The document discusses the role of data-driven analytics in carbon capture and storage (CCS), covering site selection, sequestration, and monitoring phases. It highlights the importance of understanding permeability ratios for CO2 storage efficiency, utilizing artificial neural networks for predictions, and employing smart proxy models to enhance simulation processes. Additionally, it emphasizes the use of convolutional neural networks for monitoring microseismic events during CO2 injection.

1. DATA-DRIVEN ANALYTICS
IN CARBON CAPTURE AND
STORAGE
Yohanes Nuwara
KFUPM, 10 November 2021
1

2. Outline
● Geological storage of CO2
● Role of data analytics in the life of CCS
● Data analytics during site selection
● Data analytics during sequestration
● Data analytics during monitoring

3. Geological Storage of CO2
● Viable solution for reducing
GHG emission from carbon-
intensive industries
● Many options of storage:
○ Deep saline aquifer
○ Depleted oil and gas reservoir
○ Igneous hard rock basement
● 3 important aspects of storage:
○ Capacity
○ Containment
○ Injectivity

4. Examples of existing CCS facilities
Sleipner CCS, North Sea
Quest CCS, Canada CarbFix CCS, Iceland

5. Role of Analytics in the Life of CCS

6. Data analytics in site
selection phase

7. What controls CO2 distribution?
● CO2 distribution is controlled
by the permeability anisotropy
● Permeability in vertical
direction (kv) is different from
horizontal (kh)
● Can be described by kv/kh
(ratio of kv to kh)
● This determines efficiency of
CO2 storage
S = f(kv/kh)
kv/kh >> kv/kh <<

8. Knowing vertical-to-horizontal
permeability ratio (kv/kh) is
important to determine storage
efficiency and finally to rank storage
options

9. kv/kh >>>
STRUCTURAL TRAP
For CO2 stored in structural traps, kv/kh must be LARGE

10. kv/kh <<<
STRATIGRAPHIC TRAP
For CO2 stored in stratigraphic traps, kv/kh must be SMALL
kv/kh <<<

11. Unfortunately, there is none exact
formula that determines kv/kh from
geological data. Instead, we can use
data-driven approach to “PREDICT”
kv/kh

12. North Sea Fields
● Published by GeoProvider AS
● 1,240 documented well data
● Each records consists of:
○ Core porosity
○ Core permeability (kv and kh)
○ Core saturations
○ Sedimentary descriptions
(roundness, sorting, structure,
cementation)
Courtesy MapStand

13. Sedimentary controls on permeability
Very Well Sorted Well Sorted Moderately Sorted
Poorly Sorted Very Poorly Sorted

14. Sedimentary controls on permeability

15. Grain density
Sorting
Roundness
Cementation
Porosity
kv/kh
ARTIFICIAL NEURAL NETWORK

16. WHICH LOCATION IS THE BEST?
From depositional environment to kv/kh to storage efficiency
A
B
C

17. Core-based prediction
Allin et al, 2017

18. Data analytics in
sequestration phase

19. The use of reservoir simulation
NUMERICAL SIMULATION SURROGATE MODELING
Method Finite difference, Finite element Neural network, Reinforcement
learning
Computation Takes days to finish one run Take seconds to finish one run
Data Reservoir model Results of >10 simulation runs
as training data
Limitation Numerical error Inefficient optimization

20. Smart Proxy Model
● Case study in Otway Basin, Australia
● Spatio-temporal database was
developed from multiple runs
consisting of:
○ Static data
○ User defined parameters
○ Well data
○ Dynamic data
Mohagegh (2018)
Numerical
simulation
Proxy
model
Misfit

21. Data analytics in monitoring
phase

22. Monitoring
DAS interrogator
FO cable
Observer well
CO2 injector
CO2 migration
in reservoir
FRACTURE
DAS record: No event
Green light: Safe to continue

23. Monitoring
DAS interrogator
FO cable
Observer well
CO2 injector
CO2 migration
in reservoir
FRACTURE
DAS record: Event detected
Yellow light: Reduce injection
EVENT

24. Monitoring
DAS interrogator
FO cable
Observer well
CO2 injector
CO2 migration
in reservoir
FRACTURE
Red light: Stop injection
EVENTS !!!
DAS record: Event detected

25. Hypocenter relocation with inverse modeling
Initial
location
(x, y, z, t0)
Velocity
model
Forward modeling
(ray tracing)
Calculated travel
time (tcal)
tobs-tcal
<<<
Minimize error
(optimization)
Update location
(x, y, z)
Observed
travel time
(tobs)
Final
location
(x, y, z)
Start
Finish
NO
YES

26. Convolutional Neural Network
Wamriew et al (2021)
● Training
○ Forward modeling to
generate 60,000 synthetic
DAS seismograms
○ Added with ambient noise
○ CNN learns the synthetic
data
● Prediction
○ Real DAS seismogram is
fed to CNN
○ Predict event hypocenter
locations (x, z)

27. Convolutional Neural Network
Wamriew et al (2021)
ResNet50 architecture

28. Convolutional Neural Network
Wamriew et al (2021)

29. Conclusion
● In site selection phase, Artificial Neural Network (ANN) helps predicting
kv-kh ratio from porosity and core sedimentary data
● In sequestration phase, Smart Proxy Model helps predicting CO2
saturation distribution that can cut lengthy simulation processes
● In monitoring phase, Convolutional Neural Network (CNN) could help
identifying microseismic events and predicting hypocenter recorded in
DAS cables

30. Thank you