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

Outline Basin and Petroleum System Modeling (BPSM) concept and project motivation Piceance Basin Introduction/Dataset 1D basin model and Generalized Sensitivity Analysis (GSA) application SCRF 2013

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 SCRF 2013

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) SCRF 2013

Uncertainty quantification remains challenging in BPSM (Wygrala 2008) SCRF 2013

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? SCRF 2013

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 SCRF 2013

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 SCRF 2013

1D Basin Model at Mobil Well T52-19G Burial history with temperature overlay Reconstruction of source rock burial history Well location 300 Time (MA) 0 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 SCRF 2013

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 60 40 20 0 Time (MA) 60 40 20 0 Time (MA) SCRF 2013

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 SCRF 2013

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 SCRF 2013

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? . SCRF 2013

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: SCRF 2013

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 SCRF 2013

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 : SCRF 2013

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 SCRF 2013

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 SCRF 2013