Geostatistical traveltime tomography in elliptically anisotropic media

In geological materials, anisotropy may arise due to different mechanisms and can be found at different scales. Neglecting anisotropy in traveltime tomographic reconstruction leads to artefacts that can obscure important subsurface features. In this paper, a geostatistical tomography algorithm to invert cross‐hole traveltime data in elliptically anisotropic media is presented. The advantages of geostatistical tomography are that the solution is regularized by the covariance of the model parameters, that known model parameters can be used as constraints and fitted exactly or within a prescribed variance and that stochastic simulations can be performed to appraise the variability of the solution space. The benefits of the algorithm to image anisotropic media are illustrated by two examples using synthetic georadar data and real seismic data. The first example confirms suspected electromagnetic anisotropy in the vadose zone caused by relatively rapid water content variations with respect to wavelength at georadar frequencies. The second presents how sonic log data can be used to constrain the inversion of cross‐well seismic data and how geostatistical simulations can be used to infer parameter uncertainty. Results of both examples show that considering anisotropy yields a better fit to the data at high ray angles and reduces reconstruction artefacts.     

Contrasting weathering and climate regimes in forested and cleared sandstone temples of the Angkor region

Comparative assessment of stone weathering intensities and bioclimatic conditions was conducted at four temples located in cleared and forested sites of the Angkor Park, based on similar protocols. Four thousand sculpted lotus petals carved in the same grey sandstone were categorized by using two customized scales of weathering intensity, and climate monitoring was conducted from December 2008 to November 2009. Whereas 70% of the sandstone lotus petals are almost completely destroyed by mechanical weathering in cleared areas, 74% of petals located in forested environments appear to be totally free of mechanical weathering and are only affected by superficial biochemical weathering. Ambient conditions are also contrasting, with the magnitude of the diurnal surface temperature and relative humidity ranges being three times higher at cleared sites than in wooded areas. As wetting–drying cycles are the driving force of sandstone decay at Angkor, causal links are suggested between weathering and climate regimes. In wooded areas, the microclimate is buffered by the forest and the associated lithobionts, which maintain constant humidity levels, reduce thermal stresses at the stone surface and induce a slow biochemical weathering regime. In cleared areas, direct exposure to sunshine and monsoon rains induces pronounced wetting–drying cycles conducive to swelling–shrinking movements and other potential processes, provoking the rapid mechanical decay of the sandstone. Even if local damage can be caused by tree roots, the forest cover and the associated lithobionts obviously play an overall protective role. Additionally, microtopographical factors related to architectural designs and post‐building events probably explain intra‐site and between site minor differences in the amount of sandstone decay, by influencing key factors such as the water residence time at the stone surface. Last, the contrasting weathering regimes in forested and cleared sites are but a trend, for besides overwhelming mechanical weathering, chemical weathering is also operative at cleared sites, as indicated by salt efflorescences and ferric oxidation. Copyright © 2012 John Wiley & Sons, Ltd.     

Evidence that whales (Balaenoptera borealis) visit drifting fish aggregating devices: do their presence affect the processes underlying fish aggregation?

Evidence of the presence of a group of sei whales (Balaenoptera borealis) detected around drifting fish aggregating devices (FADs) was provided by omnidirectional multi‐beam sonar during a survey off the Seychelles (Indian Ocean). The short visit by the sei whales produced a significant change in the behaviour of the fish assemblage associated with the FAD. There was first a significant increase in fish density when the whales approached the FAD, then a marked decrease after the whales had moved away from the FAD. Furthermore, the fish density was still low, 3 h after the whales had left the FAD. We assume that the presence and behaviour of the sei whales led some of the fish initially associated with the FAD to move away from it. There has been a considerable increase in the use of drifting artificial FADs in the Indian Ocean in recent decades. The frequency of cetacean visits to drifting FADs in the Indian Ocean is unknown, but they may have a major impact on assemblages of pelagic fish species around FADs. The effect of marine mammals on FAD‐associated fish could be relevant to the ecological trap theory (FAD acting as a trap for their associated fish) because of their impact on the dynamics of fish aggregation processes, through commensalism and/or predator–prey interactions.     

Pattern-based conditional simulation with a raster path: a few techniques to make it more efficient

Mass transport is known to depend on heterogeneity in geological formations. This entails geological bodies with complex geometries. The major interest of multiple-point simulation is its ability to reproduce such geological features through the use of a training image. The idea behind the training image is to describe a geological concept with the expected geological architecture. Its structural content is then used to infer multiple-point statistics. This yields a database with a variety of possible patterns or events. In this paper, we present a hybrid algorithm combining geostatistical multiplepoint and texture synthesis techniques for simulating geological reservoir models constrained to hard data. The proposed algorithm is a two steps process, involving first analysis with the building of an organized database from the training image content, and second synthesis with the simulation of a realization. Various tests are performed to investigate the potential of the algorithm in terms of computation time and ability to properly reproduce the shapes and connectivity features of the objects represented in the training image. We also propose a few improvements to make the algorithm more efficient. Last, six examples are presented based upon different kinds of training images depicting large-scale channelized and fractured media as well as fine-scale porous media.     

