Bounds for Viscoelastic Properties of Heavy-oil Rocks

Heavy oils exhibit viscoelastic behaviour which is strongly frequency- and temperature- dependent. Due to the viscoelastic rheology of heavy oils the common elastic bounding methods such as Hashin-Shtrikman bounds are not rigorous for heavy-oil rocks. In this paper, we demonstrate that viscoelastic bounds of Milton and Berryman for the effective shear modulus of a two phase three-dimensional isotropic composite can be used as rigorous bounds for heavy-oil rocks. The viscoelastic bounds provide an effective tool for testing laboratory measurements and theoretical predictions for heavy-oil rocks

Rigorous bounds for seismic dispersion and attenuation due to wave-induced fluid flow in porous rocks

The Hashin-Shtrikman (HS) bounds define the range of bulk and shear moduli of an elastic composite, given the moduli of the constituents and their volume fractions. Recently, the HS bounds have been ex tended to the quasi-static moduli of composite viscoelastic media. Because viscoelastic moduli are complex, the viscoelastic bounds form a closed curve on the complex plane. We analyze these general viscoelastic bounds for a particular case of a porous solid saturated with a Newtonian fluid. In our analysis, for poroelastic media, the viscoelastic bounds for the bulk modulus are represented by a semicircle and a segment of the real axis, connecting formal HS bounds that are computed for an inviscid fluid. Importantly, viscoelastic bounds for poroelastic media turn out to be independent of frequency. However, because the bounds are quasi-static, the frequency must be much lower than Biot’s characteristic frequency. Furthermore, we find that the bounds for the bulk modulus are attainable (realizable). We also find that these viscoelastic bounds account for viscous shear relaxation and squirt-flow dispersion, but do not account for Biot’s global flow dispersion, because the latter strongly depends on inertial forces

Predicting sea level variations with artificial neural networks at Hillarys Boat Harbour, Western Australia

In the present study, the artificial intelligence meshless methodology of neural networks was used to predict hourly sea level variations for the following 24 hours, as well as for half-daily, daily, 5-daily and 10-daily mean sea levels. The methodology is site specific; therefore, as an example, the measurements from a single tide gauge at Hillarys Boat Harbour, Western Australia, for the period December 1991-December 2002 were used to train and to validate the employed neural networks. The results obtained show the feasibility of the neural sea level forecasts in terms of the correlation coefficient (0.7-0.9), root mean square error (about 10% of tidal range) and scatter index (0.1-0.2)

The use of artificial neural networks to retrieve sea-level information from remote data sources

The knowledge of near-shore sea-level variations is of great importance in applications such as ocean engineering and safe navigation. It also plays an essential role in the practical realisation of the height reference surface in geodesy. In the cases of gaps in tide-gauge records, estimates can be obtained by various methods of interpolation and/or extrapolation, which generally assume linearity of the data. Although plausible in many cases, this assumption does not provide accurate results because shallow-water oceanic processes, such as tides, are mostly of a non-linear nature. This paper employs artificial neural networks to supplement hourly tide-gauge records using observations from other distant tide gauges. A case study is presented using data from the SEAFRAME tide-gauge sta-tions at Hillarys Boat Harbour, Indian Ocean, and Esperance, Southern Ocean, for the period 1992 to 2002. The neural network methodology of sea-level supplementation demonstrates reliable results, with a fairly good overall agreement between the retrieved information and actual measurements

Fluid substitution in porous rocks with aligned cracks: Theory versus numerical modeling

The effect of penny-shaped cracks on the elastic properties of porous media is modeled using static finite element modeling (FEM) code. Anisotropic Gassmann theory is used to predict the effective properties of the saturated cracked media from their dry properties. There is an excellent agreement between numerical results and theory, with a small error associated with partially inequilibrated patches of fluid in the FEM. These patches of fluid result in a residual stiffness which can be subtracted from the FEM results to further improve agreement with Gassmann theory

Artificial neural networks in wave predictions at the west coast of Portugal

In coastal and open ocean human activities, there is an increasing demand for accurate estimates of future sea state. In these activities, predictions of wave heights and periods are of particular importance. In this study, two different neural network strategies were employed to forecast significant wave heights and zero-up-crossing wave periods 3, 6, 12 and 24 h in advance. In the first approach, eight simple separate neural nets were implemented to simulate every wave parameter over each prediction interval. In the second approach, only two networks provided simultaneous forecasts of these wave parameters for the four prediction intervals. Two independent sets of measurements from a directional wave buoy moored off the Portuguese west coast were used to train and to validate the artificial neural nets. Saliency analysis of the results permitted an optimization of the networks' architectures. The optimal learning algorithm for each case was also determined. The short-term forecasts of the wave parameters verified by actual observations demonstrate the suitability of the artificial neural technique

