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Record W2135906824 · doi:10.2118/108675-pa

A Breakthrough Technology for Maximizing Water Injectivity and Asset Integrity

2009· article· en· W2135906824 on OpenAlexaff
David I. Horsup

Bibliographic record

VenueSPE Production & Operations · 2009
Typearticle
Languageen
FieldEngineering
TopicOil and Gas Production Techniques
Canadian institutionsNalco (Canada)
Fundersnot available
KeywordsPetroleum engineeringWater injection (oil production)Asset (computer security)Injection wellProduction (economics)Oil fieldEnvironmental remediationEnvironmental scienceOil productionIntegrity managementWellboreHazardous wasteRisk analysis (engineering)EngineeringComputer scienceBusinessWaste managementEnvironmental engineeringPipeline transportComputer securityContamination

Abstract

fetched live from OpenAlex

Summary The importance of maintaining oil production has never been more critical than it is today. For many fields using water injection to maintain reservoir pressure, the injection rate can decline over time because of the blocking of pore throats in the near wellbore region. Remediation can be expensive, incur lost production, and expose operations personnel to hazardous chemicals. Additionally, the material that blocks the pore throats also deposits within the injection infrastructure, resulting in significant asset-integrity challenges. This paper describes a new technology that has been developed that can increase significantly water injectivity, potentially prevent well interventions, maximize production, and preserve the integrity of the injection infrastructure. Through extensive research, a new, patented, multifunctional product has been developed that, when injected into the water injection system, cleans away deposits, prevents new deposits from forming, and provides corrosion inhibition to the injection infrastructure. This paper discusses the research that was performed to develop this new technology and the results of several field applications.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.481
Threshold uncertainty score0.429

Codex and Gemma teacher scores by category

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

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 head0.014
GPT teacher head0.257
Teacher spread0.243 · 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 teacher head, 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

Citations3
Published2009
Admission routes1
Has abstractyes

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