This was presented in King Fahd University of Petroleum and Minerals (KFUPM) virtually in Dhahran, Saudi Arabia. In this presentation, I discussed about the promising role of data analytics in the three phases of CCS projects, namely ANN in the site selection phase, data-driven surrogate modeling in the sequestration phase, and CNN in the monitoring phase.
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
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 <<
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
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
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)
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