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.     

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.     

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.