Michael E. Glinsky

High‐resolution numerical simulations of resuspending gravity currents: Conditions for self‐sustainment

Received 21 February 2005; revised 30 June 2005; accepted 29 August 2005; published 22 December 2005. [1] We introduce a computational model for high-resolution simulations of particle-laden gravity currents. The features of the computational model are described in detail, and validation data are discussed. Physical results are presented that focus on the influence of particle entrainment from the underlying bed. As turbulent motions detach particles from the bottom surface, resuspended particles entrained over the entire length of the current are transferred to the current’s head, causing it to become denser and potentially accelerating the front of the current. The conditions under which turbidity currents may become self-sustaining through particle entrainment are investigated as a function of slope angle, current and particle size, and particle concentration. The effect of computational domain size and initial aspect ratio of the current on the evolution of the current are also considered. Applications to flows traveling over a surface of varying slope angle, such as turbidity currents spreading down the continental slope, are modeled via a spatially varying gravity vector. Particular attention is given to the resulting particle deposits and erosion patterns.

Delivery: an open-source model-based Bayesian seismic inversion program

We introduce a new open-source toolkit for model-based Bayesian seismic inversion called Delivery. The prior model in Delivery is a trace-local layer stack, with rock physics information taken from log analysis and layer times initialised from picks. We allow for uncertainty in both the fluid type and saturation in reservoir layers: variation in seismic responses due to fluid effects are taken into account via Gassmans equation. Multiple stacks are supported, so the software implicitly performs a full AVO inversion using approximate Zoeppritz equations. The likelihood function is formed from a convolutional model with specified wavelet(s) and noise level(s). Uncertainties and irresolvabilities in the inverted models are captured by the generation of multiple stochastic models from the Bayesian posterior (using Markov Chain Monte Carlo methods), all of which acceptably match the seismic data, log data, and rough initial picks of the horizons. Post-inversion analysis of the inverted stochastic models then facilitates the answering of commercially useful questions, e.g. the probability of hydrocarbons, the expected reservoir volume and its uncertainty, and the distribution of net sand. Delivery is written in java, and thus platform independent, but the SU data backbone makes the inversion particularly suited to Unix/Linux environments and cluster systems.

Detection of reservoir quality using Bayesian seismic inversion

Sorting is a useful predictor for permeability. We show how to invert seismic data for a permeable rock sorting parameter by incorporating a probabilistic rock-physics model with floating grains into a Bayesian seismic inversion code that operates directly on rock-physics variables. The Bayesian prior embeds the coupling between elastic properties, porosity, and the floating-grain sorting parameter.The inversion uses likelihoods based on seismic amplitudes and a forwardconvolutionalmodeltogenerateaposteriordistribution containing refined estimates of the floating-grain parameter anditsuncertainty.Theposteriordistributioniscomputedusing Markov Chain Monte Carlo methods. The test cases we examineshowthatsignificantinformationaboutbothsorting characteristics and porosity is available from this inversion, even in difficult cases where the contrasts with the bounding lithologies are not strong, provided the signal-to-noise ratio S/N of the data is favorable. These test cases show about 25% and 15% improvements in estimated standard deviationsforporosityandfloating-grainfraction,respectively,for peak S/N of6:1.The full posterior distribution offloatinggraincontentismoreinformative,andshowsenhancedseparationintotwoclustersofcleanandpoorlysortedrocks.This holds true even in the more difficult test case we examine, wherenotably,thelaminatedreservoirnet-to-grossisnotsignificantlyimprovedbytheinversionprocess.

An extended Rayleigh model of bubble evolution

An extended Rayleigh model for laser generated bubbles in water and soft tissue is presented. This model includes surface tension, viscosity, a realistic equation of state, material strength and failure, stress wave emission, and linear growth of interface instabilities. The model is validated by comparison to detailed compressible hydrodynamic simulations using the LATIS computer program. The purpose of this study is to investigate the use of the extended Rayleigh model as a much faster and simpler substitute for the detailed hydrodynamic simulations when only limited information is needed. It is also meant to benchmark the hydrosimulations and highlight the relevant physics. The extended Rayleigh model and the hydrosimulations are compared using both a 1D spherical geometry with a bubble in the center and a 2D cylindrical geometry of a laser fiber immersed in water with a bubble formed at the end of the fiber. Studies are done to test the validity of the material strength and failure, stress wave emissi...

Automatic event picking in prestack migrated gathers using a probabilistic neural network

We describe algorithms for automating the process of picking seismic events in prestack migrated common depth image gathers. The approach uses supervised learning and statistical classification algorithms along with advanced signal/image processing algorithms. No model assumption is made, such as hyperbolic moveout. We train a probabilistic neural network for voxel classification using event times, subsurface points, and offsets (ground truth information) picked manually by expert interpreters. The key to success is using effective features that capture the important behavior of the measured signals. We test a variety of features calculated in a local neighborhood about the voxel under analysis. Selection algorithms ensure that we use only the features that maximize class separability. This event-picking algorithm has the potential to reduce significantly the cycle time and cost of 3-D prestack depth migration while making the velocity model inversion more robust.

