1. Yao Tong, Tapan Mukerji Stanford University
Annual Meeting 2013
Stanford Center for Reservoir Forecasting
Uncertainty Assessment Study in Basin and Petroleum System Modeling Using Piceance Basin Dataset
Yao Tong, Tapan Mukerji
Stanford University

2. Outline
Basin and Petroleum System Modeling (BPSM) concept and project motivation
Piceance Basin Introduction/Dataset
1D basin model and Generalized Sensitivity Analysis (GSA) application
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3. Basin and Petroleum System Modeling (BPSM)
A digital data model simulates the interrelated processes of petroleum system
Tracks the evolution of a basin through time
Complex process model covers large spatial and temporal intervals
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4. Role of Basin and Petroleum System Modeling
Assists in improving scientific interpretations, resource assessment studies, archives data, scenarios, and interpretations (e.g. U.S. Geological Survey)
Quantifies the complex elements and processes
Tests different geological scenarios
Risk analysis in exploration and planning (e.g. enter a new play, next drilling spot)
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5. Uncertainty quantification remains challenging in BPSM
(Wygrala 2008)
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6. Motivation of this interdisciplinary project
Phase 1:
Construct 1D and 3D basin models for Piceance Basin , enhance the understanding of unconventional resources in Piceance Basin
Phase 2:
Conduct sensitivities analysis and test uncertainty quantification method in basin and petroleum modeling discipline
How to determine sensitive parameters to model response?
What is the impact of uncertainties in the input data on the model?
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7. Explore uncertainty quantification in BPSM using real world example – Piceance Basin
The Piceance Basin is a large structural basin in northwestern Colorado covers an area of approximately 15,500 km2
Quantitatively model unconventional gas resources using BPSM tools
Establish uncertainty quantification work flow in BPSM
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8. Piceance Basin Dataset
Dataset includes 231 well information provide great data resources
Additional seismic lines for 6 horizons
5 well with velocity information for regional stratigraphy control
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9. 1D Basin Model at Mobil Well T52-19G
Burial history with temperature overlay
Reconstruction of source rock burial history
Well location
Time (MA)
Study Objectives:
Reconstruct the burial history where the source rock been buried deepest
Study the source rock maturation (Ro) and transformation ratio (TR)
Apply GSA method and identify sensitivity parameters for Cameo Coal source rock maturation studies
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10. 1D Basin Model at Mobil Well T52-19G
2 model responses were investigated:
Vitrinite reflectance, Ro
a commonly used parameter represents the source rock maturation, indicate the oil/gas window
Transformation Ratio of Source Rock, TR
kerogen conversion index, the ratio of generated petroleum to total potential petroleum in a source rock
10050
Vitrinite reflectance ,Ro (%)
Transformation Ratio ,TR (%) 3 2 1
Time (MA)
Time (MA)
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11. GSA application on 1D Basin Model
Model uncertain parameters
Heat Flow(HF)
Hydrogen Index(HI)
Total Organic Carbon(TOC)
50 combinations of the above 3 parameters sampled from normal distribution
Model responses
Case 1: Vitrinite reflectance
Case 2: Transformation Ratio of Source Rock
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12. Application of Generalized Sensitivity Analysis for 1D model
Case 1: Source rock maturation (Ro) Response
Pareto plot of the standardized measure of sensitivity for each single parameter shows Ro is most sensitive to HF
Geological implications:
Acquire reliable Heat Flow data to get a better Ro prediction and source rock maturity estimation
Thermal history is most crucial and ranking at a higher level for future study, manpower, or funding
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13. Application of Generalized Sensitivity Analysis for 1D model
Case 1: Source rock maturation (Ro) Response (Continue)
Sensitivities in the multi-way interactions can be identified in the following groups:
Low HF correlated with High threshold values of TOC
High TOC correlated with Low value of HI
Geological interpretation for multi-way interaction sensitivity? .
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14. Application of Generalized Sensitivity Analysis for 1D model
Geological interpretation for multi-way interaction sensitivity:
Geological interpretation is non-trivial
Multi-way interaction sensitivity indicate the subtle sensitivities which may otherwise ignored
Possible geological interpretation:
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15. Application of Generalized Sensitivity Analysis for 1D model
Case 2: Source Rock Transformation Ratio(TR) Response
Pareto plot of the standardized measure of sensitivity for each single parameter shows Ro is most sensitive to HF and HI
Geological implications:
Acquire both Heat Flow and HI data to estimate the amount of source rock been ‘cooked’
Thermal history and source rock properties, HI specifically, are both crucial factors to quantify source rock transformation estimation
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16. Application of Generalized Sensitivity Analysis for 1D model
Case 2: Source Rock Transformation Ratio (TR) Response
Sensitivities in the multi-way interactions can be identified in the following one group:
Low HI with middle threshold values of TOC as sensitive interaction to the source rock transformation ration response
Geological interpretation :
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17. Conclusions from 1D study
BPSM convers large spatial and temporal intervals and requires efficient uncertainty quantification
Generalized Sensitivity Analysis provided a promising way to identify sensitivity parameters and guide the uncertainty quantification analysis
Sensitivity analysis results with geological interpretations
can benefit the modeling process
Ranking sensitive parameters
Reduce uncertain parameters
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18. Future Work
Generalized Sensitivity Analysis application on Piceance 3D basin model
Selection of model responses in 3D basin model
Investigation of spatial uncertainty in 3D basin model
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