14,153 research outputs found
Data-Space Inversion with Ensemble Smoother
Reservoir engineers use large-scale numerical models to predict the
production performance in oil and gas fields. However, these models are
constructed based on scarce and often inaccurate data, making their predictions
highly uncertain. On the other hand, measurements of pressure and flow rates
are constantly collected during the operation of the field. The assimilation of
these data into the reservoir models (history matching) helps to mitigate
uncertainty and improve their predictive capacity. History matching is a
nonlinear inverse problem, which is typically handled using optimization and
Monte Carlo methods. In practice, however, generating a set of properly
history-matched models that preserve the geological realism is very
challenging, especially in cases with complicated prior description, such as
models with fractures and complex facies distributions. Recently, a new
data-space inversion (DSI) approach was introduced in the literature as an
alternative to the model-space inversion used in history matching. The
essential idea is to update directly the predictions from a prior ensemble of
models to account for the observed production history without updating the
corresponding models. The present paper introduces a DSI implementation based
on the use of an iterative ensemble smoother and demonstrates with examples
that the new implementation is computationally faster and more robust than the
earlier method based on principal component analysis. The new DSI is also
applied to estimate the production forecast in a real field with long
production history and a large number of wells. For this field problem, the new
DSI obtained forecasts comparable with a more traditional ensemble-based
history matching.Comment: 33 pages, 14 figure
Holographic particle localization under multiple scattering
We introduce a novel framework that incorporates multiple scattering for
large-scale 3D particle-localization using single-shot in-line holography.
Traditional holographic techniques rely on single-scattering models which
become inaccurate under high particle-density. We demonstrate that by
exploiting multiple-scattering, localization is significantly improved. Both
forward and back-scattering are computed by our method under a tractable
recursive framework, in which each recursion estimates the next higher-order
field within the volume. The inverse scattering is presented as a nonlinear
optimization that promotes sparsity, and can be implemented efficiently. We
experimentally reconstruct 100 million object voxels from a single 1-megapixel
hologram. Our work promises utilization of multiple scattering for versatile
large-scale applications
Gravitational waves: search results, data analysis and parameter estimation
The Amaldi 10 Parallel Session C2 on gravitational wave (GW) search results, data analysis and parameter estimation included three lively sessions of lectures by 13 presenters, and 34 posters. The talks and posters covered a huge range of material, including results and analysis techniques for ground-based GW detectors, targeting anticipated signals from different astrophysical sources: compact binary inspiral, merger and ringdown; GW bursts from intermediate mass binary black hole mergers, cosmic string cusps, core-collapse supernovae, and other unmodeled sources; continuous waves from spinning neutron stars; and a stochastic GW background. There was considerable emphasis on Bayesian techniques for estimating the parameters of coalescing compact binary systems from the gravitational waveforms extracted from the data from the advanced detector network. This included methods to distinguish deviations of the signals from what is expected in the context of General Relativity
Computational characterization and prediction of metal-organic framework properties
In this introductory review, we give an overview of the computational
chemistry methods commonly used in the field of metal-organic frameworks
(MOFs), to describe or predict the structures themselves and characterize their
various properties, either at the quantum chemical level or through classical
molecular simulation. We discuss the methods for the prediction of crystal
structures, geometrical properties and large-scale screening of hypothetical
MOFs, as well as their thermal and mechanical properties. A separate section
deals with the simulation of adsorption of fluids and fluid mixtures in MOFs
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