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Record W2991505356 · doi:10.2118/1219-0060-jpt

Automated Approach Optimizes Flow-Control Device Placement in SAGD Completions

2019· article· en· W2991505356 on OpenAlexaboutno aff
Judy Feder

Bibliographic record

VenueJournal of Petroleum Technology · 2019
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsnot available
Fundersnot available
KeywordsSteam-assisted gravity drainageFluvialPoint barSteam injectionOil sandsPetroleum engineeringReservoir simulationGeologyAsphaltCompletion (oil and gas wells)Hydrology (agriculture)Geotechnical engineeringPaleontologyStructural basinArchaeology

Abstract

fetched live from OpenAlex

This article, written by JPT Technology Editor Judy Feder, contains highlights of paper SPE 193364, “Optimization of Placement of Flow-Control Devices Under Geological Uncertainty in Steam-Assisted Gravity Drainage,” by Siavash Nejadi, Stephen M. Hubbard, Roman J. Shor, SPE, Ian D. Gates, SPE, and Jingyi Wang, SPE, University of Calgary, prepared for the 2018 SPE Thermal Well Integrity and Design Symposium, Banff, Alberta, Canada, 27–29 November. The paper has not been peer reviewed. Steam-chamber conformance in steam-assisted gravity drainage (SAGD) influences the efficiency and economic performance of bitumen recovery. Conventional SAGD well-completion designs provide limited control points in long horizontal well pairs, leading to development of nonideal steam chambers. The complete paper presents an automated approach to optimizing placement of flow-control devices (FCDs) in SAGD well-pair completions. The methodology uses a coupled wellbore/reservoir model to simulate both reservoir fluid-flow behavior and detailed wellbore hydraulics. The qualities of the well-completion-design parameters and their effect on production are assessed by calculating the net present value (NPV), which is considered the basis for•optimization. Introduction The Lower Cretaceous McMurray formation hosts the majority of bitumen in the Athabasca oils sands—the largest known resource of bitumen. The formation is composed of large-scale fluvial-estuarine point bars and other laterally accreting channel systems that are highly heterogeneous. The formation has been interpreted as having three stratigraphic subdivisions: a lower continental (fluvial), a middle fluvial-estuarine unit (point-bar dominated), and an upper marginal marine deposit. The repeated erosional cut and fill events within the McMurray have led to nested and multiple stacked structures. Similarly, laterally accreting channel systems, such as point-bar deposits consisting of inclined heterolithic strata of sandwiched sand-siltstone sequences and abandoned mud channels, lead to very complex sedimentary facies relationships in which rock types change both laterally and vertically over very short distances. SAGD completions offer the most promise of producing bitumen resources from Athabasca oil-sands deposits. The SAGD well configuration typically consists of two parallel horizontal wells within a short distance, one above the other. Steam is injected into the upper well and fluids are produced from the lower well. These design schemes provide limited control of steam injection and liquid production along horizontal sections and adversely affect steam-chamber conformance. Operational difficulties, drilling, well completion, and reservoir parameters moderate overall SAGD performance. More specifically, these factors include hydraulic gradients and pressure drop in the tubulars, injectivity and productivity variations along the wellbore from plugging and formation damage, heat exchange, energy loss to bottom water, nonparallel well-pair placement, well undulation, and—most importantly—reservoir heterogeneity and structure. The conventional SAGD well completion needs to be modified to deliver steam efficiently throughout the reservoir interval, improve liquid-production performance, and minimize steam breakthrough.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.192
Threshold uncertainty score0.553

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.250
Teacher spread0.240 · 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".

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Citations0
Published2019
Admission routes1
Has abstractyes

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