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Record W2982451494 · doi:10.2118/196310-ms

Preliminary Considerations on the Application of Out-Of-Sequence Multi-Stage Pinpoint Fracturing

2019· article· en· W2982451494 on OpenAlexaboutno aff
Benyamin Yadali Jamaloei

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsGeologyHydraulic fracturingFracture (geology)Oil shaleModulusSequence (biology)AnisotropyStress (linguistics)Stage (stratigraphy)Geotechnical engineeringPetrologyMaterials scienceComposite materialPhysics

Abstract

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Abstract Out-Of-Sequence Pinpoint Fracturing is a conceptual way of maximizing reservoir contact by creating fracture complexity via reducing or neutralizing the stress anisotropy to improve fracture conductivity and connectivity. A complex fracture network is formed by activating planes of weakness present in the form of natural fractures, fissures, faults, joints, and cleats. Branch fractures are created due to the induced stress-relief fractures where they can be connected to the main bi-wing hydraulic fractures to improve fracture network connectivity. Out-Of-Sequence Fracturing is initiated by fracturing Stage 1 (at the toe) and then fracturing Stage 3 toward the heel. Once a desired degree of stress interference between the Stages 1 and 3 (Outside Fracs) is established, Stage 2 (Centre Frac) is placed between the Outside Fracs. The Centre Frac enhances fracture network connectivity and conductivity by connecting to stress-relief fractures from the outside Fracs, taking advantage of the altered stress state. Out-Of-Sequence Fracturing has successfully been tested in Western Siberia (by LUKOIL) and Western Canada in 2014 and 2017, respectively. A fracture model is calibrated using treatment pressures and instantaneous shut-in pressures (ISIP) from the Out-Of-Sequence Pinpoint Fracturing in Western Canada. The fracture model is coupled with reservoir simulation and RTA to evaluate the production potential in Out-Of-Sequence Pinpoint Fracturing and to conduct an extensive sensitivity analysis on petrophysical/geomechanical properties (stress anisotropy, Young's modulus, Poisson's ratio, process zone stress (PZS)/net extension pressure, fracturing gradient, and matrix permeability) and treatment variables (stage spacing, treatment fluid volume/viscosity/rate, and proppant tonnage/size/concentration) to identify the factors that are most critical to optimizing the treatment. The results reveal noticeable production uplift from a carefully designed Out-Of-Sequence Pinpoint Fracturing, which avoids excessive fracture complexity that impedes fracture growth due to pressureout and screenout. Out-Of-Sequence Pinpoint Fracturing is most sensitive to stage spacing, treatment rate, proppant and fluid intensity for the Centre Frac, stress anisotropy, PZS, and brittleness factor (combination of Young's modulus and Poisson's ratio). Screening of these parameters helps identifying well candidates and treatment strategies to avoid both insufficient fracture complexity and excessive fracture complexity, where higher-than-anticipated treatment pressures are observed as an evidence of shear fractures being filled with treatment fluid, causing an additional component of stress that must be opposed by treatment fluid, realizing that higher pressures are only a risk when they are higher than surface pressure constraints. This is the first attempt in pressure history-matching and screening for formation properties and treatment strategies for maximizing Out-Of-Sequence Pinpoint Fracturing benefits. The learnings from this multi-faceted study guide future successful designs of the Out-Of-Sequence Fracturing for completion optimization in unconventional and conventional reservoirs. Rendering a full-length interference effect is possible by conducting an optimized Out-Of-Sequence Fracturing in multiple wells (as part of large-scale field developments) to ensure optimizing the stress shadowing while reducing the risk of well bashing.

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)0.003
metaresearch head score (Gemma)0.006
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.037
GPT teacher head0.277
Teacher spread0.240 · 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

Citations10
Published2019
Admission routes1
Has abstractyes

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