MétaCan
Menu
Back to cohort
Record W2024687776 · doi:10.2118/01-12-das

Economic Optimum Operating Pressure for SAGD Projects in Alberta

2001· article· en· W2024687776 on OpenAlexaboutno aff
Neil Edmunds, Harbir Chhina

Bibliographic record

VenueJournal of Canadian Petroleum Technology · 2001
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsnot available
Fundersnot available
KeywordsSteam-assisted gravity drainagePetroleum engineeringOil sandsDrainageSteam injectionEngineeringFossil fuelEnvironmental scienceAsphaltWaste management

Abstract

fetched live from OpenAlex

Introduction During the recent Surmont gas-over-bitumen Alberta Energy and Utilities Board (EUB) hearing, a number of technical issues regarding the optimum operating pressure for steam assisted gravity drainage (SAGD) schemes were raised(1). From the evidence presented, the EUB concluded that there was a detrimental effect on SAGD recovery with decreasing gas pool pressure. The objective of this paper is to present technical and practical evidence of what is the most economical optimum operating pressure for a SAGD project in northeast Alberta. We believe that the effect of operating pressure on SAGD recovery has been misunderstood, and that economics will in fact drive SAGD producers to develop low pressure recovery technology over time, regardless of the presence or status of contiguous gas caps. We first present historical evidence that the high recoveries associated with SAGD will remain so at very low pressures. Secondly, analytical predictions of oil rate and steam-oil ratio (SOR) vs. pressure are considered. Finally, the effects of pressure on full-cycle scheme design and economics are calculated, based on simulation studies typical of oil sands reservoirs in northeast Alberta. The results support an economic optimum SAGD pressure in the range of 300 – 900 kPa. Historical Steam Assisted Gravity Drainage Experience and developed in Alberta. In reality, although Alberta may have been the first horizontal well application of SAGD technology, there are other examples of SAGD projects using vertical wells. In California, this has normally been referred to as steam flooding; but in a large number of those fields, gravity drainage has been a dominant recovery mechanism in comparison to other mechanisms, such as steam drag, steam drive, solution gas drive, thermal expansion, and steam distillation. Steam assisted gravity drainage in these fields has resulted in average oil recoveries exceeding 50% and as high as 85% OOIP(2–4). An example is the Kern River field in California, one of the largest steam floods in the world. The formation depth varies from 300 – 1,400 ft. (90 – 430 m), initial reservoir pressure is 50 – 140 psig (345 – 965 kPag), oil gravity varies from 9 – 16 °CDATA [API, porosity is 35% and permeability varies from 1,000 – 7,600 mD(4, 5). At Kern River, the primary production recovery ranged from 10 – 13% OOIP, but steam flood recovery ranges from 43 to 73 % of the pre-steam oil in place. Most importantly, the normal operating steam zone pressure during infill drilling was measured to be 6 psia (180 kPaa)(2). Another successful field example reported steam zone pressure of 40 psia (275 kPaa)(2). It is the combination of very high recoveries and very low pressures which makes the case for gravity dominance in these cases; there are really no other plausible candidate mechanisms. The low operating pressures also give a clue as to the economics of low pressure gravity drainage: apparently, the operators consider the additional steam capacity they would require to raise the pressure to be an unattractive investment. Greaser and Shore(5) reported on a number of steam flooding expansion projects in the Kern River field.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.803
Threshold uncertainty score0.392

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.011
GPT teacher head0.241
Teacher spread0.230 · 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 source (direct Gemma or distilled Codex), 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

Citations115
Published2001
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Canadian Petroleum TechnologySame topicReservoir Engineering and Simulation MethodsFrench-language works237,207