An Application of Multivariate Simulation in the Cement Industry

The profitability of a cement plant depends largely on its capacity to produce homogeneous cement with chemical composition close to specified targets for the cement type produced. One crucial step is the mixing of limestone with other raw materials in proportions calculated to meet these targets. Major design and operation decisions depend on the efficiency of this homogenizing step. The adequate modeling of the mixing process requires simulation of representative cross-correlated time series of chemical compositions of the raw materials involved. The chemical composition signals are obtained by multivariate geostatistical simulation using an LU (Cholesky) decomposition of the covariance matrix. Modifications to the usual LU method are presented. First, the effect on the raw covariance matrix of the closure property of chemical analysis is imposed. Second, the problem of memory space limitations in the LU method is tackled by using overlapping sliding neighbourhoods. The simulation algorithm is applied to the Joppa cement plant owned by Lafarge North America. The simulated raw material input streams are fed into the quality mix control (QMC), a proprietary software that models and controls the mixing operation to produce an output stream with cement characteristics as close as possible to desired targets. Two signal series are studied, one autocorrelated with a moderate temporal range and one with no autocorrelation. The QMC produces C₃S output signals having comparable short scale periodic variograms except that the variance of the uncorrelated signal is four times greater than those of the autocorrelated signal and the real Joppa data. The raw material feeder variograms have the same sill for both the white noise and the autocorrelated signals. However, the autocorrelated signal feeder variogram presents lower short term dispersion variance, a characteristic feature of Joppa operations. Our results show the importance of simulating the right temporal structure of the raw materials to realistically forecast the behavior of the output signals. We also discuss some practical implications of these findings for the design and operation of a cement plant.     

Borehole radar velocity inversion using cokriging and cosimulation

A new radar velocity tomography method is presented based on slowness covariance modeling and cokriging of the slowness field using only measured travel time data. The proposed approach is compared to the classical LSQR algorithm using various synthetic models and a real data set. In each case, the proposed method provides comparable to or better results than LSQR. One advantage of this approach is that it is self-regularized and requires less a priori information. The covariance model also allows stochastic imaging of slowness fields by geostatistical simulations. Stable characteristics and uncertain features of the inverted models can then be easily identified.     

Historic reconstruction of reservoir topography using contour line interpolation and structure from motion photogrammetry

The geometry of impounded surfaces is a key tool to reservoir storage management and projection. Yet topographic data and bathymetric surveys of average-aged reservoirs may be absent for many regions worldwide. This paper examines the potential of contour line interpolation (TOPO) and Structure from Motion (SfM) photogrammetry to reconstruct the topography of existing reservoirs prior to dam closure. The study centres on the Paso de las Piedras reservoir, Argentina, and assesses the accuracy and reliability of TOPO- and SfM- derived digital elevation models (DEMs) using different grid resolutions. All DEMs were of acceptable quality. However, different interpolation techniques produced different types of error, which increased (or decreased) with increasing (or decreasing) grid resolution as a function of their nature, and relative to the terrain complexity. In terms of DEM reliability to reproduce area–elevation relationships, processing-related disagreements between DEMs were markedly influenced by topography. Even though they produce intrinsic errors, it is concluded that both TOPO and SfM techniques hold great potential to reconstruct the bathymetry of existing reservoirs. For areas exhibiting similar terrain complexity, the implementation of one or another technique will depend ultimately on the need for preserving accurate elevation (TOPO) or topographic detail (SfM).     

Multiscale Parameterization of Petrophysical Properties for Efficient History-Matching

The prediction of fluid flows within hydrocarbon reservoirs requires the characterization of petrophysical properties. Such characterization is performed on the basis of geostatistics and history-matching; in short, a reservoir model is first randomly drawn, and then sequentially adjusted until it reproduces the available dynamic data. Two main concerns typical of the problem under consideration are the heterogeneity of rocks occurring at all scales and the use of data of distinct resolution levels. Therefore, referring to sequential Gaussian simulation, this paper proposes a new stochastic simulation method able to handle several scales for both continuous or discrete random fields. This method adds flexibility to history-matching as it boils down to the multiscale parameterization of reservoir models. In other words, reservoir models can be updated at either coarse or fine scales, or both. Parameterization adapts to the available data; the coarser the scale targeted, the smaller the number of unknown parameters, and the more efficient the history-matching process. This paper focuses on the use of variational optimization techniques driven by the gradual deformation method to vary reservoir models. Other data assimilation methods and perturbation processes could have been envisioned as well. Last, a numerical application case is presented in order to highlight the advantages of the proposed method for conditioning permeability models to dynamic data. For simplicity, we focus on two-scale processes. The coarse scale describes the variations in the trend while the fine scale characterizes local variations around the trend. The relationships between data resolution and parameterization are investigated.