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

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

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

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

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

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

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

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 150 0(ft.) SCRF 2014

Geostatistical tools and stochastic modeling workflow to quantify spatial uncertainty Construct source rock thickness realization maps 150 0(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

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

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

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

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

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

Sensitive parameters for hydrocarbon spatial distribution pattern SR 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

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

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

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

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

Backup SCRF 2014

Total HC generation balance, thermal event dominant SCRF 2014

Transformation ratio for 2 thermal profiles SCRF 2014