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Record W3125617486 · doi:10.2118/204481-pa

Average Fracture Compressibility from Flowback Data

2021· article· en· W3125617486 on OpenAlexaff
Sabbir Hossain, Obinna Ezulike, Yingkun Fu, Hassan Dehghanpour

Bibliographic record

VenueSPE Production & Operations · 2021
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsCompressibilityFracture (geology)Hydraulic fracturingVolumetric flow rateGeologyFlow (mathematics)Geotechnical engineeringSoil scienceMechanicsPetroleum engineeringMineralogyPhysics

Abstract

fetched live from OpenAlex

Summary We propose a novel method for estimating average fracture compressibility cf¯ during flowback process and apply it to flowback data from 10 multifractured horizontal wells completed in Woodford (WF) and Meramec (MM) formations. We conduct complementary diagnostic flow-regime analyses and calculate cf¯ by combining a flowing-material-balance (FMB) equation with pressure-normalized-rate (PNR)-decline analysis. Flowback data of these wells show up to 2 weeks of single-phase water production followed by hydrocarbon breakthrough. Plots of water-rate-normalized pressure and its derivative show pronounced unit slopes, suggesting boundary-dominated flow (BDF) of water in fractures during single-phase flow. Water PNR decline curves follow a harmonic trend during single-phase- and multiphase-flow periods. Ultimate water production from the forecasted harmonic trend gives an estimate of initial fracture volume. The cf¯ estimates for these wells are verified by comparing them with the ones from the Aguilera (1999) type curves for natural fractures and experimental data. The results show that our cf¯ estimates (4 to 22×10−5 psi−1) are close to the lower limit of the values estimated by previous studies, which can be explained by the presence of proppants in hydraulic fractures.

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 categoriesInsufficient payload (model declined to judge)
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 score0.785
Threshold uncertainty score0.999

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.023
GPT teacher head0.259
Teacher spread0.236 · 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.

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

Citations4
Published2021
Admission routes1
Has abstractyes

Explore more

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