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Record W1974614909 · doi:10.2118/170949-ms

Environmental Risk and Well Integrity of Plugged and Abandoned Wells

2014· article· en· W1974614909 on OpenAlexaff
George E. King, Randy L. Valencia

Bibliographic record

VenueSPE Annual Technical Conference and Exhibition · 2014
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsApache (Canada)
Fundersnot available
KeywordsAbandonment (legal)Risk analysis (engineering)Isolation (microbiology)Forensic engineeringVariety (cybernetics)EngineeringComputer scienceBusinessPolitical science

Abstract

fetched live from OpenAlex

Abstract This paper is the third in a series of environmental risk assessments covering hydraulic fracturing (SPE 152596) and well construction (SPE 166142). Risk assessment and well failure from SPE literature and governmental agencies have been used to construct a detailed but non-company associated study of plug and abandonment (P&A) objectives, problems, best practices, application details and methods. The objective is to identify technology improvements as well as potential or proven problem areas. Technology gaps will be related where they are identified by either problem reports or failures. Case histories have been captured that illustrate a variety of abandonment reasons and approaches in plug setting, isolation and testing/monitoring methods. The study well groupings are divided by age and era or vintage of abandonment to examine the worth of technologies in force at the time of abandonment. Well age, well type and general geographic influences are presented with notation of specific problems and conditions that challenged effective isolation. Special attention will be paid to cases of failed isolation as cited by the governmental inspection and/or governing body as well as repair methods that restore integrity. Attention will also be given to problems encountered in older wells. Lessons gleaned from this study will be of value to well construction and operational maintenance with consideration paid to well type, geological hazards, production history and durability of the isolation seal.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.404
Threshold uncertainty score0.447

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 head0.006
GPT teacher head0.189
Teacher spread0.183 · 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

Citations52
Published2014
Admission routes1
Has abstractyes

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