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Record W1972475114 · doi:10.2118/03-05-02

A Practical and Economical Fracturing Solution For Low Permeability Shallow Reservoirs

2003· article· en· W1972475114 on OpenAlexaff
Fang Gu, Ergün Kuru, Zhiqiang Zhao, Zhenze Mo

Bibliographic record

VenueJournal of Canadian Petroleum Technology · 2003
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPetroleum engineeringFracturing fluidHydraulic fracturingPermeability (electromagnetism)SlurryGeologyEnvironmental scienceChemistryEnvironmental engineering

Abstract

fetched live from OpenAlex

Abstract Fluid cost saving is critical for fracturing operations in low permeability reservoirs where the production revenues are low but the job size is relatively large and the fluid cost is high. Cross-linked fluids (CLF) are usually the first option. However, they may cause significant damage to both propped fractures and formations, and are not the cheapest option. Polymer-free fluids, on the other hand, cause much less damage but they are expensive and fluid costs may impair the economic results of fracturing. Waterfrac would be a compromise solution for low permeability reservoirs since its fluids are cheap and fluid damage is low. The success of waterfrac with low slurry concentrations is, however, difficult to predict. This paper presents a new fluid system that was formulated to maximize the economic return of fracturing wells in low permeability shallow oil reservoirs. It is a solid-free, linear, synthetic polymer-based system with a very low formation damage characteristic. The new fluid system can meet a variety of fracturing requirements, including slurry concentrations of conventional field levels. Moreover, it is much cheaper than cross-linked guar gel (CLGG). The method for designing fluid components and the procedure for preparing the fluid to achieve minimum formation damage and minimize the cost are described. A comparison of the production performances from the same well or adjacent reference wells fractured with the new fluids and CLGG is made. The reservoir geology, fluid type, and operation data of fracturing, and well performances from more than 300 successful wells in three different low permeability shallow oil reservoirs (800 ∼ 1,500 m depth) are also presented in detail. Introduction Hydraulic fracturing is a necessary technique to develop low permeability reservoirs. Fracturing fluid takes a paramount role in fracturing treatments. In most cases, cross-linked fluids (CLF) can meet the treatment requirements. However, severe formation damage frequently occurred when cross-linked guar gel (CLGG) was used. The core flow tests by Devine et al.(1) showed that the permeability reduction by CLGG was 56% ∼ 71% for cores of 100 ∼ 200 mD, and 12% ∼ 35% for cores of less than 1 mD. The fluid damage to the tight sand was less severe than that of a permeable formation(1, 2). The experiment study by Almond(3) at 120 ° F with 20/40 mesh sand packs (simulating fractures) showed that the flow reduction by CLGG damage could be as high as 100%. Without doubt, damage of this order of magnitude will greatly decrease the productivity of fractured wells, whether the damage happens in formations or in propped fractures. Therefore, fluid impairment by CLGG could be a significant problem adversely influencing the success of fracturing operations. Fluid cost is another critical factor of fracturing, especially in low permeability and tight reservoirs where the job size is large and the fluid cost is high, but the well productivity is low and declines faster than that of fractured wells in conventional reservoirs. Fluid cost saving is a key factor to economically develop these reservoirs, in particular when they are marginal reservoirs.

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
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.011
GPT teacher head0.229
Teacher spread0.218 · 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

Citations3
Published2003
Admission routes1
Has abstractyes

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