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Record W4244431052 · doi:10.2118/2007-166

Advances in SAGD Drilling in Western Canada Using Innovative Bit Technology

2007· article· en· W4244431052 on OpenAlexaboutno aff
A.O. Okusanya, D.V. Roberts, J.D. Dach

Bibliographic record

VenueCanadian International Petroleum Conference · 2007
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsnot available
Fundersnot available
KeywordsDrillingBit (key)Petroleum engineeringComputer scienceGeologyEngineeringMechanical engineeringComputer security

Abstract

fetched live from OpenAlex

Abstract Western Canada's massive oil sands are being exploited at deeper levels by steam-assisted gravity-drainage (SAGD). Carefully geosteered parallel pairs of large-diameter horizontal well bores are drilled for concurrent steam injection into the upper well with oil production from the lower. The bitumen-cemented sands of the McMurray Formation have challenged both operational and engineering personnel responsible for drilling these challenging wells. Bits of all designs have required custom features to combat the highly abrasive sands with sloughing and hole cleaning problems while delivering an optimally steered well path through tight reservoir tolerances. A cross-functional team of operators' engineers and drilling personnel, together with field engineers, bit designers, field, and office personnel from the bit manufacturer, came together to analyze the challenges and fast-track fit-for-purpose solutions for all SAGD projects in Alberta. Steel teeth roller cone bits require extensive extra-thick layers of hard-facing to resist tooth wear. Sand washing through the cones causes high levels of erosion which is hindered by extensive hard-facing. The gauge areas of the bits are rotated and/or slid through a cuttings bed of coarse abrasive sand particles, resulting in rounded gauge and shirttail wear, which again require tungsten carbide and hardfacing wear pads and shirt-tail protection. Elastomer or metalfaced seals have been enhanced to prevent sand encroachment on the bearings during motor drilling. PDC bits were also faced with significant erosional problems drilling horizontally which have required special wear-pads and updrill features to ensure hole gauge, steerability, and the ability to back-ream through the cuttings bed. Depth of cut features for build up rates of 9 degrees per 30 meters ensure steerability when linked to wear resistant cutters, especially when placed on the gauge. Spiral gauge features work like an auger to clean sand away from the bit, while customized gauge lengths ensure a match between bit and motor for optimised steerability. Collectively, these PDC and steel-tooth bits have become the enabling technology for drilling and completing SAGD wells, especially now that bits can be rerun on future wells, further reducing cost per metre. Introduction Steam Assisted Gravity Drainage (SAGD) is the most popular enhanced oil recovery technology being adopted by Canadian heavy oil producers. It is very effective in mobilizing bitumen and achieving high recovery from thick, high permeability reservoirs (low gravity <10 deg API). An estimated 174 billion barrels of oil in the Athabasca, Cold Lake and Peace River deposits are potentially recoverable with the present technology.1,2,3 However, with technological improvements Canada oil sands reserves could be close to 315 billion barrels. The biggest deposit is the Athabasca which is thicker and shallower while the Cold Lake deposit is thinner and deeper. Surface mining is only feasible for recovering 10% f the oil sands deposits between 70–75metres of surface. SAGD is the current method of choice to access resources too deep to mine and will recover the potential 90% of the remaining oils and deposits (with the cyclic steam stimulation- CSS).

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 score0.336
Threshold uncertainty score0.773

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.009
GPT teacher head0.219
Teacher spread0.210 · 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

Citations4
Published2007
Admission routes1
Has abstractyes

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