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Record W1969882599 · doi:10.2118/75221-ms

Successful Acid Treatments in Horizontal Openholes Using Dynamic Diversion and Downhole Mixing—An In-Depth Postjob Evaluation

2002· article· en· W1969882599 on OpenAlexaff
Jim B. Surjaatmadja, B. W. McDaniel, A. Cheng, Keith Rispler, M. Rees, Abe Khallad

Bibliographic record

VenueSPE/DOE Improved Oil Recovery Symposium · 2002
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsPetro-Canada
Fundersnot available
KeywordsWellboreWell stimulationMixing (physics)Annulus (botany)Coiled tubingPetroleum engineeringCompletion (oil and gas wells)Fracture (geology)GeologyProcess (computing)Hydraulic fracturingGeotechnical engineeringMaterials scienceComposite materialComputer scienceReservoir engineeringPetroleumPhysics

Abstract

fetched live from OpenAlex

Abstract This paper discusses a relatively new acid-stimulation process that uses dynamic fluid energy to divert flow into a specific fracture point in the well, which can initiate and accurately place a fracture. The acid-stimulation process often uses two independent fluid streams: one in the pipe and one in the annulus. With this process, two different fluids can be mixed downhole with high energy to form a homogenous mixture. Four such treatments in four openhole, horizontal wells were performed in one formation. The first well was acidfractured with coiled tubing to replace many small fractures along the open hole. The second well was acid-fractured with coiled tubing, but downhole mixing concepts were used to provide in-situ generation of CO2 foam. Fewer, yet larger, fractures were placed in this well. The third and fourth wells were treated with acid using rotating and nonrotating jetting tools while mixing the acid with CO2 downhole. Many small, near-wellbore fractures were expected in these wells. Investigating the mechanism that may have contributed to these successes in the field is important. Laboratory tests were performed to analyze the jetting mechanism exposed to rock. The findings of these tests are reported in this paper.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.615
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
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.012
GPT teacher head0.238
Teacher spread0.226 · 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 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

Citations11
Published2002
Admission routes1
Has abstractyes

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