System and Method for Integrated Risk and Health Management of Electric Submersible Pumping Systems

• US 20150095100A1
• Filed: 09/30/2013
• Published: 04/02/2015
• Est. Priority Date: 09/30/2013
• Status: Abandoned Application

• Current Assignee: Ge Oil & Gas Esp, Inc. (General Electric Company)
• Original Assignee: Ge Oil & Gas Esp, Inc. (General Electric Company)
• Inventors: Vittal, Sameer, Lee, Chongchan, Patrick, Romano
• Application Number:
  US14/042,078
• Publication Number:
  US 20150095100A1
• Time in Patent Office: Days
• US Class Current: 705/7.28
• CPC Class Codes: E21B 43/128 Adaptation of pump systems with down-hole electric drives
• CPC Class Codes: E21B 47/008 Monitoring of down-hole pump systems, e.g. for the detection of "pumped-off" conditions
• CPC Class Codes: F04B 49/065 and making use of computers
• CPC Class Codes: F04D 13/10 adapted for use in mining bore holes
• CPC Class Codes: F04D 15/0088 Testing machines
• CPC Class Codes: G05B 23/0213 Modular or universal configuration of the monitoring system, e.g. monitoring system having modules that may be combined to build monitoring program; monitoring system that can be applied to legacy systems; adaptable monitoring system; using different communication protocols
• CPC Class Codes: G05B 23/0283 Predictive maintenance, e.g. involving the monitoring of a system and, based on the monitoring results, taking decisions on the maintenance schedule of the monitored system; Estimating remaining useful life [RUL] preventive maintenance, i.e. planning maintenance according to the available resources without monitoring the system G06Q10/06
• CPC Class Codes: G06Q 10/0635 Risk analysis of enterprise or organisation activities
• CPC Class Codes: Y02P 90/80 Management or planning

Abstract

A system and process for optimizing the performance and evaluating the risks of pumping systems includes the steps of measuring the operation and condition of components within a discrete electric submersible pumping system, accumulating these measurements across a field of electric submersible pumping systems, performing statistical analysis on the accumulated measurements and producing one or more selected outputs from the group statistical analysis. In the preferred embodiments, the statistical analysis and data processing occurs at both the individual pumping system and at one or more centralized locations.

Claims

1. A process for producing a risk analysis report for a plurality of pumping systems, the process comprising the steps of:

providing a local control unit at each of the plurality of pumping systems;

providing output signals to each of the plurality of local control units from each of the corresponding pumping systems, wherein each of the output signals is reflective of an operating condition measured at the pumping system;

processing the output signals at each of the plurality of local control units;

producing a health index at each of the plurality of local control units; and

uploading the health index from each of the plurality of local control units to a central data center.

2. The process of claim 1, wherein the step of providing output signals to each of the plurality of local control units further comprises providing on a scheduled periodic basis an output signal selected from the group consisting of:

motor voltage, motor current, power factor, pump intake pressure, motor temperature, motor frequency, pump intake temperature, vibration, flowing bottom hole pressure, well head pressure and leakage current.

3. The process of claim 1, wherein the step of processing the output signals at each of the plurality of local control units further comprises performing a statistical analysis on the output signals using multivariate statistical algorithms.

4. The process of claim 3, wherein the step of processing the output signals at each of the plurality of local control units further comprises performing a multivariate statistical algorithm selected from the group consisting of:

probability-density based usage indices, multivariate Hotelling T-squared distributions, change point detection algorithms, and Bayesian and neural network-based anomaly detection and classification algorithms.

5. The process of claim 1, further comprising the steps of:

categorizing at the central data center the health indices received from the plurality of local control units; and

generating a multi-level survival model based on the categorized health indices.

6. The process of claim 5, wherein the step of categorizing at the central data center the health indices received from the plurality of local control units further comprises categorizing the health indices according to classes selected from the group consisting of equipment models, geographic regions and downhole applications.

7. The process of claim 5, wherein the step of generating a multi-level survival model further comprises trending the categorized health indices to produce multi-level survival models.

8. The process of claim 7, wherein the step of generating a multi-level survival model further comprises trending the categorized health indices to produce multi-level survival models at regional, site and individual pumping system levels.

9. The process of claim 5, further comprising the steps of:

applying health indices specific to a selected pumping system to the multi-level survival model; and

generating the risk analysis report for the selected pumping system based on the application of the specific health indices within the multi-level survival model.

10. The process of claim 9, wherein the step of generating the risk analysis report for the selected pumping system based on the application of the specific health indices within the multi-level survival model further comprises generating a risk analysis report selected from the group consisting of technical risk report, operational risk report and financial risk report.

11. The process of claim 10, wherein the step of generating the risk analysis report for the selected pumping system further comprises generating the risk analysis report for a plurality of selected pumping systems.

12. A process for optimizing the performance of a selected pumping system within a plurality of pumping systems, the process comprising the steps of:

providing a local control unit at each of the plurality of pumping systems;

providing output signals to each of the plurality of local control units from each of the corresponding pumping systems, wherein each of the output signals is reflective of an operating condition measured at the pumping system;

processing the output signals at each of the plurality of local control units;

producing a health index at each of the plurality of local control units;

uploading the health index from each of the plurality of local control units to a central data center;

categorizing at the central data center the health indices received from the plurality of local control units;

generating a multi-level survival model based on the categorized health indices; and

applying health indices specific to the selected pumping system to the multi-level survival model to produce optimized operating instructions; and

adjusting the operational characteristics of the selected pumping system in accordance with the optimized operating instructions.

13. The process of claim 12, wherein the step of uploading the health index from each of the plurality of local control units to a central data center further comprises uploading the health indices over a wide area network.

14. The process of claim 12, wherein the step of adjusting the operational characteristics of the selected pumping system further comprises adjusting the operational characteristics of the selected pumping system from the central data center over a wide area network.

15. A process for producing a financial risk report for a long-term service contract for a selected pumping system within a plurality of pumping systems, the process comprising the steps of:

providing a local control unit at each of the plurality of pumping systems;

providing output signals to each of the plurality of local control units from each of the corresponding pumping systems, wherein each of the output signals is reflective of an operating condition measured at the pumping system;

processing the output signals at each of the plurality of local control units;

producing a health index at each of the plurality of local control units;

uploading the health index from each of the plurality of local control units to a central data center;

categorizing at the central data center the health indices received from the plurality of local control units;

generating a multi-level survival model based on the categorized health indices; and

applying health indices specific to the selected pumping system to the multi-level survival model to determine failure rate information for the selected pumping system; and

generating the financial risk report for the long-term service contract based on the determined failure rate information.