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Record W2209504942 · doi:10.2118/168598-pa

Fracture Characterization Using Flowback Salt-Concentration Transient

2015· article· en· W2209504942 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSPE Journal · 2015
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsNexen (Canada)University of Alberta
Fundersnot available
KeywordsSalinityShale gasPlateau (mathematics)GeologyFracture (geology)Environmental scienceHydrology (agriculture)Oil shalePetroleum engineeringSoil scienceGeotechnical engineeringMathematics

Abstract

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Summary As observed in many shale-gas operations, salt concentration of flowback water increases with time. Usually, the shape of salt-concentration/load-recovery plots is different from one well to another. We hypothesize that the shape of the salinity profile during the flowback process provides useful information about the complexity of the fracture network. In this study, we propose a model to describe the relationship between salinity and cumulative water production. We also compare the model results and flowback-salinity data to characterize the fracture network. Flowback-salinity data are collected from three multifractured horizontal wells completed in the three shale members [Muskwa (Mu), Otter-Park (OP), and Evie (Ev)] of the Horn River Basin. The salinity profiles for the Mu and OP wells initially increase and finally reach a plateau, whereas the salinity profile for the Ev well shows a continuous increase and does not show a plateau. We hypothesize that the early water with lower salt concentration at the onset of the flowback process is mainly produced from the primary fractures with larger aperture size. Also, we believe that the fractures with smaller aperture size become more important as the flowback process progresses, and therefore, the high-salinity water produced at later times is mainly produced from secondary fractures. We also propose a model to describe the salinity-profile behaviors. The model presents the aperture-size distribution (ASD) of the fracture network. A comparative analysis of the model results and the flowback-salinity data indicates that the Ev well with a steady increase in its salinity profile has a wider ASD compared with the Mu and OP wells with a plateau in their salinity profiles. This suggests that the fracture network is more complex in Ev compared with those in Mu and OP. More-complex fracture network in Ev is also in agreement with its higher gas and lower water recovery during the flowback process as opposed to the lower gas and higher water recovery in Mu and OP. The presented model for describing the behavior of the salinity profile during the flowback process and its meaningful relationship to the fracture-network complexity provide an alternative approach for reservoir characterization. This study encourages the industry to manage the flowback operations carefully and to monitor the water chemistry.

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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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.349
Threshold uncertainty score0.362

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)0.0000.000

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 head0.015
GPT teacher head0.230
Teacher spread0.215 · 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