Energy Efficient Steam-Based Hybrid Technologies: Modeling Approach of Laboratory Experiments
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".