Two-dimensional Rayleigh model for bubble evolution in soft tissue

The understanding of vapor bubble generation in a soft tissue near a fiber-optic tip has in the past required two-dimensional (2D) hydrodynamic simulations. For 1D spherical bubble expansions a simplified and useful Rayleigh-type model can be applied. For 2D bubble evolution, such a model has not been developed. In this work we develop a Rayleigh-type model for 2D bubble expansion that is much faster and simpler than 2D hydrodynamic simulations and can be applied toward the design and understanding of fiber-based medical therapies. The model is based on a flow potential representation of the hydrodynamic motion and is described by a Laplace equation with a moving boundary condition at the bubble surface. In order for the Rayleigh-type 2D model to approximate bubble evolution in soft tissue, we include viscosity and surface tension in the fluid description. We show that the 1D Rayleigh equation is a special case of our model. The Laplace equation is solved for each time step by a finite-element solver usin...

A model for variation of velocity versus density trends in porous sedimentary rocks

A rock physics model appropriate for porous media in which some of the solid material is “floating” or not involved in load support is developed that explains observed variation in compressional wave velocity versus density trends. This same model predicts no significant change in the shear versus compressional wave velocity trend, as is also observed. These floating grains are correlated with poor sorting of the matrix grains as expected. The presence of the floating grains is found to correlate with a decrease in the permeability of the rock and therefore the viability of potential petroleum reservoirs. Shifts in the velocity versus density relationships can be determined by wireline log but not directly by seismic reflectivity measurements. However, the introduction of an additional concept, the capture fraction of smaller grains, adds another constraint to the model which enables remote sensing of the viability of certain petroleum reservoirs by seismic reflectivity measurements alone. Experimental ev...

Geologic lithofacies identification using the multiscale character of seismic reflections

A forward acoustic model shows that geologic lithofacies groups can be identified by the character of the wavelet transform of their seismic reflection response even for incident signals with a wavelength much larger than the dominant bed thickness. The same model shows that multiple interbed reflections can be neglected. This allows the use of an analytical relation of the linear reflection response expressed as a convolution between the incident signal and the scaled derivative of the acoustic impedance. The relation is applied to solve the inverse problem for the acoustic impedance, using orthogonal discrete wavelet transform (DWT) and Fourier transform methods; good agreement is obtained between the well log waveletspectrum and both the forward modeled seismic data and the real seismic data. It is found that the DWT approach is superior, having a better signal-to-noise ratio and more localized deconvolution artifacts. A population of well logs containing a wide range of lithologies and bed thicknesses, which are categorized into lithofacies groups, is used to define the conditional probability of a wavelet transform response given a lithofacies group. These conditional probabilities are used to estimate the lithofacies probability given a seismicwavelet response via a Bayesian inversion.

Self-consistent coupling of cavitation bubbles in aqueous systems

The dynamics of an ensemble of cavitation voids initiated by laser-produced stress waves in aqueous systems is considered. Aqueous systems have large similarity to soft tissues. Laser-initiated stress waves are reflected from tissue boundaries, thereby inducing a tensile stress that is responsible for tissue damage. The early stage of damage is represented by an ensemble of voids or bubbles that nucleate and grow around impurities under stress wave tension. For impurity densities larger than 105 cm−3 the bubbles growth reduces the tensile wave component and causes the pressure to oscillate between tension and compression. For impurity densities below 108 cm−3 the bubbles grow on a long time scale (∼10 μs) relative to the wave interaction time (∼100 ns). For bubble densities above 108 cm−3 the bubble lifetime is greatly shortened because of the reduced tensile component. On a long time scale the growing bubbles cause a significant reduction in the liquid average compression pressure below the ambient atmos...

Downscaling Multiple Seismic Inversion Constraints to Fine-Scale Flow Models

This paper (SPE 110771) was accepted for presentation at the SPE Annual Technical Conference and Exhibition, Anaheim, California, 11–14 November 2007, and revised for publication. Original manuscript received for review 2 August 2007. Revised manuscript received for review 19 June 2009. Paper peer approved 16 July 2009. Summary Well data reveal reservoir layering with high vertical resolution but are areally sparse, whereas seismic data have low vertical resolution but are areally dense. Improved reservoir models can be constructed by integrating these data. The proposed method combines stochastic seismic inversion results, finer-scale well data, and geologic continuity models to build ensembles of flow models. Stochastic seismic inversions operating at the mesoscale generate rock property estimates, such as porosity, that are consistent with regional rock physics and true-amplitude imaged seismic data. These can be used in a cascading workflow to generate ensembles of fine-scale reservoir models wherein each realization from the Bayesian seismic inversion is treated as an exact constraint for a subensemble of fine-scale models. Exact constraints ensure that relevant interproperty and interzone correlations implied by rock physics and seismic data are preserved in the downscaled models. Uncertainty in the rock physics and seismic response is included by using multiple stochastic inversions in a cascading workflow. In contrast, inexact constraints generally do not preserve these correlations. We use two-point covariance at the fine scale to provide prior model thickness and porosity distributions of multiple facies. A Bayesian formulation uses the kriged data as the prior with the coarse constraints as the likelihood, and this posterior is sampled using a Markov Chain Monte Carlo (MCMC) method in a sequential simulation framework. These methods generate rich pinchout behavior and flexible spatial connectivities in the fine-scale model. These flow models are easily represented on a cornerpoint grid. 2D examples illustrate the interactions of prior and constraint data, and 3D examples demonstrate algorithm performance and the effects of stratigraphic variability on flow behavior.