System and method for automation of detection of stress patterns and equipment failures in hydrocarbon extraction and production

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.

We claim: 
     
         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.