## Lillian Pierce : Carleson operators of Radon type

- Other Meetings and Events ( 38 Views )A celebrated theorem of Carleson shows that the Fourier series of an L^2 function converges pointwise almost everywhere. At the heart of this work lies an L^2 estimate for a particular type of maximal singular integral operator, which has since become known as a Carleson operator. In the past 40 years, a number of important results have been proved for generalizations of the original Carleson operator. In this talk we will introduce the Carleson operator and survey its generalizations, and then describe new joint work with Po Lam Yung on Carleson operators with a certain type of polynomial phase that also incorporate the behavior of Radon transforms.

## Allan Seheult : Bayesian Forecasting and Calibration for Complex Phenomena Using Multi-level Computer Codes

- Other Meetings and Events ( 36 Views )We describe a general Bayesian approach for using computer codes for a complex physical system to assist in forecasting actual system outcomes. Our approach is based on expert judgements and experiments on fast versions of the computer code. These are combined to construct models for the relationships between the code's inputs and outputs, respecting the natural space/time features of the physical system. The resulting beliefs are systematically updated as we make evaluations of the code for varying input sets and calibrate the input space against past data on the system. The updated beliefs are then used to construct forecasts for future system outcomes. While the approach is quite general, it has been developed particularly to handle problems with high-dimensional input and output spaces, for which each run of the computer code is expensive. The methodology will be applied to problems in uncertainty analysis for hydrocarbon reservoirs.

## Max Morris : Design and Analysis for an Inverse Problem Arising From an Advection-Dispersion Process

- Other Meetings and Events ( 35 Views )We consider a process of one-dimensional fluid flow through a soil packed tube in which a contaminant is initially distributed. The contaminant concentration, as a function of location in the tube and time after flushing begins, is classically modeled as the solution of a linear second order partial differential equation. Here, we consider the related issues of how contaminant concentration measured at some location-time combinations can be used to approximate concentration at other locations and times (ie., exprimental design). The method is demonstrated for the case in which initial concentrations are approximated based on data collected only at the downstream end of the tube. Finally, the effect of misspecifying one of the model parameters is discussed, and alternative designs are developed for instances in which that parameter must be estimated from the data.

## Zhuoxin Bi : tracer flow in random porous media

- Other Meetings and Events ( 28 Views )I will explain some basic concepts for fluid flow in porous media, go through some details about the 3D streamline simulator and SGSIM (Sequential Gaussian SIMulation) for generating random permeability fields under multiGaussian assumption.

## Dean Oliver : Sampling the Posterior Distribution for Reservoir Properties Conditional to Production Data

- Other Meetings and Events ( 28 Views )A major problem of Petroleum engineering si the prediction of future oil and water production from a reservoir whose properties are inferred from measurements along well paths, and from observations of pressure, production, and fluid saturations at well locations. If the properties of the porous material were known at all locations, and all boundary conditions were specified, the production rates of fluids would be computed from the numerical solution of a set of partial differential equations governing mass conservation and flow. Rock properties are known to be heterogeneous on many scales, however, and the measurements are always insufficient to determine the properties throughout the reservoir. In the petroleum and groundwater fields, rock properties (permeability and porosity) are modeled as spatial random fields, whose auto-covariance and cross-covariances are known from ovservations of outcrops and cores. Uncertainty in future production is characterized by the empirical distribution from the suite of realizations of rock properties. The problem is assessing uncertainty in reservoir production or groundwater remediation predictions is that while valid prodecures for sampling the posterior pdf are available, the computational cost of generating the necessary number of samples from such procedures is prohibitive. An increase in computer speed is unlikely to solve this problem as the trend has been to build more complex numerical models of the reservoir as computer capability increases. Most recent effort has gone in to approximate methods of sampling. In this talk, I will describe our experience with the use of Markov Chain Monte Carlo methods and with approximate sampling methods.