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Record W2012070999 · doi:10.2118/142854-pa

Assessing Well-Integrity Risk: A Qualitative Model

2012· article· en· W2012070999 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSPE Drilling & Completion · 2012
Typearticle
Languageen
FieldDecision Sciences
TopicRisk and Safety Analysis
Canadian institutionsConocoPhillips (Canada)
FundersConocoPhillips
KeywordsBrainstormingFailure mode and effects analysisRisk analysis (engineering)Risk assessmentRanking (information retrieval)Process (computing)Risk managementComputer scienceFault tree analysisContainment (computer programming)EngineeringReliability engineeringBusinessComputer security

Abstract

fetched live from OpenAlex

Summary For successful delivery of well integrity (WI), there needs to be an understanding of the risks that can cause undesirable events such as safety hazards or loss of containment. Performing a risk assessment (RA) on a well, or type of well, will help determine and rank the potential risks and provide information that allows limited resources to be applied in the most effective manner. The main objectives of performing a risk assessment include (a) following a formal process to assess risk consistently and to enable comparison between well-barrier failure-mode scenarios; (b) qualitatively assessing well-barrier failure risk for every segment of a well; (c) documenting suggestions that are offered by the riskassessment team for mitigating well-barrier failure risk; and (d) providing a report of the methodology, failure-mode scenarios, risk ranking, and potential mitigation actions for use as a reference tool for managing WI on a routine basis. Our WI/RA model follows a common qualitative risk-assessment process—a team-based, structured brainstorming format, using the "What-If Methodology" to identify potential hazards associated with well-barrier failure modes. In addition, the model has the following attributes: It incorporates a unique method to segment well barriers into discrete sections, successively "failing" each section for evaluation. The list of analyzed well-barrier failure modes, along with their risk ranking, becomes the risk register for the well or type of well.It is adaptable for assessing well-barrier failure modes on a single well, or a group of wells, having the same general design parameters. An entire well portfolio can be assessed quickly by analyzing types of wells rather than individual wells.It can be used to assess well-barrier failure risk for any type of well.The model can easily be modified to conform to any company's risk model.The WI/RA model has been proven toSuccessfully assess well-barrier failure risk for thousands of wellsFocus specifically on well-barrier failure modes, and as a result be an effective tool that should be incorporated into a "best-in-class" WI programBe used as a management tool to provide guidance for how limited resources can be used effectively to continuously deliver WI.

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.

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)0.010
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.539
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.344
GPT teacher head0.500
Teacher spread0.156 · 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