Zhangshuan Hou

Direct reservoir parameter estimation using joint inversion of marine seismic AVA and CSEM data

A new joint inversion algorithm to directly estimate reservoir parameters is described. This algorithm combines seismic amplitude versus angle (AVA) and marine controlled source electromagnetic (CSEM) data. The rock-properties model needed to link the geophysical parameters to the reservoir parameters is described. Errors in the rock-properties model parameters, measured in percent, introduce errors of comparable size in the joint inversion reservoir parameter estimates. Tests of the concept on synthetic one-dimensional models demonstrate improved fluid saturation and porosity estimates for joint AVA-CSEM data inversion (compared to AVA or CSEM inversion alone). Comparing inversions of AVA, CSEM, and joint AVA-CSEM data over the North Sea Troll field, at a location with well control, shows that the joint inversion produces estimated gas saturation, oil saturation and porosity that is closest (as measured by the RMS difference, L1 norm of the difference, and net over the interval) to the logged values whereas CSEM inversion provides the closest estimates of water saturation.

A Bayesian model for gas saturation estimation using marine seismic AVA and CSEM data

We develop a Bayesian model to jointly invert marine seismic amplitude versus angle (AVA) and controlled-source electromagnetic (CSEM) data for a layered reservoir model. We consider the porosity and fluid saturation of each layer in the reservoir, the bulk and shear moduli and density of each layer not in the reservoir, and the electrical conductivity of the overburden and bedrock as random variables. We also consider prestack seismic AVA data in a selected time window as well as real and quadrature components of the recorded electrical field as data. Using Markov chain Monte Carlo (MCMC) sampling methods, wedraw a large number of samples from the joint posterior distribution function. With these samples, we obtain not only the estimates of each unknown variable, but also various types of uncertainty information associated with the estimation. This method is applied to both synthetic and field data to investigate the combined use of seismic AVA and CSEM data for gas saturation estimation. Results show th...

Reservoir-parameter identification using minimum relative entropy-based Bayesian inversion of seismic AVA and marine CSEM data

A stochastic joint-inversion approach for estimating reservoir-fluid saturations and porosity is proposed. The approach couples seismic amplitude variation with angle (AVA) and marine controlled-source electromagnetic (CSEM) forward models into a Bayesian framework, which allows for integration of complementary information. To obtain minimally subjective prior probabilities required for the Bayesian approach, the principle of minimum relative entropy (MRE) is employed. Instead of single-value estimates provided by deterministic methods, the approach gives a probability distribution for any unknown parameter of interest, such as reservoir-fluid saturations or porosity at various locations. The distribution means, modes, and confidence intervals can be calculated, providing a more complete understanding of the uncertainty in the parameter estimates. The approach is demonstrated using synthetic and field data sets. Results show that joint inversion using seismic and EM data gives better estimates of reservoir parameters than estimates from either geophysical data set used in isolation. Moreover, a more informative prior leads to much narrower predictive intervals of the target parameters, with mean values of the posterior distributions closer to logged values.

Uncertainty Analysis of Runoff Simulations and Parameter Identifiability in the Community Land Model: Evidence from MOPEX Basins

AbstractIn this study, the authors applied version 4 of the Community Land Model (CLM4) integrated with an uncertainty quantification (UQ) framework to 20 selected watersheds from the Model Parameter Estimation Experiment (MOPEX) spanning a wide range of climate and site conditions to investigate the sensitivity of runoff simulations to major hydrologic parameters and to assess the fidelity of CLM4, as the land component of the Community Earth System Model (CESM), in capturing realistic hydrological responses. They found that for runoff simulations, the most significant parameters are those related to the subsurface runoff parameterizations. Soil texture–related parameters and surface runoff parameters are of secondary significance. Moreover, climate and soil conditions play important roles in the parameter sensitivity. In general, water-limited hydrologic regime and finer soil texture result in stronger sensitivity of output variables, such as runoff and its surface and subsurface components, to the inpu...

