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Record W4248804324 · doi:10.2118/spe-173348-ms

Challenging Assumptions About Fracture Stimulation Placement Effectiveness Using Fiber Optic Distributed Sensing Diagnostics: Diversion, Stage Isolation and Overflushing

2015· article· en· W4248804324 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 Hydraulic Fracturing Technology Conference · 2015
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsShell (Canada)
Fundersnot available
KeywordsPerforationWell stimulationHydraulic fracturingFracture (geology)Computer sciencePetroleum engineeringProcess (computing)WellboreStage (stratigraphy)GeologyEnvironmental scienceEngineeringGeotechnical engineeringMechanical engineeringPetroleum

Abstract

fetched live from OpenAlex

Abstract The connection of the wellbore to the hydrocarbon resource volumes via effective fracture stimulation is a critical factor in unconventional reservoir completions. Various well construction and dynamic placement methods are used to distribute treatment volumes into targeted sections of the wellbore. This paper provides some insights into the effectiveness of hydraulic fracture stimulation process using Fiber Optics (FO): distributed acoustic sensing (DAS) and distributed temperature sensing (DTS). This paper reviews examples from multiple wells where FO has been used to gain a better understanding of three highly debated fracture stimulation distribution topics: Diversion, Stage Isolation and Overflushing. Diversion is increasingly being used as a way to improve the efficiency of hydraulic fracture stimulation distributions. The effectiveness of the diversion techniques has traditionally been judged on the basis of surface pressure response during treatment and ultimately, from production comparisons to reference wells. Unfortunately, getting clear answers from production performance takes significant time. FO allows for monitoring of the diversion process in real-time. Analysis of DAS and DTS responses is used to quantify diversion efficiency in re-directing hydraulic fracture stimulation from dominant perforation clusters to those not being stimulated. Lack of isolation between stages has frequently been observed in wells with diagnostics. There is consensus amongst the completion community that communication between stages is highly undesirable because the energy and materials of the stimulation are partially or totally misdirected from the target interval to other portions of the wellbore. The analysis of DAS and DTS not only can help determine the frequency of occurrence of communication between stages in cemented and uncemented horizontal wells but also can provide insights about the different communication paths. Fiber Optic distributed sensing in conjunction with complementary diagnostics is also being used to investigate if connections are being maintained at the end of the treatment between the newly created fracs and the wellbore. The use of integrated diagnostics allows evaluation of the frequency in which overflushing (over-displacement) occurs in both vertical and horizontal wells and its impact on well inflow performance where production profiling data is available.

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)0.000
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.484
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.020
GPT teacher head0.239
Teacher spread0.220 · 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