Generalized sensitivity analysis study in basin and petroleum system modeling, case study on Piceance Basin, Colorado

Abstract

Basin and petroleum system modeling is an interdisciplinary endeavor, and utilizes integrated data to study sedimentary basins. Numerical models are constructed and simulated to quantitatively model the geodynamic processes in sedimentary basins. Often, the modeling process covers large spatial and temporal intervals with many uncertain input parameters. These can be continuous parameters characterized by certain statistical distribution, spatially distributed variables, or discrete parameters such as geological scenarios. Identifying sensitivities from these complex input model parameters and recognizing key uncertainties are crucial and challenging for basin modeling development and applications. The major contribution of this work is to introduce and implement efficient and accurate sensitivity analysis approach for basin and petroleum system modeling discipline.We investigated two types of sensitivity analysis methods and compared their performance for identifying the impact of uncertain parameters on both spatial and temporal model responses. The first approach utilizes the variance-based Sobol indices to quantify parameter sensitivities, while the second approach is a distance-based sensitivity analysis which utilizes the distance between model responses to determine sensitivities of different parameters. The sensitivity analysis approaches are illustrated through a basin modeling example involving the processes of sediment compaction, source rock maturation and hydrocarbon generation in the Piceance Basin, Colorado, US. Monte Carlo samples of the input uncertainties related to physical properties of the source rock, thermal boundary conditions, and geological setting scenarios are generated. Multiple basin models constructed using these uncertain input parameters are simulated across the geological time span, and time-varying model response (hydrocarbon generation from Lower Cretaceous to present-day) and spatial model response (pressure and porosity distribution at present-day) are obtained. Sensitive parameters that impact these spatio-temporal model responses are then analyzed.Results show that the distance-based sensitivity analysis approach could achieve similar results with fewer model runs compared to the variance-based Sobol method. Model responses in spatial and temporal domain are impacted by different uncertain input parameters. Subtle relationship between input parameters and model response could also be identified. In particular an unexpected link between chemical kinetics and porosity versus depth behavior was uncovered. The knowledge obtained from sensitivity studies enhance the understanding of the complex geological processes and can benefit the modeling development and forecast capability. Though the sensitivity results are case-specific, the approach and workflow are generally applicable to other basins and earth sciences modeling studies.