Method and system for interpreting tubing data
An instrument, such as a wall-thickness, rod-wear, or pitting sensor, can monitor tubing as a field service crew extracts the tubing from an oil well or inserts the tubing into the well. A computer-based system can process data from the instrument to evaluate the validity of the data. Validating the data can comprise determining whether any features, structures, or patterns present in the data correlate with actual tubing defects or were caused by a condition unrelated to tubing quality, such as signal noise. The computer-based system can also analyze the data to deduce information about the performance of the well or to determine the well's operating state or status. For example, the data analysis can determine whether the well's fluids have a chemical condition that should be treated or whether a detrimental harmonic oscillation has been occurring in the well's mechanical pumping system.
1. A method for interpreting tubing data comprising;
scanning a plurality of tubing segments with a tubing scanner to produce tubing segment scan data, said scanner comprising a rod-wear sensor;
correlating the segment scan data with positional data obtained from an encoder;
analyzing the tubing segment scan data using pattern recognition software to identify wear patterns in the tubing segments, wherein the pattern recognition software employs Fourier transform processing; and
displaying the tubing segment data.
2. The method of claim 1 wherein the tubing scanner further comprises a pitting sensor.
3. The method of claim 2 wherein the pattern recognition software flags regions having significant pitting without significant rod wear.
4. The method of claim 2 wherein the pattern recognition software flags regions having significant rod wear without significant pitting.
5. The method of claim 1 wherein the pattern recognition software processes the data based upon genetic algorithm, fractal mathematics, artificial intelligence, adaptive filtering, Kalman filtering, least squares analysis, partial least squares analysis, stochastic filtering, statistical pattern recognition, linear algorithm, or linear programming.
6. The method of claim 1 wherein the pattern recognition software flags wear features.
7. The method of claim 1 further comprising determining if a region of rod wear is attributable to stroke length of the sucker rod.
8. The method of claim 1 further comprising flagging the region of rod wear as being erroneous.
9. The method of claim 1 further comprising displaying the data as a function of depth.
10. A method for evaluating a tubing string comprising a plurality of tubing sections at a wellsite comprising a well, comprising the steps of:
moving at least one tubing section into or out of the well;
scanning the tubing section with at least one sensor to receive a plurality of wear data and a plurality of pitting data as at least a portion of the tubing section is being moved into or out of the well;
displaying the wear data and the pitting data on a visual display; and
analyzing the plurality of wear data and pitting data.
11. The method of claim 10 , wherein the step of analyzing the wear data and pitting data comprises:
receiving depth data for the tubing string as the at least one tubing section is being moved into or out of the well;
correlating the wear data with corresponding depth data to comprise a wear log for the tubing string;
correlating the pitting data with corresponding depth data to comprise a pitting log for the tubing string; and
comparing the wear log and the pitting log for the at least one tubing section.
12. The method of claim 11 , further comprising the steps of:
identifying an indication of excessive wear on the wear log;
determining that the indication of excessive wear does not reoccur on the wear log at a substantially regular depth interval; and
generating a visual indicator identifying the excessive wear feature as being potentially erroneous on the display adjacent to the indication of the excessive wear feature.
13. The method of claim 11 , further comprising the steps of:
determining that the pitting log reveals significant pitting of the tubing section at a first depth;
determining that the wear log does not reveal significant wear of the tubing section at the first depth; and
generating a visual indicator on the display comprising an identification of a chemical issue at the first depth for the tubing section.
14. The method of claim 13 , further comprising the step of generating a notification to schedule a chemical treatment for the well.
15. The method of claim 11 , further comprising the steps of:
determining that the wear log reveals significant wear of the tubing section at a first depth;
determining that the pitting log does not reveal significant pitting of the tubing section at the first depth; and
generating a visual indicator on the display adjacent to an indication of the first depth of the wear log comprising a notification that the wear log data revealing significant wear at the first depth is potentially erroneous.
16. The method of claim 15 , wherein the visual indicator composes highlighting the wear data at the first depth.
17. The method of claim 15 , wherein the visual indicator composes a comment presented adjacent the wear log at the first depth.
18. The method of claim 11 , further comprising the steps of:
determining that the wear log reveals a length of significant wear extending along a length of the tubing string at a first depth;
determining that the length of significant wear is less than a predetermined length; and
generating a visual indicator on the display adjacent to the display of the length of significant wear on the wear log at the first depth.
19. The method of claim 18 , wherein the visual indicator composes a notification that the display of the length of significant wear on the wear log is potentially erroneous.
20. The method of claim 18 , wherein the predetermined length is a stroke length for a sucker rod at the well.
21. The method of claim 11 , further comprising the steps of:
determining that the wear log reveals significant wear of the tubing string at a first depth;
determining that the pitting log reveals significant pitting of the tubing string at the first depth;
determining if there is a dogleg in the well at the first depth; and
generating a visual indicator of a dogleg on the display at the indication of the first depth on the display based on a positive determination that there is a dogleg in the well at the first depth.
22. The method of claim 11 , further comprising the steps of:
determining at a first depth that the wear log reveals high wear of the tubing string and the pitting log reveals high pitting of the tubing string;
determining at a second depth that the wear log reveals low wear on the tubing string and the pitting log reveals low pitting on the tubing string;
determining at a third depth that the wear log reveals high wear of the tubing string and the pitting log reveals high pitting of the tubing string;
wherein the second depth is between the first depth and the third depth; and
generating a visual indicator of a harmonic condition.
23. The method of claim 10 , wherein the wear data and the pitting data are presented on a common depth axis on the display.
24. The method of claim 10 further comprising the steps of:
generating a confidence plot comprising an indication of a reliability of each feature presented on at least one of the wear log and the pitting log; and
presenting the confidence plot with the wear data and the pitting data on a common depth axis on the display.
25. The method of claim 10 further comprising the step of receiving a quality grade for the tubing section.
26. The method of claim 25 , wherein the quality grade is received from a manual input by an operator.
27. A method for evaluating a tubing string comprising a plurality of tubing sections at a wellsite comprising a well, comprising the steps of:
moving at least one tubing section into or out of the well;
scanning the tubing section with at least one sensor to receive a plurality of wear data and a plurality of pitting data as at least a portion of the tubing section is being moved into or out of the well;
receiving depth data for the tubing string as the at least one tubing section is being moved into or out of the well;
correlating the wear data with the corresponding depth data to comprise a wear log;
correlating the pitting data with the corresponding depth data to comprise a pitting log;
displaying the wear log and the pitting log on a common depth axis on a visual display; and
analyzing the wear log and the pitting log with a pattern recognition software to identify wear patterns in the tubing string.