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Record W2211250677 · doi:10.2118/113173-ms

ESP Operation, Optimization, and Performance Review: ConocoPhillips China Inc. Bohai Bay Project

2007· article· en· W2211250677 on OpenAlexaff
Zhizhuang Jiang, Bassam Zreik

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicOil and Gas Production Techniques
Canadian institutionsConocoPhillips (Canada)
Fundersnot available
KeywordsArtificial liftSubmarine pipelineSubmersible pumpEnvironmental scienceSubseaPetroleum engineeringOil fieldPetroleumEngineeringMarine engineeringGeologyGeotechnical engineering

Abstract

fetched live from OpenAlex

SPE Gulf Coast Section Electric Submersible Pump Workshop, The Woodlands, Texas, 25–27 April, 2007 Abstract ConocoPhillips China Inc. (COPC) operates the Penglai 19–3 oil field, located offshore in Bohai Bay, the People's Republic of China. COPC holds a production sharing agreement with China National Offshore Oil Corporation(CNOOC). The Penglai 19–3 field is the second largest oil field in China with 3.8 billion bbl of oil in place, discovered in May 1999 and put on productionin December 2002. Electrical submersible pumps (ESPs) were selected as the most economic artificial lift method to develop the field, based on the reliability, flexibility and robustness to produce wells with high flow rates and lift heavyoil in an offshore environment. The first ESP installations were challenged with high free gas and excessives and production, resulting in operational issues and a number of failures. Even in this hostile environment, production peaked at 37,800 BOPD during November 2003, before declining as a consequence of reservoir pressure depletion. Moreover, the lower reservoir pressure increased the free gas, thereby reducing pump performance, excessive sand production lead to plugging of the pumping system and sand fill across the reservoir reduced productivity. Various enhancements to the completion and ESP system were made during subsequent well interventions improving ESP performance, maximizing and maintaining production from the field. Furthermore, performing continuous ESP data trend analysis and performance modeling enabled the artificial lift system to be analyzed and diagnosed to maintain optimum well performance. Collaboration between Operator and Service Provider through performance review meetings and changes to operational practices were also implemented. This paper reviews the operation, optimization, and performance of the ESP systems and the challenges faced during the first 3 years of production. Covering initial installations, subsequent well interventions, operation philosophy, optimization methodology, case studies demonstrating value of gas separation and handling devices, completion improvements, ESP configuration enhancements, run life assessment and equipment dismantle findings.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.005
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.006
GPT teacher head0.224
Teacher spread0.218 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations15
Published2007
Admission routes1
Has abstractyes

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