1. Yao Tong, Tapan Mukerji Stanford University
Annual Meeting 2014
Stanford Center for Reservoir Forecasting
Quantifying Spatial Uncertainties and Application of Generalized Sensitivity Analysis in Basin and Petroleum System Modeling with a Case Study on Piceance Basin
Yao Tong, Tapan Mukerji
Stanford University

2. Motivation
Current common practice in basin modeling lacks capability to capture spatial uncertainties
Challenging to identify sensitive parameters for basin models
SCRF 2014

3. Basin and Petroleum System Modeling (BPSM)
Numerical model simulates geologic, thermal and fluid-flow processes in sedimentary basins over geological time span
Role of BPSM
Improving scientific interpretations and enhance understanding
Risk analysis tools in oil&gas exploration
Reconstructs the deposition of source, reservoir, seal and overburden rocks and the processes of trap formation and hydrocarbon generation, migration and
accumulation from past to present
SCRF 2014

4. Real world example – Piceance Basin
Piceance basin and major fields map
Present-day view of the 3D basin model
Large structural basin in northwestern Colorado
Covers an area of approximately 15,500 km2
Unconventional gas resources play with mature production history
SCRF 2014

5. Piceance Basin Model Mesaverde Gp. Cameo Coal
Grid 120 x 140 x 8, with horizontal grid size 1Km x 1Km, vertical grid ranges from meters to hundreds of meters
8 layers : Precambrian to Tertiary
Geological time span from 300 Ma to present-day
SCRF 2014

6. Uncertainty quantification for hydrocarbon prediction
Initial basin model prediction: present-day HC map
Uncertainty in the prediction?
HC amount, HC distribution
Quantify input uncertainties
Identify sensitive parameters
SCRF 2014

7. Summary of uncertain parameters
Primary uncertain factors:
Regional source rock quality and quantity
Thermal history impacted by uplift event
Parameter Type
Probability Distribution
Total Organic Carbon (TOC)
Continuous
Normal [60 20]
Hydrogen Index (HI)
Normal [150 50]
?? Source rock thickness ?
Thermal history
Discrete, scenario-based
2 thermal profiles,
equally probable
30 basin models from Monte Carlo Sampling
SCRF 2014

8. Quantify spatial uncertainty in model input - source rock thickness
Sparse data (18 wells) , poor regional control
Source thickness across the whole basin ?
Initial estimation map?
Underestimate spatial uncertainty
Topography map and well locations
(ft.)
SCRF 2014

9. Geostatistical tools and stochastic modeling workflow to quantify spatial uncertainty
Construct source rock thickness realization maps
(ft.)
120 by 140, each grid = one grid cell in basin model
Spherical variogram, range = 50 grid blocks
Generated using SGSIM, all conditioned to hard well data
SCRF 2014

10. Basin models from source rock thickness realizations
Model predictions of present-day HC maps
Initial model
Basin model 1
Basin model 2
Basin model 3
Initial Model
Basin Model 1
Basin Model 2
Basin Model 3
Source Rock
Thickness Input
Estimation
map
Source rock realization 1
Source rock realization 2
Source rock realization 3
Total Gas [Tcf]
93.18 73
100.6
82.78
SCRF 2014

11. Summary of uncertain parameters
Primary uncertain factors:
Regional source rock quality and quantity
Thermal history impacted by uplift event
Parameter Type
Probability Distribution
Total Organic Carbon (TOC)
Continuous
Normal [60 20]
Hydrogen Index (HI)
Normal [150 50]
Source rock thickness
Discrete, scenario-based
3 realizations,
equally probable
Thermal history
2 thermal profiles,
30 basin models from Monte Carlo Sampling
SCRF 2014

12. Identify sensitive parameters
Identify sensitive parameters from complicated model inputs using Generalized Sensitivity Analysis (GSA) method
Tackling uncertain parameters from various aspects
Geophysical, geological and engineering data
Continuous, discrete, scenario-based
SCRF 2014

13. Model responses for resource characterization
Selected model responses:
Present-day HC spatial distribution pattern
Gas resource amount accumulated in source rock (unconventional resource assessment)
Present-day HC prediction maps
SCRF 2014

14. Quantify model response appropriately
Model response 1.Present-day HC spatial distribution pattern
CHP (cluster-based histogram of patterns) method
Distances between pattern histograms represent dissimilarity of HC. spatial distribution patterns
SCRF 2014

15. Sensitive parameters for hydrocarbon spatial distribution patternSR
TOC
Thermal HI SR
TOC
Thermal HI
Source rock quantity, quality and thermal history are sensitive parameters for HC Spatial distribution
Indicate complexity of predicting hydrocarbon spatial distribution
Thermal --- apatite fission track
Source rock ---rarely obtained
SCRF 2014

16. Sensitive parameters for HC accumulation within source rock
Model response 2.Gas resources accumulation within source rock
Essential for quantify unconventional resources
Accumulation most sensitive to TOC, HI - source rock quality parameters HI
TOC SR
Thermal
SCRF 2014

17. Unconventional gas accumulation largely controlled by source rock quality
Comparison of gas accumulation in source rock
[Mtons]
Left : 55 wt% TOC; right 30 wt% TOC
Larger TOC provide more adsorption capacity, source rock retention capacity is tightly linked to the TOC content
SCRF 2014

18. Conclusions and future work
Capturing spatial uncertainties essential for resource characterization in BPSM
Stochastic modeling approach with geostatistical tools
Geological assumptions/inputs
Generalized Sensitivity Analysis - effective way of identifying sensitivities in BPSM
Quantify model inputs/responses appropriately
Modeling descriptive geological scenarios
Bridge statistical results and geoscience knowledge
SCRF 2014

19. Acknowledgement SCRF affiliates
Stanford Basin and Petroleum System Modeling Group
Dr. Ken Peters, Schlumberger
Dr. Paul Weimer, University of Colorado, Boulder
SCRF 2014

20. Backup
SCRF 2014

21. Total HC generation balance, thermal event dominant
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22. Transformation ratio for 2 thermal profiles
SCRF 2014