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We apply an improved potential flow model based on conformal mapping to study the sweep pattern of waterfloods in bounded reservoirs.
Such an approach can model waterflooding in marginal fields like the Quitman oil field more accurately than previous potential flow methods and can visualize the sweep pattern and compute time-of-flight contours in a simpler and faster fashion than numerical streamline simulators.
This is due to degradation of the sweep pattern by random genetic drift, which we discuss next.
Clonal interference explains and quantifies the observed sweep pattern: we find an average of at least one strongly beneficial amino acid substitution per year, and a given selective sweep has three to four driving mutations on average.
We investigate analytically and numerically how the accuracy of our estimator is affected by the decay of the sweep pattern over time as a consequence of random genetic drift and discuss potential effects of recombination, soft sweeps, and demography.
Our analysis shows that quantitative statistics of the punctuated sweep pattern and of polymorphism time series produces quite specific tests for models for influenza evolution and, in particular, for clonal interference.
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Reservoir heterogeneity controls the injection and sweep patterns in the flood.
Our method is assumed effective in illustrating the value of analytical streamline simulations for first-order assessment of sweep patterns in hydrocarbon field produced with waterflooding.
Sector models are possible to estimate sweep patterns between injector and producer paths; well allocation factors, recovery factors and well productivity rates can be quantified for a particular field situation and studied in detail.
We therefore focused on two genes, each located in two distinct QTL colocalizing with different selective sweep patterns.
When reordering the distance matrix according to this assignment, we recover the two individual sweep patterns as blocks on the diagonal.
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