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Record W4414447181 · doi:10.2118/226604-ms

Introducing a Customized Workflow to Analyze DFIT-FBA Post-Flowback Shut-In (Rebound) Data

2025· article· en· W4414447181 on OpenAlexaff
S. Haqparast, Christopher R. Clarkson

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHeat transfer and supercritical fluids
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsWorkflowWell test (oil and gas)Hydraulic fracturingTest dataFlow (mathematics)Hydrostatic testField (mathematics)Reservoir simulation

Abstract

fetched live from OpenAlex

Abstract DFIT-FBA (FBA = flowback analysis) offers a faster alternative to traditional diagnostic fracture injection tests (DFITs) for determining key parameters used in hydraulic fracturing design. While a method exists for estimating reservoir pressure from the flowback period of DFIT-FBA, this method could be inaccurate for certain cases. In this study, shut-in data (also called rebound) recorded after the flowback period of the DFIT-FBA test were analyzed to provide an independent estimate of reservoir pressure. An extensive DFIT-FBA post-flowback shut-in test was simulated and analyzed to interpret all pressure signatures. This detailed investigation provided valuable insights into physical mechanisms occurring during the post-flowback shut-in period of a DFIT-FBA test. Based on these findings, a customized analysis workflow was developed and then utilized to estimate reservoir pressure from post-flowback shut-in data for nine field cases. These results were then compared with the results obtained from the analysis of the flowback period of DFIT-FBA. Key observations from the simulation study include: 1) linear flow is the only reservoir flow regime observed during the post-DFIT-FBA shut-in data of the simulated case; 2) reservoir pressures estimated from the shut-in test are always higher than the actual reservoir pressure even after an extended shut-in period. The major takeaways from analyzing field examples include: 1) shut-in pressures recorded after a DFIT-FBA test reach a maximum, and will decline if pressure is monitored long enough; 2) more accurate reservoir pressure estimates will be obtained if the test is extended far beyond the point where maximum pressure is observed; 3) if the minimum horizontal stress and reservoir pressure are similar in magnitude, and/or occur close in time, the reservoir pressure signature during the flowback period of a DFIT-FBA could be masked by the fracture closure event. In such cases, the reservoir pressure estimate obtained from the analysis of DFIT-FBA flowback data should be used with caution, unless confirmed through independent estimates, such as DFIT-FBA post-flowback shut-in data.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.767
Threshold uncertainty score0.873

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.265
Teacher spread0.254 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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