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Record W2069006571 · doi:10.1190/tle33101108.1

Innovative use of rate-transient analysis methods to obtain hydraulic-fracture properties for low-permeability reservoirs exhibiting multiphase flow

2014· article· en· W2069006571 on OpenAlexafffund
Christopher R. Clarkson, Farhad Qanbari, J. D. Williams-Kovacs

Bibliographic record

VenueThe Leading Edge · 2014
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsUniversity of Calgary
FundersAlberta Innovates - Technology Futures
KeywordsHydraulic fracturingPetroleum engineeringFracture (geology)Permeability (electromagnetism)Multiphase flowWell stimulationComplex fractureHydraulic conductivityGeologyGeotechnical engineeringVolumetric flow rateMechanicsEnvironmental scienceMaterials scienceSoil scienceReservoir engineeringPetroleumChemistry

Abstract

fetched live from OpenAlex

Abstract Multifractured horizontal wells, while enabling commercial production from unconventional gas and light-oil reservoirs, are challenging to analyze quantitatively to obtain reservoir and hydraulic-fracture properties. Production rates and flowing pressures gathered immediately after hydraulic-fracture stimulation (flowback) and over a longer time period (on-line production) can be interpreted for hydraulic-fracture properties such as fracture surface area or half-length and fracture conductivity, but dynamic fracture properties and multiphase flow during both stages of production can complicate the analysis. Recent studies have suggested that flowback data can provide early insight into these fracture properties if high-resolution fluid rates/pressures are gathered, but the physics of the process are complex, and analytic methods for interpretation are at an early stage of development. Analytic methods for longer-term production data analysis, although better established, are still limited primarily to single-phase flow and simple fracture and reservoir behavior. Rate-transient methods can be applied to both flowback and long-term production data to quantify hydraulic-fracture properties and changes in effective hydraulic-fracture length during production. For the flowback period, simplified analytic methods have been developed for before-breakthrough production of hydraulic-fracturing fluids and after-breakthrough production of hydraulic-fracture and reservoir fluids. These methods are still under development, and early applications can be illustrated. For long-term production analysis, classic rate-transient analysis techniques, such as the square-root-of-time plot, have been modified to account for multiphase flow and stress-sensitive permeability exhibited by low-permeability gas condensate and black-oil reservoirs producing below saturation pressure. A field example consisting of a multifractured horizontal well completed in a tight-oil reservoir isused to to compare hydraulic-fracture properties derived from flowback and long-term production data. Although flowback analysis yields hydraulic-fracture half-lengths consistent with hydraulic-fracture modeling results, long-term production analysis yields much smaller fracture half-lengths, possibly because of breakthrough of gas once bubble-point pressure is reached. Additional mechanisms for effective producing fracture half-length reduction can be proposed. Another important observation is that estimation of fracture properties from long-term production analysis of a reservoir producing below saturation pressure can be in significant error if the estimates are derived from techniques assuming single-phase flow. Corrections to the analytic methods for multiphase flow yield fracture half-lengths consistent with those obtained from history matching using rigorous numerical simulation. In a preliminary analysis, the techniques under development are intended to aid hydraulic-fracture evaluation and design in tight reservoirs that exhibit complex flow characteristics. Furthermore, implications of the findings will be important to assist in designing well operations to maximize well performance.

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)0.003
metaresearch head score (Gemma)0.003
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: Empirical · Consensus signal: none
Teacher disagreement score0.525
Threshold uncertainty score0.906

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.035
GPT teacher head0.298
Teacher spread0.264 · 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
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

Citations40
Published2014
Admission routes2
Has abstractyes

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