MétaCan
Menu
Back to cohort
Record W3044579520 · doi:10.2118/199703-pa

Investigating Near-Wellbore Diversion Methods for Refracturing Horizontal Wells

2020· article· en· W3044579520 on OpenAlexaff
Junjing Zhang, M. D. White, Jamie McEwen, Sam Schroeder, David D. Cramer

Bibliographic record

VenueSPE Production & Operations · 2020
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsConocoPhillips (Canada)
Fundersnot available
KeywordsWellborePetroleum engineeringPerforationHydraulic fracturingGeologyGeotechnical engineeringElectrical conduitSpark plugEngineering

Abstract

fetched live from OpenAlex

Summary Near-wellbore diversion of fracturing fluid and proppant is a common approach to achieving improved treatment efficiency when refracturing horizontal wells for expanding treatment coverage within the lateral. There are five broad categories of near-wellbore diversion methods: particulate diversion for bridging open fractures connected to the wellbore; perforation sealing to limit injectivity into open perforation clusters at the wellbore; filling up the drained fracture system with water for achieving more uniform pressurization (i.e., fill-up); injection rate cycling/hesitation fracturing; and mechanical isolation by installing cemented or expandable liners in the lateral followed by plug-and-perforated stimulation. These tactics can be used standalone or combined. Particulate diverting agents can be additionally categorized by particle type (e.g., granular, fibrous) and solubility characteristics. Perforation sealing agents consist of deformable and rigid/spherical subtypes, both of which can be further categorized by solubility characteristics. In this study, treatment and production data for 72 ConocoPhillips refractured wells in a North America shale play were analyzed to evaluate the effectiveness of the various near-wellbore diversion methods and materials. An index was formulated using information on reservoir depletion to normalize changes in bottomhole fracture pressure over time. This was determined by periodically discontinuing injection to obtain instantaneous shut-in pressures (ISIPs) over the course of the treatment. The calculated indices were plotted for each type of diverting system to compare trends for gaining insight on in-situ stress buildup. Production data grouped by different diversion methods were also analyzed. The near-wellbore diversion methods included mixed-size particulates with and without fibrous materials, deformable and rigid perforation sealers, fill-up tactics in which near-wellbore diverting agents were not used, and mechanical isolation by cementing a newly installed liner in the lateral followed by plug-and-perforated stimulation. Fracture hit analysis of offset well treatments indicated that refracturing treatments using particulate diverters were heel biased with respect to reservoir repressurization. The study showed that the incremental pressure as a result of diverter landing on perforations is a poor indication of diverter efficiency. A non-normalized ISIP trend is misleading as an indicator for post-refracturing well performance. Refractured wells with either particulate diverters or perforation sealers both show initial fluid fill-up into the depleted region before the stress buildup plateaus. Wells that have liners installed and cemented inside the original wellbore and that are then restimulated with standard plug-and-perforated techniques show superior performance compared with all diversion methods used in bullhead refracturing treatments. Choice of diversion can have a significant impact on results, but not all particulate diverters or perforation sealers behave similarly. Wells refractured using only the fill-up method have long-term productivity on par with or better than wells refractured with most types of diverting agents.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.382
Threshold uncertainty score0.568

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 studies0.0010.000
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 head0.025
GPT teacher head0.292
Teacher spread0.267 · 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 designSimulation or modeling
Domainnot available
GenreMethods

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

Citations6
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueSPE Production & OperationsSame topicHydraulic Fracturing and Reservoir AnalysisFrench-language works237,207