SYSTEM AND METHOD FOR AUTOMATION OF DETECTION OF STRESS PATTERNS AND EQUIPMENT FAILURES IN HYDROCARBON EXTRACTION AND PRODUCTION

• US 20170096889A1
• Filed: 03/27/2015
• Published: 04/06/2017
• Est. Priority Date: 03/28/2014
• Status: Abandoned Application

• Current Assignee: Sensia, LLC (Rockwell Automation, Inc.)
• Original Assignee: Schlumberger Technology Corporation (Schlumberger NV)
• Inventors: Blanckaert, Thibault, Subervie, Yves-Marie Clet Robert
• Application Number:
  US15/128,253
• Publication Number:
  US 20170096889A1
• Time in Patent Office: Days
• CPC Class Codes: E21B 41/0085 Adaptations of electric power generating means for use in boreholes
• CPC Class Codes: E21B 43/128 Adaptation of pump systems with down-hole electric drives
• CPC Class Codes: E21B 44/00 Automatic control systems specially adapted for drilling operations, i.e. self-operating systems which function to carry out or modify a drilling operation without intervention of a human operator, e.g. computer-controlled drilling systems; Systems specially adapted for monitoring a plurality of drilling variables or conditions
• CPC Class Codes: E21B 47/008 Monitoring of down-hole pump systems, e.g. for the detection of "pumped-off" conditions
• CPC Class Codes: E21B 47/06 Measuring temperature or pressure
• CPC Class Codes: E21B 47/07 Temperature
• CPC Class Codes: G05B 19/4065 Monitoring tool breakage, life or condition
• CPC Class Codes: G05B 2219/45004 Mining
• CPC Class Codes: G05B 2219/45129 Boring, drilling

Abstract

Process control system and method using data-stream segmentation, segment modeling, and Bayesian belief networks for analyzing sensor data are used to determine equipment events, such as equipment failure and equipment stress conditions, that may lead to equipment failure. Other systems and methods are disclosed.

Claims

1. A method for detecting equipment failures or stress conditions that may result in equipment failures in a process in the hydrocarbon industry, where the hydrocarbon-industry-process is subject to a change in a plurality of operating conditions each monitored by at least one sensor providing a plurality of input data streams, comprising:

segmenting the input data streams such that each segment of data points is modeled using a simple mathematical model;

using the segmentations and statistical parameters associated with the segmentations and the underlying data to compute probabilities associated with at least one high-level inquiry in regard to the input streams thereby computing probabilities for inquiry answers; and

inputting the high-level inquiry probabilities into a reasoning engine and operating the reasoning engine to determine the probability of an equipment event.

2. The method of claim 1 wherein the at least one high-level inquiry is a determination of a probability values for basic tendencies of an operating physical property relative to a reference distribution.

3. The method of claim 2 wherein the basic tendencies are decrease strongly, decrease, steady, increase, and increase strongly and the probabilities for each of these is determined by comparing the probability distribution according to the model against defined thresholds in the reference distribution.

4. The method of claim 1 wherein the at least one high-level inquiry is a determination of a probability of noise levels of an operating physical property.

5. The method of claim 4 wherein noise levels stable, unstable, and very unstable and probabilities for each of these is determined by comparing the distribution for noise in the input sensor data against pre-defined threshold values.

6. The method of claim 1 wherein the at least one high-level inquiry is a determination of a probability of correlation values between two operating physical properties.

7. The method of claim 6 wherein the correlation values are negative correlation, positive correlation, and no correlation and the probability of each of these is determined by comparing the distribution of correlation against pre-defined thresholds.

8. The method of claim 1 wherein the reasoning engine comprises a Bayesian belief network linking the high-level inquiry probabilities to equipment event probabilities.

9. The method of claim 1 further comprising upon the reasoning engine determining a probability of an equipment event exceeding a predefined threshold, triggering an alarm.

10. The method of claim 1 wherein the hydro-carbon process is artificial lift using an electric submersible pump or a progressive cavity pump.

11. The method claim 10 wherein the sensor data includes at least one sensor input selected from discharge pressure, intake pressure, delta pressure, current draw, motor temperature, wellhead temperature, wellhead pressure.

12. The method of claim 10 wherein the equipment event is one of deadhead, low flow, gas ingestion, downhole mechanical failure, and pump off.

13. A hydrocarbon process control system comprising:

at least one sensor measuring an operating property of a hydrocarbon process controlled by the control system;

a signal processing module for segmenting an input stream from the at least one sensor and for computing probabilities of answers to at least one high-level inquiry in regard to the input stream from the at least one sensor; and

an expert system connected to the signal processing module and operable to receive the probabilities for the answers to the at least one high-level inquiry and operable to compute therefrom probabilities of at least one equipment event.

14. The hydrocarbon process control system of claim 13 wherein the at least one high-level inquiry is a determination of a probability values for basic tendencies of an operating physical property relative to a reference distribution.

15. The hydrocarbon process control system of claim 14 wherein the basic tendencies are decrease strongly, decrease, steady, increase, and increase strongly and the probabilities for each of these is determined by comparing the probability distribution according to the model against defined thresholds in the reference distribution.

16. The hydrocarbon process control system of claim 13 wherein the at least one high-level inquiry is a determination of a probability of noise levels of an operating physical property.

17. The hydrocarbon process control system of claim 16 wherein noise levels stable, unstable, and very unstable and probabilities for each of these is determined by comparing the distribution for noise in the input sensor data against pre-defined threshold values.

18. The hydrocarbon process control system of claim 13 wherein the at least one high-level inquiry is a determination of a probability of correlation values between two operating physical properties.

19. The hydrocarbon process control system of claim 18 wherein the correlation values are negative correlation, positive correlation, and no correlation and the probability of each of these is determined by comparing the distribution of correlation against pre-defined thresholds.

20. The hydrocarbon process control system of claim 13 wherein the reasoning engine comprises a Bayesian belief network linking the high-level inquiry probabilities to equipment event probabilities.

21. The hydrocarbon process control system of claim 13 further comprising upon the reasoning engine determining a probability of an equipment event exceeding a predefined threshold, triggering an alarm.

22. The hydrocarbon process control system of claim 13 wherein the hydro-carbon process is artificial lift using an electric submersible pump or a progressive cavity pump.

23. The hydrocarbon process control system claim 22 wherein the sensor data includes at least one sensor input selected from discharge pressure, intake pressure, delta pressure, current draw, motor temperature, wellhead temperature, wellhead pressure.

24. The hydrocarbon process control system of claim 22 wherein the equipment event is one of deadhead, low flow, gas ingestion, downhole mechanical failure, and pump off.