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Record W4381330724 · doi:10.2118/213841-ms

Chemical Prevention of Corrosion-Induced Premature Well Failures Using a Novel Lubricity Mitigation Strategy

2023· article· en· W4381330724 on OpenAlexaboutno aff
Jason Kisel, Leanne Seher, Garett Heath, Temi Okesanya

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicOil and Gas Production Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsCorrosionRoot causeCatastrophic failureReliability (semiconductor)Root cause analysisService lifeSucker rodCorrosion fatigueLubricityForensic engineeringEngineeringPetroleum engineeringEnvironmental scienceReliability engineeringMaterials scienceMechanical engineeringMetallurgy

Abstract

fetched live from OpenAlex

Abstract The sucker rod systems and tubing strings play a crucial role in the mechanical oil recovery process; their stability and reliability are desirable factors in optimizing oil production from a well. Although durable, these metallic downhole tools and equipment can experience failure during oil recovery. These failures usually stem from accumulated fatigue induced by corrosion or associated mechanisms. The dilapidation of these costly crucial pumping components often leads to expensive downtimes, lost production, and unnecessary maintenance costs, which can all be exacerbated by frequent failures. While such downhole equipment failures have traditionally and simplistically been prevented using conventional corrosion inhibitors, this approach has proved ineffective in high-failure frequency wells, instigating the use of ancillary production chemicals to address other issues that are symptomatic of observed corrosion mechanisms. It is imperative to develop a mitigation strategy to extend the run life of wells and occlude premature downhole equipment failures, thereby providing increased production time and cost savings for production companies. This paper presents a field-proven methodology that was used to extend the run life of high-failure frequency wells in the Western Canadian Sedimentary Basin (WCSB), involving the continuous application of lubricious corrosion inhibitors. The corrosion inhibitors’ Coefficient of Friction (CoF) was quantified using the Bruker UMT tribology tester. This bifurcate study focuses on two major aspects: a root cause analysis of the high-frequency failures and the corresponding chemical mitigation strategy to address these rampant failures. The scientific root cause analysis showed that wellbore configuration has a substantial and consistent effect on downhole equipment failures and hints at the inimical role of friction in inducing rampant failures, prompting the need to incorporate lubricity in the chemical mitigation strategy, especially for complex directional wells. This successful approach has been implemented in over 3000 wells and has eliminated frequent pumping component failures and extended the run-life of wells in some cases from about three months to up to three years in the Western Canadian Sedimentary Basin (WCSB), resulting in up to $250,000.00 in cost savings on each well from reduced production chemicals usage and lower well-servicing costs. The results from this data-backed study provide a more validated rationale and mitigation approach to dealing with premature well failures. This paper showcases and elucidates an economical and pragmatic chemical mitigation strategy to address frequent well failures and optimize oil production while providing operational recommendations during drilling that are key to improving a well's long-term productivity.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.026
GPT teacher head0.265
Teacher spread0.239 · 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 designBench or experimental
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

Citations0
Published2023
Admission routes1
Has abstractyes

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