sequential process
Operator Defines survey Defines technical Executes
requirements parameters
Service Executes Executes (Supports)
Company
Acquisition Processing Interpretation
▪ Sequential workflows
▪ Demarcated roles
▪ Different IT systems
▪ Time consuming
▪ Infrequent iterations
Traditional model for Seismic Data Acquisition, Processing, and Interpretation
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Reservoir model and FDP
Target and objectives
Trajectory design
Mod
el u
pdat
e
Well drilled to target
Well Placement
OPERATOR
SERVICE
COMPANY
Service Companies
Level of involvement
Target and objectives
Trajectory design
Well drilled to target
Well Placement
Reservoir model and FDP
OPERATOR
SERVICE
COMPANY
Same concept to be extended to entire E&P value chain
Schlum
berger-Private
▪ Economics
▪ Industry demographics
▪ Environmental
▪ Social expectations
▪ Technical complexity
▪ Tools, measurements
▪ Data Integration
▪ Workflow integration
▪ Discipline Integration
▪ Artificial Intelligence
▪ New relationship models between Operators & Service Companies
▪ More stratigic environment
▪ Less transactional model
External Forces Disruptive Technology Evolved Business Model
Improved efficiency for short and long termImmediate increased production rates
Greater value creation