Parametric sensitivity analysis of precipitation at global and local scales in the Community Atmosphere Model CAM5

We investigate the sensitivity of precipitation characteristics (mean, extreme, and diurnal cycle) to a set of uncertain parameters that influence the qualitative and quantitative behavior of cloud and aerosol processes in the Community Atmosphere Model (CAM5). We adopt both the Latin hypercube and Quasi-Monte Carlo sampling approaches to effectively explore the high-dimensional parameter space and then conduct two large sets of simulations. One set consists of 1100 simulations (cloud ensemble) perturbing 22 parameters related to cloud physics and convection, and the other set consists of 256 simulations (aerosol ensemble) focusing on 16 parameters related to aerosols and cloud microphysics. In the cloud ensemble, six parameters having the greatest influences on the global mean precipitation are identified, three of which (related to the deep convection scheme) are the primary contributors to the total variance of the phase and amplitude of the precipitation diurnal cycle over land. The extreme precipitation characteristics are sensitive to a fewer number of parameters. Precipitation does not always respond monotonically to parameter change. The influence of individual parameters does not depend on the sampling approaches or concomitant parameters selected. Generally, the Generalized Linear Model is able to explain more of the parametric sensitivity of global precipitation than local or regional features. The total explained variance for precipitation is primarily due to contributions from the individual parameters (75–90% in total). The total variance shows a significant seasonal variability in midlatitude continental regions, but very small in tropical continental regions.

Joint inversion of seismic AVO and EM data for gas saturation estimation using a sampling- based stochastic model

Summary A stochastic model is developed to estimate gas saturation and porosity using seismic AVO and EM data. Markov chain Monte Carlo (MCMC) sampling methods are used to obtain posterior probability density functions of unknown parameters constrained by seismic AVO and EM data and prior information. Unlike conventional inverse methods, which search for an optimal solution giving the smallest misfit, MCMC methods estimate probability density functions of unknown gas saturation and porosity. This allows for evaluation of uncertainty as well as estimation of those parameters. A synthetic study, typical of gas exploration in the deep water of the Gulf of Mexico, is developed to demonstrate the benefits of joint inversion of seismic AVO and EM data. Results show that the inclusion of EM data reduces the uncertainty and ambiguity in gas saturation and porosity estimation.

An Uncertainty Quantification Framework for Studying the Effect of Spatial Heterogeneity in Reservoir Permeability on CO2 Sequestration

A new uncertainty quantification framework is adopted for carbon sequestration to evaluate the effect of spatial heterogeneity of reservoir permeability on CO2 migration. Sequential Gaussian simulation is used to generate multiple realizations of permeability fields with various spatial statistical attributes. In order to deal with the computational difficulties, the following ideas/approaches are integrated. First, different efficient sampling approaches (probabilistic collocation, quasi-Monte Carlo, and adaptive sampling) are used to reduce the number of forward calculations, explore effectively the parameter space, and quantify the input uncertainty. Second, a scalable numerical simulator, extreme-scale Subsurface Transport Over Multiple Phases, is adopted as the forward modeling simulator for CO2 migration. The framework has the capability to quantify input uncertainty, generate exploratory samples effectively, perform scalable numerical simulations, visualize output uncertainty, and evaluate input-output relationships. The framework is demonstrated with a given CO2 injection scenario in heterogeneous sandstone reservoirs. Results show that geostatistical parameters for permeability have different impacts on CO2 plume radius: the mean parameter has positive effects at the top layers, but affects the bottom layers negatively. The variance generally has a positive effect on the plume radius at all layers, particularly at middle layers, where the transport of CO2 is highly influenced by the subsurface heterogeneity structure. The anisotropy ratio has weak impacts on the plume radius, but affects the shape of the CO2 plume.

