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Record W2604824834 · doi:10.2118/185724-ms

Fracture Propagation, Leakoff and Flowback Modeling for Tight Oil Wells Using the Dynamic Drainage Area Concept

2017· article· en· W2604824834 on OpenAlexafffund
Christopher R. Clarkson, Farhad Qanbari, J. D. Williams-Kovacs, Behnam Zanganeh

Bibliographic record

VenueSPE Western Regional Meeting · 2017
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsUniversity of Calgary
FundersAlberta Innovates - Technology Futures
KeywordsPetroleum engineeringTight oilHydraulic fracturingGeologyFracture (geology)Well stimulationInitializationDrainageOil shaleTight gasDirectional drillingGeotechnical engineeringReservoir engineeringDrillingEngineeringPetroleumComputer science

Abstract

fetched live from OpenAlex

Abstract Recently it has been demonstrated that flowback data obtained immediately after fracture stimulation of multi-fractured horizontal wells (MFHWs) completed in tight/shale reservoirs may be analyzed quantitatively for hydraulic fracture properties. However, the initial conditions of fluid pressures and saturations at the start of flowback, which are a critical starting point for flowback simulation, are generally unknown and must be approximated. In order to properly initialize flowback simulations, the pre-flowback fracture stimulation treatment, as well as post-treatment shut-in period, should first be modeled in order to provide a reasonable estimate of fluid pressures and saturations within the hydraulic fracture and adjacent reservoir. In recent work, the authors developed a semi-analytical model to history-match flowback and early-time production data of MFHWs completed in tight oil reservoirs using the "dynamic drainage area" (DDA) concept. The model assumes that each fracture stage consists of a primary hydraulic fracture (PHF) region, and an adjacent enhanced fracture region (EFR) or non-stimulated region (NSR) in the reservoir. However, initial fracture fluid pressure in the PHF, and fluid pressure/saturation distributions in the adjacent EFR/NSR are required for the model initialization and are highly uncertain. In the current work, flowback data from a previously-analyzed MFHW horizontal well completed in a tight oil reservoir is revisited to determine if flowback initial conditions could be constrained rigorously. For this purpose, frac modeling (net-pressure analysis) was first performed using fracture treatment data for the well and commercial and publically-available simulators to constrain PHF property input for the DDA flowback model. The DDA model, run in injection mode, was then used to calculate the frac fluid leakoff rate from the PHF to the NSR during the fracture treatment, using the field frac pressures as input. Importantly, leakoff is modeled more rigorously using the DDA model than for the frac simulator, because it accounts for two-phase flow and stress-dependent permeability. Leakoff after the fracture treatment and before the flowback period was also modeled using the DDA approach to estimate the pre-flowback NSR fluid saturations and pressures, which served as the initial conditions for flowback modeling. The amount of leakoff estimated with the model is relatively small in this case, in part due to the small volumes of fluid used in the fracture treatment and low permeability of the reservoir. The resulting flowback history-match (also performed using the DDA model, in flowback mode) is similar to that achieved previously because the pre-flowback leakoff modeling resulted in only a slightly elevated water saturation estimate over virgin reservoir conditions. The results of this innovative approach to flowback modeling should be of interest to those petroleum engineers interested in quantitative analysis of flowback data to obtain fracture properties, but who are concerned about correct initialization of models for flowback simulation leading to more realistic results.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.026
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
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 head0.023
GPT teacher head0.261
Teacher spread0.238 · 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 source (direct Gemma or distilled Codex), 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

Citations29
Published2017
Admission routes2
Has abstractyes

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