Run-Life Improvement by Implementation of Artificial-Lift-Systems Failure Classification
Bibliographic record
Abstract
This article, written by Special Publications Editor Adam Wilson, contains highlights of paper SPE 173913, “Run-Life Improvement by Implementation of Artificial-Lift- Systems Failure Classification and Root-Cause Failure Classification,” by Edward Rubiano, José Luis Martin, Jesus Prada, Manuel Monroy, Laura Labrador, Jonathan Celis, and Jahir Gutierrez, Oxy, and Michel Bohorquez, Ecopetrol, prepared for the 2015 SPE Artificial Lift Conference—Latin America and Caribbean, Salvador, Brazil, 27–28 May. The paper has not been peer reviewed. This paper describes a methodology for classification of artificial-lift-system (ALS) failures and addition of a commonly used root-cause failure classification. This methodology was applied to different ALSs such as beam pumps (BPs), progressing cavity pumps (PCPs), electrical submersible pumps (ESPs), and electrical submersible progressing cavity pumps (ESPCPs). The starting point was the definition of the boundaries of each system. Then, each job in the well was defined as (1) a failure, (2) a failure, non-ALS, or (3) no failure, in order to look for ways to improve run life. Introduction La Cira-Infantas oil field is in the Middle Magdalena Valley in Colombia (Fig. 1). The vast majority of the wells are completed with 7-in. production casing and perforated with 5 shots/ft with no sand-control completion in place, and then the ALS is installed. ALS in La Cira-Infantas From the beginning of the activity in La Cira-Infantas, almost 100 years ago, several ALSs have been used to produce fluids from the wells. Since 2005, the field has offered different conditions, depending on the mature state of the waterflooding— high water percentage, high and low flow rates, free gas, and corrosive fluids. Currently, ALSs such as BPs, PCPs, ESPs, and ESPCPs are used for producing the total fluid of the field. Failure Analysis The root-cause failure-analysis (RCFA) process used in La Cira-Infantas has the objective of finding the real cause of a given failure and identifying effective solutions to prevent a recurrence of the failure. The steps for the RCFA are as follows. Initial Diagnosis and Failure Confirmation. When an ALS ceases to lift the expected production, specific procedures are performed to try to restart the system. If production is not re-established, a downhole failure is confirmed. Pulling Equipment From the Well. Mechanical and electrical tests are performed to determine ALS condition. Tubing is tested for leaks. As equipment is pulled, each part of the ALS is inspected carefully and qualitative measurements are taken.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".