Sensitivity of turbine-height wind speeds to parameters in planetary boundary-layer and surface-layer schemes in the weather research and forecasting model

We evaluate the sensitivity of simulated turbine-height wind speeds to 26 parameters within the Mellor–Yamada–Nakanishi–Niino (MYNN) planetary boundary-layer scheme and MM5 surface-layer scheme of the Weather Research and Forecasting model over an area of complex terrain. An efficient sampling algorithm and generalized linear model are used to explore the multiple-dimensional parameter space and quantify the parametric sensitivity of simulated turbine-height wind speeds. The results indicate that most of the variability in the ensemble simulations is due to parameters related to the dissipation of turbulent kinetic energy (TKE), Prandtl number, turbulent length scales, surface roughness, and the von Kármán constant. The parameter associated with the TKE dissipation rate is found to be most important, and a larger dissipation rate produces larger hub-height wind speeds. A larger Prandtl number results in smaller nighttime wind speeds. Increasing surface roughness reduces the frequencies of both extremely weak and strong airflows, implying a reduction in the variability of wind speed. All of the above parameters significantly affect the vertical profiles of wind speed and the magnitude of wind shear. The relative contributions of individual parameters are found to be dependent on both the terrain slope and atmospheric stability.

Next‐Generation Intensity‐Duration‐Frequency Curves for Hydrologic Design in Snow‐Dominated Environments

There is a renewed focus on the design of infrastructure resilient to extreme hydrometeorological events. While precipitation-based intensity-duration-frequency (IDF) curves are commonly used as part of infrastructure design, a large percentage of peak runoff events in snow-dominated regions are caused by snowmelt, particularly during rain-on-snow (ROS) events. In these regions, precipitation-based IDF curves may lead to substantial overestimation/underestimation of design basis events and subsequent overdesign/underdesign of infrastructure. To overcome this deficiency, we proposed next-generation IDF (NG-IDF) curves, which characterize the actual water reaching the land surface. We compared NG-IDF curves to standard precipitation-based IDF curves for estimates of extreme events at 376 Snowpack Telemetry (SNOTEL) stations across the western United States that each had at least 30 years of high-quality records. We found standard precipitation-based IDF curves at 45% of the stations were subject to underdesign, many with significant underestimation of 100 year extreme events, for which the precipitation-based IDF curves can underestimate water potentially available for runoff by as much as 125% due to snowmelt and ROS events. The regions with the greatest potential for underdesign were in the Pacific Northwest, the Sierra Nevada Mountains, and the Middle and Southern Rockies. We also found the potential for overdesign at 20% of the stations, primarily in the Middle Rockies and Arizona mountains. These results demonstrate the need to consider snow processes in the development of IDF curves, and they suggest use of the more robust NG-IDF curves for hydrologic design in snow-dominated environments. Plain Language Summary Recent natural disasters highlight the need for proper hydrologic design of infrastructure to accommodate extreme flood events. Hydraulic structures such as flood drainage systems are typically designed to convey a storm of a given duration and frequency of occurrence (e.g., the 100 year, 24 h storm event). These events are characterized by curves of a given frequency showing the relationship between precipitation intensity and duration (i.e., IDF curves). In locations with significant snowfall, standard precipitation-based IDF curves fail to capture the snowmelt and rain-on-snow events which may lead to substantial overestimation/underestimation of design basis events used for infrastructure. This study proposed next-generation IDF (NG-IDF) curves to overcome this deficiency. We used observed daily precipitation and changes in snow water equivalent at 376 Snowpack Telemetry (SNOTEL) stations to construct and compare standard precipitation and NG-IDF curves for estimates of extreme events across the western United States. Standard precipitation-based IDF curves were subject to underdesign at 45% of the stations in the Pacific Northwest, the Sierra Nevada Mountains, and the Middle and Southern Rockies. Underestimation of 100 year, 24 h events can be as much as 125%. These results suggest use of the more robust NG-IDF curves for hydrologic design in snow-dominated environments.

A look-ahead probabilistic contingency analysis framework incorporating smart sampling techniques

This paper describes a framework of incorporating smart sampling techniques in a probabilistic look-ahead contingency analysis application. The predictive probabilistic contingency analysis helps describe the impact of uncertainties caused by variable generation and load on potential violations of transmission limits. The objectives of smart sampling techniques are to represent structure and statistical characteristics of different sources of uncertainty in the power system (e.g., load, wind, and solar generation) efficiently and accurately, and to significantly reduce the data set size and the computational time needed for multiple look-ahead contingency analyses. Case studies on the Alstom test system are presented to demonstrate the performance of the framework. The efficiency of the smart sampling techniques is also discussed.