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Record W2075350984 · doi:10.2118/81730-ms

New Life for Old Wells – A Case Study of the Effects of Re-Stimulating Gas Wells Using Fracturing Through Coiled Tubing and Snubbing Techniques

2003· article· en· W2075350984 on OpenAlexaffabout
Chad Gutor, Ali Al-Saleem, Bruce Rieger, Stephen Lemp

Bibliographic record

VenueSPE/ICoTA Coiled Tubing Conference and Exhibition · 2003
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsSchlumberger (Canada)
Fundersnot available
KeywordsCoiled tubingPetroleum engineeringGeology

Abstract

fetched live from OpenAlex

Abstract The use of a coiled tubing conduit for the hydraulic fracturing of shallow gas wells in southern Alberta, Canada has increased each year since its beginnings in 1997. The coiled tubing fracturing (CTF) technique has been utilized for both new and old wells as a means of fracture-stimulating multiple reservoir intervals. This paper will detail a re-stimulation project completed during the summer and fall of 2002, in which the CTF and snubbing-conveyed fracturing (SF) processes were utilized to re-fracture a group of shallow gas wells that were originally completed in the 1970s. The objective is to examine the possible ways of enhancing production of older shallow gas wells by fracture stimulation utilizing tubing-conveyed processes. Specifically, the paper will outline ways to consider and select wells that are candidates for re-entries, in addition to the essential work and evaluation that has to be done both prior to and following the re-entry, re-perforation, and stimulation techniques. This study will also provide a relative comparison between conventional fracturing techniques used previously and the CTF and SF processes used most recently. Also included are pre- and post-stimulation production data that give a clear indication of how effective the fracturing through tubing process is when used to re-stimulate these older wells.

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 categoriesMeta-epidemiology (narrow)
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 score0.187
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.018
GPT teacher head0.254
Teacher spread0.235 · 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.

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

Citations7
Published2003
Admission routes2
Has abstractyes

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