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Record W2986526343 · doi:10.2118/198697-ms

Effective Reservoir Management with Flow Control Devices for SAGD Producer Wells in Mackay River

2019· article· en· W2986526343 on OpenAlexaff
Giuseppe Rosi, Da Zhu, Dermot O'Hagan

Bibliographic record

VenueSPE Thermal Well Integrity and Design Symposium · 2019
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsSuncor Energy (Canada)
Fundersnot available
KeywordsInflowDrawdown (hydrology)Petroleum engineeringOil fieldCurrent (fluid)Oil wellEnvironmental scienceGeologyGeotechnical engineeringAquifer

Abstract

fetched live from OpenAlex

Abstract Inflow Control Devices (ICDs) have been adopted for commercial steam-assisted gravity drainage (SAGD) production for nearly ten years and yet the function they serve is not well understood, and field data evaluating their performance remains scant. Thus, the purpose of the current study is twofold: Firstly, the study derives a simplified analytical model demonstrating how increasing the dP across ICDs acts to improve conformance along a producing lateral. The resulting equation of the analysis acts as a simple rule of thumb for determining an appropriate pressure drop across ICDs to achieve conformance. Secondly, the study evaluates the performance of ICDs that had been installed in four wells, two of which had ICDs installed prior to circulation and two that adopted ICDs later in their lifecycle. The field data shows that ICDs increase production rates and improve conformance along the lateral. These improvements are achieved by an increased drawdown facilitated by the ICDs. This part of the study highlights how early-life results may differ between ICD bearing wells compared to their conventionally completed (slotted liner) offsets: ICD bearing wells exhibit improved conformance and an ability to develop more challenging reservoir resulting in different oil production profiles and composite SORs.

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)0.001
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 score0.428
Threshold uncertainty score0.816

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.010
GPT teacher head0.235
Teacher spread0.225 · 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

Citations3
Published2019
Admission routes1
Has abstractyes

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Same venueSPE Thermal Well Integrity and Design SymposiumSame topicReservoir Engineering and Simulation MethodsFrench-language works237,207