ALBERT

Description: Regridding geological models to a higher resolution for flow simulation is an important problem in geostatistical modeling. For practical reasons, over a large area, models can only be built at a relatively coarse resolution. Subsequently, the resolution of specified regions of interest must be increased before upscaling for flow modeling. The construction of a high-resolution model of the entire reservoir at the beginning of the evaluation may be impractical because of computational and time constraints. It is standard practice to implement nearest neighbor interpolation to increase the resolution of models. Although it is a simple practical solution, nearest neighbor interpolation introduces spatial continuity artifacts that are often unrealistic. This paper proposes an automatic stochastic regridding approach based on simulation. The simulation is conditioned to the initial coarse resolution model/realization. The process includes the extraction of specified regions of interest, definition of corresponding local variography, and implementation of Sequential Gaussian Simulation (SGS) and/or Sequential Indicator Simulation (SIS) to characterize continuous and categorical variables, respectively. In each specified region, the local variography can be defined by either implementing automatic fitting algorithms or assigning the global variography initially used to build the coarse resolution model. The regridding process is automated. The advantage of this approach over the conventional nearest neighbor interpolation is in the improvement in the realistic spatial variability features of small scale geologic heterogeneity. The benefits of obtaining a proper regridded model are discussed in a case study of a fluvial reservoir in the McMurray formation. One of the main reasons for generating high resolution models is in the appropriate characterization of small scale impermeable geobodies such as remnant shales. The coarse resolution models are not able to properly characterize the small scale geologic features of the shales; more amount of information is required to characterize smaller scale features. The metric of performance considered is the effective vertical permeability. The automated stochastic regridding workflow described in this paper is available on a Fortran platform with additional scripting which will be distributed upon request. Note that the terms "regridding" and "stochastic regridding" are used interchangeably and both refer to the proposed workflow of modeling at higher resolution.

Description: Persistent adaptive challenges are often met with the evolution of novel physiological traits. Although there are specific examples of single genes providing new physiological functions, studies on the origin of complex organ functions are lacking. One such derived set of complex functions is found in the Lepidopteran bursa copulatrix, an organ within the female reproductive tract that digests nutrients from the male ejaculate or spermatophore. Here, we characterized bursa physiology and the evolutionary mechanisms by which it was equipped with digestive and absorptive functionality. By studying the transcriptome of the bursa and eight other tissues, we revealed a suite of highly expressed and secreted gene products providing the bursa with a combination of stomach-like traits for mechanical and enzymatic digestion of the male spermatophore. By subsequently placing these bursa genes in an evolutionary framework, we found that the vast majority of their novel digestive functions were co-opted by borrowing genes that continue to be expressed in nonreproductive tissues. However, a number of bursa-specific genes have also arisen, some of which represent unique gene families restricted to Lepidoptera and may provide novel bursa-specific functions. This pattern of promiscuous gene borrowing and relatively infrequent evolution of tissue-specific duplicates stands in contrast to studies of the evolution of novelty via single gene co-option. Our results suggest that the evolution of complex organ-level phenotypes may often be enabled (and subsequently constrained) by changes in tissue specificity that allow expression of existing genes in novel contexts, such as reproduction. The extent to which the selective pressures encountered in these novel roles require resolution via duplication and sub/neofunctionalization is likely to be determined by the need for specialized reproductive functionality. Thus, complex physiological phenotypes such as that found in the bursa offer important opportunities for understanding the relative role of pleiotropy and specialization in adaptive evolution.