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Record W2003988272 · doi:10.2118/137579-ms

Numerical Simulation and Optimization of the SAGD Process in Surmont Oil Sands Lease

2010· article· en· W2003988272 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueAbu Dhabi International Petroleum Exhibition and Conference · 2010
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsSteam-assisted gravity drainagePetroleum engineeringOil sandsGeologySteam injectionComputer simulationReservoir simulationFossil fuelAsphaltEnvironmental scienceEngineeringWaste management

Abstract

fetched live from OpenAlex

Abstract Optimization of Steam Assisted Gravity Drainage (SAGD) remains a major concern in Surmont leases (an Athabasca oil sands deposit located in northeastern Alberta, Canada) due to the extensive presence of top gas and top water zones over the bitumen. Observation well data has detected the pressure communication between the SAGD steam chamber and overlying thief zones. Maintaining the steam chamber pressure is very difficult due to these thief zone interactions. Previous numerical simulations, laboratory experiments and field production data have demonstrated that the overlying top water and gas thief zones have a detrimental effect on the SAGD process. Oil production and steam oil ratios tend to decrease as the depletion of top gas continues. Also, the heat loss to the overlying thief zone will be more significant when the top water zones are present. However, an optimal operating strategy for the full field scale SAGD process with both top gas and top water remains uncertain. In addition, a detailed investigation of the impact of top gas and water thief zones on SAGD performance provides the basis in calibration of geostatistical and flow models for commercial phase planning and forecasting. The objective of this paper is to construct numerical flow simulation of a Surmont pilot using a well-defined 3D geostatistical model to determine the impact of the top thief zones on bitumen recovery. The focus of the study will be on three horizontal well pairs plus 15 vertical observation wells at the McMurray formation. The stochastic geostatistical model is to build a representation of the McMurray geology that honors the deposition structure, facies proportions, reservoir characteristics and petrophysical properties. Structural tops are interpreted from well logs and porosity-permeability relationships established from quantitative log analysis and core-log calibration. The facies-based log-derived porosity, permeability, shale volume and water saturation are populated into a grid block by Sequential Gaussian Simulation (SGS) in the petrophysical modeling process. Then a static model is upscaled to coarse simulation grids, and a submodel for each single well pair is extracted for the purpose of history match in STARS™ simulator. Reasonable history match of oil and water rates has been achieved by calibrating this static model with the field production data. The steam chamber pressure and temperature profile from the numerical model has been conformed to the field data from the observation wells. Optimization of cumulative steam oil ratios (cSOR) by varying injection pressure and the steam trap control with the top thief zones has been investigated in great detail. In conclusion, SAGD performance is dominantly controlled by geological heterogeneity, completion and operation constrains, and steam chamber pressure variations. Finally, integrated optimization strategies have been developed and tested on a full field-based heterogeneous simulation model.

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.

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 score0.083
Threshold uncertainty score0.337

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)0.0000.000

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 head0.012
GPT teacher head0.264
Teacher spread0.252 · 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