Automated wavelet-based data compression systems and methods

A computer-implemented method of compressing time-series data, the method comprising:

• (a) receiving a plurality of data points in a time-series at a current tree in a short term storage;
• (b) calculating at least one approximation coefficient based on the plurality of data points using a wavelet transform, including calculating a highest level approximation coefficient;
• (c) calculating an estimated value based on the highest level approximation coefficient;
• (d) calculating differences between the estimated value and the plurality of data points of the short term storage;
• (e) comparing whether a maximum difference among the calculated differences is less than a predetermined threshold;
• and (f) based on the maximum difference being greater than or equal to the predetermined threshold, preparing a compressed data set by storing the oldest data point in the time-series of the short term storage in a long term storage, discarding the oldest half of the data points in the time-series and coefficients from the short term storage, and retaining the newest half of the data points in the short term storage;
• and receiving one or more new data points into a new tree and repeating steps (b)-(e);
• (g) based on the maximum difference being less than the predetermined threshold, determining whether a tree size of the current tree is less than a preset maximum tree size;
• (g)(1) if the tree size of the current tree is less than the preset maximum tree size, receiving one or more new data points into a new tree and repeating steps (b)-(e), and (g)(2) if the tree size of the current tree is equal to the preset maximum tree size, preparing the compressed data set by storing the oldest data point in the time-series of the short term storage in the long term storage and discarding all coefficients and all data points in the current tree in the short term storage;
• and receiving one or more new data points into a new tree and repeating steps (b)-(e).

A system for compressing time-series data generated by a hydrocarbon production facility, the system comprising:

• a processor;
• a memory communicatively connected to the processor, wherein the memory includes a short term storage and a long term storage, and wherein the memory storing computer-executable instructions which, when executed, cause the processor to perform:
• (a) receiving a selection of a data tag associated with a data stream including a plurality of data points in a time-series at a current tree in the short term storage, wherein the data tag is associated with a sensor in a hydrocarbon production facility;
• (b) calculating at least one approximation coefficient based on the plurality of data points using a wavelet transform, including calculating a highest level approximation coefficient;
• (c) calculating an estimated value based on the highest level approximation coefficient;
• (d) calculating differences between the estimated value and the plurality of data points of the short term storage;
• (e) comparing whether a maximum difference among the calculated differences is less than a predetermined threshold, wherein the predetermined threshold is based on accuracy of the sensor;
• and (f) based on the maximum difference being greater than or equal to the predetermined threshold, preparing a compressed data set by storing the oldest data point in the time-series of the short terra storage in the long terra storage, discarding the oldest half of the data points in the time-series and coefficients from the short term storage, and retaining the newest half of the data points in the short term storage;
• and receiving one or more new data points into a new tree and repeating steps (b)-(e);
• (g) based on the maximum difference being less than the predetermined threshold, determining whether a tree size of the current tree is less than a preset maximum tree size;
• (g)(1) if the tree size of the current tree is less than the preset maximum tree size, receiving one or more new data points into a new tree and repeating steps (b)-(e), and (g)(2) if the tree size of the current tree is equal to the preset maximum tree size, preparing the compressed data set by storing the oldest data point in the time-series of the short term storage in the long term storage and discarding all coefficients and all data points in the current tree in the short term storage;
• and receiving one or more new data points into a new tree and repeating steps (b)-(e).