1. S-GEMS-UQ: An Uncertainty Quantification Toolkit for SGEMS
Annual Meeting 2013
Stanford Center for Reservoir Forecasting
S-GEMS-UQ: An Uncertainty Quantification Toolkit for SGEMS
Lewis Li, Alex Boucher, & Jef Caers

2. Geostatistical Modeling of Uncertainty
Geostatistical Realizations
Forward
model
Analysis of
Responses
Spatial
Model
parameters
Forward model
parameters
This figure represented a generalized workflow for uncertainty quantification. We first have field data that can be for instance, seismic data or well logs. From this data, we can abstract parameters that are used for spatial modeling. These are then passed into some geostatisical algorithm; be it variogram based or mult point statistics. The result realizations are then put through a forward model, (this could be either a full flow simulator or some proxy response). We then would analyze the response data to quantify uncertainty or do sensitivity analysis on the model parameters.
Field
Data
SCRF 2013

3. Software Challenges
Nearly every step of workflow is performed in separate programs
Needs extensive modification for different scenarios
Linking to external programs
Post processing is cumbersome due to parameters, models, responses, etc. being fragmented across different programs
Large size of data sets
Often need to generate hundreds or thousands of models, system needs to be responsive enough to accommodate
Ease of visualizing
No user friendly and intuitive interfaces
SCRF 2013

4. SGEMS-UQ Goals Compatible: Reduce/eliminate ad-hoc linking of codes
Flexible: Can be adapted regardless of:
Which geostatistical algorithms are generated
Which proxy models are used
Which simulation parameters are specified
How distances are computed
How users which to interpret results and perform sensitivity analysis
Extensible: Framework for data mining
Scalable: Be scalable for large datasets
User Friendly: Provide intuitive GUI
SCRF 2013

5. Goal: Compatibility Link with SGEMS 3.0
Develop as a plugin
Link with external flow simulators
Results are usually comma separated values
Use XML (Extensible Markup Language)
SCRF 2013

6. Response XML Responses can be of a variety of formats
Vector
Scalar
Time series
Probability distribution
Defined a specified XML format with appropriate tags
Program detects response type and selects appropriate data structure and links to corresponding object
Provide Python script to convert exported files into this format

7. Goal: Extensible Modular Design
XML combined with SQL database for storing parameters
Allows for a data mining framework

8. Data Manipulation Language
SELECT ... FROM ... WHERE ...
INSERT INTO ... VALUES ...
UPDATE ... SET ... WHERE ...
DELETE FROM ... WHERE ...
Ex: SELECT * FROM SGSIM WHERE TrainingImage = TI1
All we need to know is which algorithm each realization was generated with
Use a hash table to store this in memory

9. Data Mining SQL SQL Parameter A Parameter B Parameter C … Parameter A

10. Distance Based Uncertainty Quantification

11. SGeMS-UQ Workflow Access SGeMS main program workspace
Read, store and manage simulation parameters
Read proxy responses from third party simulators
Display responses
Compute MDS
Perform k-medoid clustering
Export clustering results
Data mine the parameters and perform sensitivity analysis

12. SCRF 2011