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Record W4323655454 · doi:10.2118/212820-ms

Heavy Oil Late Life Energy Recovery—Maximizing the Value of Mature Thermal Assets

2023· article· en· W4323655454 on OpenAlexaff
Ivan Beentjes, Dmitry Bogatkov

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsSuncor Energy (Canada)
Fundersnot available
KeywordsTailingsElectricity generationOil sandsWaste managementEnvironmental sciencePetroleum engineeringGeothermal energyAsphaltGreenhouse gasGeothermal gradientThermal energyEngineeringGeology

Abstract

fetched live from OpenAlex

Abstract The Heavy Oil Late Life Energy Recovery (HOLLER) project is the application of geothermal technology in steam assisted gravity drainage (SAGD) wells that are near end of life. While conventional geothermal technology is encumbered by the high cost of drilling deep wells to reach formations with the temperatures required for economic power generation, in situ bitumen producers have access to existing SAGD wells within mature reservoirs that are at shallow depths and high temperatures. The thermal energy from just one SAGD well can produce enough electricity to power thousands of homes for a year and major oilsands producers collectively have thousands of such wells. Our goal is to harness this thermal energy using the existing well inventory to create a closed geothermal system using process effluent water (PEW) such as boiler blowdown or tailings pond water as the heat recovery medium. This strategy has the potential to improve SAGD economics through incremental bitumen recovery, the generation of low-carbon base load electricity, and driving down SAGD greenhouse gas (GHG) emissions by recovering some of the spent energy. This strategy also provides an option to dispose process water and/or tailings water to accelerate the reclamation of tailings ponds. Suncor’s In Situ Technology team applied a stage-gated technology development process to progress HOLLER from Technology Readiness Level (TRL) 0—Idea to TRL 7—Field test. We applied the diverge-converge approach to 30 ideas that were distilled into four recommended commercial solutions. Our de-risking activities include numerical reservoir simulation, chemical process simulation, post-SAGD core and water analysis, laboratory studies for compatibility of various PEW sources with reservoir fluids and rock, core flooding, corrosion studies, facility design, economics, risk and uncertainty analysis, patenting, and testing in the field. As a result of the technology development work, we have developed a three-phase strategy to maximize the value of depleted in situ reservoirs: water disposal, energy recovery and permanent closure. This strategy offers synergies between mining and in situ operations, reduction in GHG emissions and environmental liabilities all while generating a net profit for the enterprise. If applied industry-wide, HOLLER technology has the potential of reducing not only the intensity, but also the absolute GHG emissions, while offering unique opportunities for collaboration between the in situ producers and mining operations. HOLLER is unique in its potential to retroactively reduce the GHG intensity of bitumen already recovered by thermal methods. It offers low emissions incremental bitumen production, nearly emissions-free power generation, increased efficiency of existing facilities through the direct use of recovered heat – while reducing mine tailings liabilities. HOLLER enhances the oilsands industry’s sustainability efforts.

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 score0.068
Threshold uncertainty score0.357

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 head0.016
GPT teacher head0.247
Teacher spread0.231 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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