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Record W2028738842 · doi:10.2118/80385-ms

PWRI: Scale Formation Risk Assessment and Management

2003· article· en· W2028738842 on OpenAlexaff
Eric Mackay, I. R. Collins, M. M. Jordan, N. D. Feasey

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsNalco (Canada)Nalcor Energy (Canada)
Fundersnot available
KeywordsSubseaPetroleum engineeringInjection wellWater injection (oil production)Environmental scienceProduced waterScale (ratio)Deposition (geology)SeawaterCompletion (oil and gas wells)Marine engineeringProcess engineeringEnvironmental engineeringGeologyEngineeringOceanographySediment

Abstract

fetched live from OpenAlex

Abstract The injection of seawater into oilfield reservoirs to maintain reservoir pressure and improve secondary recovery is a well-established, mature, operation. Moreover, the degree of risk posed by deposition of mineral scales to the injection and production wells during such operations has been much studied. However, the current drive within the North Sea to reduce the environmental burden of production chemicals and to reduce oil discharge to the environment has focused attention on the challenge of produced water management and has introduced new challenges for scale management involving produced water re-injection. This paper will outline the risk assessment process required prior to undertaking produced water re-injection. The factors that will be considered are the location of scale deposition around fractured and unfractured injection wells, formation damage potential and impact, and retardation effects on injected scale inhibitors. The paper will draw upon computer modelling techniques, laboratory generated coreflood data, and field results that will demonstrate the impact of the following factors on long term water injectivity: viz, scaling tendency, suspended solids content, suspended oil content, injection temperature, reservoir type, and completion type. Furthermore, scale control measures currently being employed (e.g., scale inhibition, hydraulic fracturing, drag reduction, and solvent cleaning) will be assessed and reviewed against the risks identified. Finally, this paper will outline in detail the particular scaling issues associated with produced water re-injection for both platform and subsea facilities.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.807
Threshold uncertainty score0.181

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 head0.005
GPT teacher head0.218
Teacher spread0.213 · 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 designSimulation or modeling
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

Citations69
Published2003
Admission routes1
Has abstractyes

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