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Record W4395008230 · doi:10.2118/218804-ms

A Chemical Method of Achieving Uniform Stimulation Intensity Along Long Laterals in Horizontal Wells

2024· article· en· W4395008230 on OpenAlexaff
Zebo Yuan, Ning Xu, Jin Song, Ying Shi, Chen Zhangxin

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsIntensity (physics)OpticsPhysics

Abstract

fetched live from OpenAlex

Abstract To exploit a reservoir more efficiently, horizontal wells with long laterals are being drilled in many oilfields in the world. Uniform acidizing by bullheading is a challenge in these wells because the volume of acid that enters each segment tends to be different: larger at the heel while smaller at the toe. This further causes a non-uniform production profile which advances breakthrough of water. In this paper, a method of using a temperature-sensitive viscous acid to achieve uniform stimulation profile is introduced. The acid is delicately designed so that the viscosity of it changes with temperature and the changing rate can be quantitatively controlled. As the acid flows in wellbore, the diffusion coefficient of H+ and therefore the acid-rock reaction rate gradually increases while it is being warmed by wellbore. A model describing the change of viscosity and H+ mass transfer coefficient is established and coupled with a wellbore heat transfer model. The basic of this acid is hydrochloric acid but it is mixed with a special gelatinizer. The initial viscosity of the acid ranges from 60 to 1400 mPa.s at surface, depending on the concentration of gelatinizer. Its viscosity gradually decreases to 200 mPa.s with temperature increasing to 100 ℃.The leaking off velocity decreases from 2.0×10−6 m/s to 2.0×10−7 m/s accordingly, depending on the injecting time. And the acid concentration at the wall of wellbore increase by three times when the injecting rate doubles, also depending on the wellbore temperature and pressure. The acid-rock reaction rate is faster at toe than at heel, which just compensates the differences of acid volume allocation between toe and heel due to wellbore flow friction. A model describing the correlations between the acid viscosity, diffusion coefficient of H+, the concentrations of HCl, the injection rate and the temperature is generated. This model is coupled with an acid flow and reaction model and a wellbore heat transfer model and in the end a profile of H+ consumption along the lateral is predicted. With use of this model, by adjustment of acid formula and injection rate, a uniform stimulation intensity profile along the long lateral can be achieved. A well case is introduced to show the application of this model. The dynamic temperature profile, the acid viscosity profile, the allocation of hydrogen ions as well as the equivalent wormhole lengths along the wellbore are all given in the paper. This paper introduces a chemical way to achieve uniform acidizing intensity in horizontal wells with long laterals without running in mechanical tools, reducing cost and leaving a good wellbore environment for future well interventions after treatment.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.008
GPT teacher head0.255
Teacher spread0.247 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2024
Admission routes1
Has abstractyes

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