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There are two reasons to filter data in WellTest:

1. Memory management: After importing files with millions of data points, data reduction is required to prevent slow, sluggish behaviour and "out of memory" related errors. Although, Data Management is partly a placeholder of the raw data, there is no need to keep millions of data points, and it is more important to free up computer memory for other modules within WellTest. We recommend filtering gauges with millions of points down to 100,000 points immediately after importing. Data reduction for memory management is primarily accomplished by Filtering in Data Management. Although this filter has advanced options, in most cases, a simple arithmetic filter can be used for the entire dataset, and will keep enough resolution for analysis. For additional information, see Handling Large Datasets.

2. Calculation efficiency: When preparing data for analysis, another level of filtering is performed to reduce calculation times. Filtering in the Production Editor drastically reduces convergence times when performing automatic parameter estimation and running numerical simulations. Although 20 points could capture the trend of any flow period, it is impossible to know which 20 points to pick before scrutinizing the data. Therefore, we find a balance between the amount of data we keep to analyze and computational time. For analytical analysis / modeling, it is highly recommended to filter the Production Editor data down to less than 5,000 data points. For numerical modeling, 1,000 points is more than sufficient.