Evaluation of the subject geological area suitability for oil recovery by High-Pressure Air Injection method
Bibliographic record
Abstract
Abstract. The considerable decline of conventional oil and gas reserves and respectively their production introduces new challenges to the energy industry. It resulted in the involvement of hard-to-recover reserves using advanced enhanced oil recovery (EOR) techniques. Thermal methods of EOR are recognized as most technically and commercially developed methods for the highly viscous crude oil. High-Pressure Air Injection (HPAI) is one of the thermal production methods that reduce oil viscosity and increases recovery. HPAI has already been effectively applied for different types of reservoirs development and proven to be economically feasible. The application performance of the HPAI technology strongly depends on the quality of experimental and numerical modeling conducted on the target object basis. Before the field tests, physicochemical and thermodynamic characteristics of the process were studied. Further consequent numerical modeling of laboratory-scale oxidation experiments and field-scale simulation was conducted to estimate HPAI method feasibility based on the results of oxidation studies. A medium pressure combustion tube (MPCT) oxidation experiment was carried out to provide stoichiometry of the reactions and field design parameters. A 3D numerical model of the MPCT experiment was constructed taking into account the multilayer design, thermal properties, heating regimes, and reaction model. The “history” matched parameters such as fluid production masses and volumes, temperature profiles along the tubes at different times and produced gas composition demonstrated good correspondence with experimental results. The results obtained during the experiment and modeling of MPCT (fluid properties, relative phase permeability, kinetic model, technological parameters) were used in field-scale modeling using various thermal EOR scenarios. Air breakthrough into production wells was observed, thus a 2 % oxygen concentration limit where implied. The overall performance of four different scenarios was compared within 30 years timeframe. The development system was also examined to achieve the maximum economic indicators with the identifications of risks and main uncertainties.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".