Fluid substitution in heavy oil rocks

Copyright © (2008) by the Society of Exploration Geophysicists All rights reserved. Heavy oils are defined as having high densities and extremely high viscosities. Due to their viscoelastic behavior the traditional rock physics based on Gassmann theory becomes inapplicable. In this paper, we use effective-medium approach known as coherent potential approximation or CPA as an alternative fluid substitution scheme for rocks saturated with viscoelastic fluids. Such rocks are modelled as solids with elliptical fluid inclusions when fluid concentration is small and as suspensions of solid particles in the fluid when the solid concentration is small. This approach is consistent with concepts of percolation and critical porosity, and allows one to model both sandstones and unconsolidated sands. We test the approach against known solutions. First, we apply CPA to fluid-solid mixtures and compare the obtained estimates with Gassmann results. Second, we compare CPA predictions for solid-solid mixtures with numerical simulations. Good match between the results confirms the applicability of the CPA scheme. We extend the scheme to predict the effective frequency- and temperature-dependent properties of heavy oil rocks. CPA scheme reproduces frequency-dependent attenuation and dispersion which are qualitatively consistent with laboratory measurements and numerical simulations. This confirms that the proposed scheme provides realistic estimates of the properties of rocks saturated with heavy oil

Ultrasonic moduli for fluid-saturated rocks: Mavko-Jizba relations rederived and generalized

Mavko and Jizba propose a quantitative model for squirt dispersion of elastic-wave velocities between seismic and ultrasonic frequencies in granular rocks. Their central results are the expressions for the so-called unrelaxed frame bulk and shear moduli computed under an assumption that the stiff pores are drained (or dry) but the soft pores are filled with fluid. Mavko-Jizba expressions are limited to liquid-saturated rocks but become inaccurate when the fluid-bulk modulus is small (e.g., for gas-saturated rocks). We have derived new expressions for unrelaxed moduli of fluid-saturated porous rocks using Sayers-Kachanov discontinuity formalism. The derived expressions generalize the established Mavko-Jizba relations to gas-saturated rocks, reduce to Mavko-Jizba results when the pore fluid is liquid, and yield dry moduli when fluid-bulk modulus tends to zero. We tested this by comparing our model and the model of Mavko and Jizba against laboratory measurements on a sample of Westerly granite

Using Artificial Neural Networks to estimate sea level in continental and island coastal environments

The knowledge of sea level variations is of great importance in geoenvironmental and ocean-engineering applications. Estimations of sea level change with different warning times are of vital importance for the population of low-lying regions and islands. This contribution describes some recent advances in the application of a meshless artificial intelligence technique (neural networks) to the tasks of sea level retrieval and forecast. This technique was employed because it has been proven to approximate the non-linear behaviour in a geophysical system. The data used were taken from several SEAFRAME stations, which provide records for the Australian Baseline Sea Level Monitoring Project. A feed-forward, three-layered, artificial neural network was implemented to retrieve and predict sea level variations with different lead times. This methodology demonstrated reliable results in terms of the correlation coefficient (0.82-0.96), root mean square error (about 10% of tidal range) and scatter index (0.1-0.2), when compared with actual observations

Filling gaps in wave records with artificial neural networks

This contribution presents a neural data interpolation methodology, which was implemented to restore missing wave measurements. The methodology is based on the ability of artificial neural networks to find and reproduce non-linear dependencies within complex geophysical systems. The data were obtained from a field campaign during July 1985- ecember 1993 near Tasmania. Wave observations from a "Waverider" buoy were broadcasted as a high frequency radio signal via a quarter-wave antenna to a "Diwar" receiver. These measurements were used to train and to validate the neural nets employed. To restore missing data over time periods from 12 to 36 hours, five feed-forward, three-layered, artificial neural networks of a similar structure were implemented. The artificial neural networks' performance was estimated in terms of the bias, root mean square error, correlation coefficient, and scatter index. The methodology demonstrated reliable results with a fairly good overall agreement between the restored wave records and actual measurements