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Record W3207927074 · doi:10.2118/206635-ms

Field Testing of the Flowback Technology for Multistage-Fractured Horizontal Wells: Generalization to Find an Optimum Balance Between Aggressive and Smooth Scenarios

2021· article· en· W3207927074 on OpenAlex
Albert Vainshtein, Georgii Fisher, Gleb Strizhnev, С. А. Боронин, Andrei Osiptsov, Ildar Radikovich Abdrakhmanov, Gregory Paderin, Alexander Sergeevich Prutsakov, Ruslan Uchuev, И. А. Гарагаш, Kristina Tolmacheva, Egor Shel, Dmitry Prunov, Nikolay Chebykin, Ildar Fayzullin

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 Russian Petroleum Technology Conference · 2021
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsOptech (Canada)
Fundersnot available
KeywordsGeomechanicsHydraulic fracturingPetroleum engineeringFracture (geology)GeologyRheologyPressure dropTight gasGeotechnical engineeringMechanicsMaterials science

Abstract

fetched live from OpenAlex

Abstract We present the results of field experiments campaign on start-up of wells located in a sandstone oilfield of Western Siberia and history matching of coupled "wellbore-hydraulic fracture" model describing well start-up and fracture clean-up. The conclusion is made about the impact of rheological and geomechanical factors on the well cumulative production andfracture conductivity.The results are generalized for four wells of the field experiment and 30 wells of the retrospective analysis. Calculations of well startup are carried out using standalone fracture cleanup model and the coupled model, which includes models for filtration inside closed hydraulic fracture and flow in the wellbore. The data obtained during field tests on well startup is used to history match the fracture clean-up model. The adaptation allows to evaluate the sensitivity of well production to various physical parameters and find the safe operating envelope of operational parameters during well startup. Numerical simulations allow take into account geomechanics effectsand rheology properties of fracturing fluid, study the dynamics of effective (cleaned) fracture length as well as evaluate the influence of pressure drop dynamics on filtration properties of the fracture and cumulative well production. We extended the number of wells to study the impact of flowback scenarios on production andgeneralized the results of our previous study.Key parameters affecting the history match process of the mathematical model are determined,the uncertainty associated with fluid rheology is reduced. Using the history-matched model, we evaluated geomechanics effects on fracture degradation depending on bottom-hole pressure drop dynamics. Based on the obtained dynamics of dimensionless parameters, such as pressure and fracture productivity, we propose an optimized well start-up strategy aimed at maximizing effective fracture length and cumulative production. Additionally, we visualized the dynamics of fracture conductivity distribution along its length. The obtained results are consistent with interpretation of physical processes accompanying well start-up and fracture clean-up. Dimensionless productivity index is chosen to quantify the effects of geomechanics and fluid rheology on well production.On the basis of matched mathematical model, we predict a potential increase in production of the well with optimized start-up.The recommendations are presented in the form of the dynamics of wellhead choke opening and a sequence of choke diameters. We propose an integrated approach for planning a well flowback strategy after multi-stage hydraulic fracturing. The proposed decision-making algorithm considers the effects of geomechanics and yield-stress hydraulic fracturing fluid rheology on cumulative production. It allows to develop a design for the well start-up and fracture cleanup in terms of dynamics of wellheadchoke opening.

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.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.282
Threshold uncertainty score0.836

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 integrity0.0010.000
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 head0.011
GPT teacher head0.237
Teacher spread0.227 · 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