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Record W4224058395 · doi:10.2118/209439-ms

Energy Efficient Steam-Based Hybrid Technologies: Modeling Approach of Laboratory Experiments

2022· article· en· W4224058395 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.

Bibliographic record

VenueSPE Improved Oil Recovery Conference · 2022
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsFlue gasSteam injectionPetroleum engineeringBoiler (water heating)Environmental scienceEnhanced oil recoveryProcess engineeringWaste managementEngineering

Abstract

fetched live from OpenAlex

Abstract Colombia is evaluating different steam-based hybrid oil recovery technologies as a strategy to face current challenges in the development of heavy oil reservoirs. Oil price volatility, the need for an energy transition, and carbon footprint reduction are factors limiting the commercial deployment of conventional steam injection projects. Ecopetrol evaluates the hybrid steam methods at laboratory scale as one of the different options to overcome current constraints developing heavy oil resources. The ongoing experimental program is supported by numerical modeling as a prior step to upscale the results at the pilot-scale. This study aims to present history match results and describe the numerical modeling approach of hybrid steam experiments (50 mm diameter × 1.1 m long assembly) and compare it against the baseline steam injection simulation. The first hybrid test involved the injection of steam and flue gas considering consecutive floods that included a saturated steam flood (SSF), a flue gas slug injection, and a second saturated steam flood. The second test was a steam and solvent injection following the same experimental protocol (SSF + solvent + SSF). The variables matched included produced fluids, pressures, produced gas compositions, and temperature profiles. One important feature is that all three models use the same set of water-oil relative permeability curves obtained from an independent experiment. Also, it was assumed those curves are not a function of temperature, which simplifies the modeling and allows focusing on the physical mechanisms relevant to each experiment. For instance, for the hybrid steam-flue gas test, it was necessary to include an additional set of gas-oil relative permeability curves to account for the presence of the flue gas in the gas phase. The hybrid steam-solvent test was focused on modeling the mixing of the native oil with the injected solvent. The proposed workflow led to a good history match of all variables, particularly total produced fluids, temperature profiles, and injection pressures. Additional recommendations are provided based on laboratory observations to understand important mechanisms such as trapped gas, relative permeability hysteresis, and solvent characteristics. A new methodology to simulate hybrid steam methods is provided. The proposed numerical approach incorporates novel energy efficiency and carbon intensity indexes to guide the decision-making and identify recovery strategies driven by its efficiency and reduce carbon footprint. Both hybrid tests led to energy efficiency improvements and reduction in carbon intensity up to 20%. These indexes combined with experimental results will be key input parameters for designing and commissioning future pilot tests using numerical simulations at the field scale.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.520
Threshold uncertainty score1.000

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 head0.021
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