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Record W2316896716 · doi:10.2118/175941-ms

Parameters Affecting Load Recovery and Oil Breakthrough Time after Hydraulic Fracturing in Tight Oil Wells

2015· article· en· W2316896716 on OpenAlexafffund
Behnam Zanganeh, Mohammad Soroush, J. D. Williams-Kovacs, Christopher R. Clarkson

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaAlberta Innovates - Technology Futures
KeywordsPetroleum engineeringHydraulic fracturingPermeability (electromagnetism)GeomechanicsCompressibilitySaturation (graph theory)Volumetric flow rateCapillary pressureFluid dynamicsPorous mediumRelative permeabilityGeotechnical engineeringGeologyPorosityMechanicsChemistry

Abstract

fetched live from OpenAlex

Abstract After hydraulic fracture treatment, wells typically produce significant volumes of water, which is primarily recovered fracturing fluid. The amount of fracturing fluid produced back is called load recovery. Single phase water production may continue for several days before oil breakthrough from the formation occurs. A wide range of load recoveries (15%–80%) are reported in the literature, depending on various reservoir, fracture, pumping and operational parameters. This study conducts a comprehensive numerical and statistical analysis to identify the most important parameters affecting load recovery. The sensitivity of oil breakthrough time to these parameters is also investigated. A fit-for-purpose model was created using a commercial coupled reservoir flow/geomechanics simulator to simulate fracturing treatment and flowback operations. Water and oil production trends, and flow regimes observed in the model, are verified using rate-transient analysis (RTA). The effect of twenty parameters including pumping rate, shut-in time, producing bottomhole pressure, production rate constraint, matrix porosity and permeability, matrix and hydraulic fracture compressibility, relative permeability end points and capillary pressure on load recovery and oil breakthrough time is investigated by utilizing Design of Experiment (DOE). The developed model considers non-uniform leak-off during pumping, providing a better understating of water retention, while the application of the DOE method allows the effect of various parameters and their interaction to be studied rigorously. A critical finding is that the majority of total load recovery occurs within the first three months of production. Furthermore, matrix permeability, shut-in time, connate water saturation and fracture compressibility, were found to be the most dominant factors, which can impact the load recovery by up to 22%, 15%, 14%, and 13%, respectively. Matrix permeability, production rate constraint, fracture residual oil saturation and shut-in time are four of the prominent parameters affecting oil breakthrough time. Understanding the impact of operational parameters such as shut-in time and production rate can help industry to implement recycling/disposal options and ultimately optimize load recovery. This can also create significant environmental compliance benefits for operators in addition to benefitting operational efficiency.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.007
GPT teacher head0.199
Teacher spread0.191 · 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 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

Citations15
Published2015
Admission routes2
Has abstractyes

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