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Record W1979136713 · doi:10.2118/2009-047

Estimation of Steam-Chamber Extent Using 4D Seismic

2009· article· en· W1979136713 on OpenAlexaboutno aff
Motonao Tanaka, Kazunori Endo, Shigenobu Onozuka

Bibliographic record

VenueCanadian International Petroleum Conference · 2009
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Imaging and Inversion Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsEstimationGeologyEnvironmental sciencePetroleum engineeringEngineering

Abstract

fetched live from OpenAlex

Abstract The Steam-Assisted Gravity Drainage (SAGD) process has been successfully implemented to produce ultra-viscous bitumen from the Athabasca oil-sands in the Province of Alberta, Canada. In the Hangingstone area, 15 pairs of SAGD wells had been drilled by 2006 in the reservoir of maximum 30 m thickness and about 300 m depth. The production reached an average of 8,000 BOPD in recent years. The reservoir is geologically characterized as stacked incised valley fills in fluvial to upper-estuarine channels. Thin mudstone layers and abrupt changes in facies caused by the sedimentary deposits present complexities and difficulties for SAGD implementation. A 3D seismic survey was conducted in 2002 to obtain a clear view of geology that was fully utilized for planning additional wells. In order to evaluate SAGD efficiency and performance, a time-lapse 3D seismic survey was carried out in 2006. In this paper, P-wave velocity (Vp) maps transformed from the seismic travel-time maps were interpreted with a new methodology for evaluating the areal extent of the steamchamber zone created by the SAGD process. In the previous experimental study of seismic velocity measurements with oilsands cores, Vp was found to steeply drop with an increase in temperature and to gently decrease with an increase in pore pressure. Based on the experimental results, a petrophysical model was formulated to express Vp as a function of temperature, pressure, and water saturation. The high pressure and high temperature zone of the SAGD process should generate differences between the first (2002) and second (2006) Vp maps from which we can estimate the area of the reduced bitumen viscosity with a temperature increase. As effects of pressure are probably more areally extensive than effects of temperature, these two effects on the Vp maps need to be segregated. As a new method, a scaling factor for the Vp reduction was first estimated to adjust the laboratory scale and field scale. We then calculated a distribution of Vp reduction corresponding to steam-chamber conditions in order to decouple composite effects of temperature and pressure based on the petrophysical model. Distinguishing high temperature and high pore-pressure zone from low temperature and high pore-pressure zone, we could determine a steam-chamber distribution. The bitumen volume in the steam-chamber zone was estimated and compared with the actual production. The methodology, interpretation procedures, and the results obtained are presented in detail. Introduction Although the oil price has dropped rapidly after the financial crisis in 2008, the development of unconventional oil resources like extra-heavy oil and oil shale still remains significant in the quest for increasing reserves or energy security (Stark et al. 2008). Such bituminous and heavy oil, however, complicates their production using normal techniques. Then, a variety of methods are developed to decrease high viscosity. The injection of heat or solvents is used extensively. The SAGD technique which was first designed by Butler (1992, 1994) is one of the most effective steam injection methods and it has been widely applied in Canadian oil-sand reservoirs. The steam movement is highly influenced by complex substructure in reservoirs.

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 categoriesInsufficient payload (model declined to judge)
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 score0.472
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.022
GPT teacher head0.238
Teacher spread0.216 · 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.

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

Citations19
Published2009
Admission routes1
Has abstractyes

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