Experimental Performance of Steam-Based Hybrid Technologies to Improve Energy Efficiency in a Colombian Heavy Oil Reservoir
Bibliographic record
Abstract
Abstract Due to the large amount of heavy oil reserves, thermal recovery projects have been developed in Colombia in the last decade. Several fields under Cyclic Steam Stimulation (CSS), and Steam Flooding (SF) plans are currently underway at field scale. In the country there are also significant reserves of heavy oil which are still under cold production which represent an important opportunity to implement novel technologies that improve energy efficiency. Such technologies should be implemented based on the maturity of the current recovery process and reservoir characteristics. Implementation of steam-based hybrid technologies are under evaluation in Ecopetrol as a strategy to increase both heavy oil recovery and energy efficiency. This work is focused on the design, execution, and results of two physical simulation experiments developed to study the behavior of the Teca Cocorná core and fluids with a combination of steam-based hybrid injection technology. Both tests were performed under similar conditions of temperature and pressure to assess the effect of a hybrid injection process on the residual oil saturation, produced hydrogen sulfide (H2S) and changes in produced fluids. The experimental methodology is described in detail for both experiments as well as relevant test results which constitute important parameters for reservoir simulation forecasts. The first hybrid test was a combination of steam flood and solvent injection (naphtha) which consisted of the injection of 0.38 pore volumes (PV) of cold water equivalent (CWE) superheated steam at 271°C, followed by the injection of 0.05 PV of naphtha solvent (CWE), and finally by the injection of 1.0 PV of superheated steam (CWE). The second hybrid test combined steam flood with injection of flue gas consisting of 15 percent carbon dioxide (CO2) and 85 percent nitrogen (N2). The injection scheme for this test was similar to the first test and consisted of the injection of 0.40 PV of superheated steam at a temperature of 271°C (CWE), followed by 0.30 PV of flue gas (CWE) and finally by 0.71 PV of superheated steam (CWE). This study shows that the Teca Cocorná oil-core system, as tested, responded positively in the laboratory to hybrid steam methods in terms of production and energy efficiency. A detailed comparison including temperature front, steam-oil-ratio, produced fluids and residual saturations is presented. In addition, the energy consumption is estimated. These results provide valuable information required for numerical and economic evaluations of steam-based processes prior to field tests.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".