Preventive Maintenance of Electric Submersible Pumps and its Relationship to Root Cause of Failure Analysis
Bibliographic record
Abstract
Abstract We live in working environments where it is difficult if not career limiting, to say "no" to job assignments from bosses and colleagues. Pulling before catastrophic failure is normally not done, except in rare instances. Units are rarely pulled until total electrical failure occurs and the unit will not restart. Many times these restart attempts, after the systems have gone to ground, destroys the evidence needed to determine the reason for failure. Proper diagnostics of a unit that shuts down on overload could, in many instances, reduce repair costs for the unit, provided they are pulled prior to restarting. However, this is not the general procedure in the field. Many lease operators do not have the equipment or training to troubleshoot ESP systems after they have gone down and, being pressured for production will automatically attempt a restart as soon as they discover the failure. Producing wells can be monitored, and corrections in operation such as incoming power, wellhead pressure, casing pressure, etc., can reduce the stresses and increase the life of an ESP system. Also, use of proper monitoring techniques can aid in determining needs for replacing equipment and can reduce the repair costs if units are pulled and/or resized prior to catastrophic failure. The operator is the most important determining factor on runlife and is probably the least trained in the operating and design limitations of ESP systems. Further improvement in RCFA in the ESP field is undoubtedly in the hands of the ESP users. More manufacturer-user RCFA teams working together are becoming necessary to extend ESP run times. The objective of this paper is to provide modifications to Root Cause Failures Analysis (RCFA) methods, specific to ESP applications and show how many of the methods can be applied as Preventive Maintenance in the ESP field. It will be supported by case study from the Meekwap field where the "Time between Failures" (TBF) was tripled as a result of close cooperation between field operator and ESP manufacturer. Field Introduction The Meekwap field is located in the Swan Hills area in North-Central Alberta, Canada. It is under an extensive waterflood with 800 - 1600 m3/d of source water from the Debolt formation injected into the Nisku. Field flood pressure ranges from 22 MPa to 15 MPa. The reservoir is mix of dolomite and limestone and belongs to Nisku formation. Dolomite generally utilizes ESP's (11 wells) for production, whereas Limestone area is generally produced with conventional rod pumps, and a total of 20 producers are in five production sections. Emulsion is sour (up to 60,000 ppm) with high chlorides content (3,000 ppm). The oil gravity is 36 degrees API, water gradient is 10.9 kPa/m and Bottom Hole Temperature (BHT) is 86 ° C. ROOT CAUSE FAILURE ANALYSIS - PROPER APPROACH TO FAILURE ANALYSIS RCFA is a disciplined problem solving methodology, used to determine root causes of specific failure events. The following process is necessary to implement a successful RCFA:Determine the failure mode. This is commonly mistaken for the root cause of failure.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".