Performance Analysis of the Artificial Lift Systems for Heavy Oil Wells in the Egyptian Eastern Desert
Bibliographic record
Abstract
Abstract This paper describes the implementation of a performance analysis (PA) review and a comprehensive root cause failure analysis (RCFA) for the artificial lift system (ALS) failures in heavy oil wells. The subject wells, located in Egyptian Eastern Desert (EED), have been operated by Canadian-Egyptian joint venture. Progressive cavity pump (PCP) and beam pump (BP) have been selected as the preferred ALS methods. With the objective of improving ALS performance, RCFA identifies the main causes of ALS failures and proposes guidelines and recommendations with a new open source service for the current and future development for EED Oil companies. PA has been applied using some Key Performance Indicators (KPI), such as: failure index, recurrence index, and average run life in order to track all failed and crucial issues. RCFA has been applied to different ALS, such as: BP and PCP. Therefore, the definition or limitation of the boundaries of each system and the classification of failures were performed. RCFA covered failures between 2012 and 2018 for average 150 active wells and 622 failures. Finally, the new open source service applied as a trial to evolve the failure tracking and decision making methodology. PA review showed a high failure and recurrence index that reached 3.0 and 3.7, respectively, in 2014 then declined to 1.0 and 1.7, respectively, in 2017 as a result of comprehensive corrective actions implemented. RCFA showed that rod string failure in BP was and still is a dominant failure with 200 failures over the last six years. Moreover, Down-hole pump failure in PCP was the major PCP failure system although its failures declined from 80 failures in 2014 to 14 failures in 2018. Several field cases were presented to cover the dominant cause of failures for both BP and PCP wells. Most wells had a significant improvement in their mean time to failure (MTTF) after reviewing design, installation and operation procedures. The procedure for carrying out this methodology and implementing lessons learned has been presented in this paper. In addition, the new open source integration service provides an increased visibility about individual well performance issues and more broadly, about field performance and ALS failures.
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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 | 0.000 | 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) | 0.000 | 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".