In this study, Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR) was evaluated for the assessment of long-term drought monitoring in Huaihe River Basin using daily gauge observation data for the period from 1983 to 2017. The evaluation results show that the PERSIANN-CDR algorithm has a good detection ability for small precipitation events over the whole basin, but a poor ability for extreme precipitation events (>50 mm/day). Daily PERSIANN-CDR estimates perform relatively better in areas with abundant precipitation, while the monthly and yearly PERSIANN-CDR estimates are highly consistent with gauge observations both in magnitude and space. The Standardized Precipitation Index (SPI) at various time scales (3, 6, and 12 months) was calculated based on PERSIANN-CDR and gauge observation, respectively. Grid-based values of statistics derived from those SPI values demonstrate that PERSIANN-CDR has a good ability to capture drought events of each time scale across the basin. However, caution should be applied when using PERSIANN-CDR estimates for basin-scale drought trend analysis. Furthermore, three drought events with long duration and large extent were selected to test the applicability of PERSIANN-CDR in drought monitoring. The results show that it has a good ability to capture when and where droughts occur and how far they spread. Due to the overestimation of small precipitation events, PERSIANN-CDR tends to overestimate the number of extreme droughts and their extents. This needs to be considered in future algorithm improvement.
The pulse fracturing is widely used in unconventional reservoirs. It alternately pulse pumping the proppant slurry and clean fluid to form discontinuous placement proppant pillars in the artificial fractures and the pulse fracture conductivity is several orders of magnitude higher than conventional hydraulic fracture conductivity. However, the understanding of the deformation law of proppant pillar under the action of closure pressure and proppant normal stress is unclear, resulting in difficult to calculate the fracture conductivity and prefer proppant.
Firstly, replacement construction and experimental displacement by Renault Similarity Criteria, three typical proppant pillars placement structures are extracted through the large-scale visualized flat plate device. The Young's modulus of the proppant pillars are calculated in modified API conductivity cell. Secondly, proppant pillars are dispersed into particles by the Smooth Particle Method (SPH). Using the parameters obtain from the above experiments, fracture-proppant pillar contact models are established to simulate the deformation process of proppant pillar and get normal stress of proppant particles. Thirdly, extracting the shape of stabilized proppant pillars, establish the fracture-proppant pillar flow model, calculate the fracture conductivity in different closure pressure.
The simulation results show that as the closure pressure increases from 14MPa to 41MPa, the fracture width present an accelerated downward trend, The fracture width under the support of the initial radius of 9 mm proppant pillars are the largest, decreasing from 2.52mm to 1.72mm, the larger the radius of the proppant pillar, the greater the fracture width, the normal stress of three types of proppant pillar particles are both changed from 73MPa to 110MPa. The elliptical cylinder proppant pillar has the largest fracture conductivity. Its fracture conductivity is reduced from 12500D•cm to 3630D•cm. The larger the construction displacement and the pulse time of proppant slurry, the greater the fracture conductivity.
The model in this article can calculate the normal stress of proppant particle and fracture conductivity in different closure pressure, which can significantly guide the choice of construction parameters and the type of proppant.
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