In petroleum reservoirs, parameters such as permeability, porosity, extension, and skin effects, have a high level of uncertainty. Different techniques attempt to improve the estimation of these parameters to improve production forecasting and cost reduction. The conventional production test, for example, requires the analysis of reservoir well pressure data. However, these data have several noise sources, such as the tidal effect, that may compromise the accuracy of test results. This letter proposes a method to smooth the noise generated by the tidal effect from well pressure data, using mathematical tools, such as the Fourier transform. We apply the discrete sine transform type I (DST-I) to filter the signal, and we use the pressure derivative to verify the results.

Connected component labeling (CCL) is a fundamental image processing problem that has been studied in many platforms, including GPUs. A common approach to CCL performance analysis is studying the total processing time as a function of abstract image features, like the number of connected components or the fraction of foreground pixels, and input data usually includes synthetic images and segmented video datasets. In this paper, we develop on these ideas and propose an evaluation methodology for GPU CCL algorithms based on synthetic image patterns, addressing the nonexistence of a standard and reliable benchmark in the literature. Our methodology, applied on two important algorithms from existing literature, uncovers their data dependency with great detail, and allows us to model their processing time in three real-world video data sets as functions of abstract, high-level image concepts. We also apply our methodology for studying the memory and performance requirements of two strategies for computing connected component properties: an existing memory-hungry approach, and a new memory-preserving strategy.