Enhanced Recovery of Heavy Oil in the Niger Delta: Nelson and Mcneil Model a Key Option for In-Situ Combustion Application
Bibliographic record
Abstract
In-situ heavy oil recovery involves several field tested enhanced oil recovery methods/techniques with applicable models. Such field tested techniques include non-thermal oil recovery, hybrid oil recovery and solvent-base oil recovery. The viabilities and field successes recorded by these in-situ heavy oil recovery techniques/methods cannot be overemphasized. But, the main focus of this study is on heavy oil recovery using in-situ combustion with attention on the application of Nelson and McNeil model as documented in the in-situ combustion handbook (Partha, 1999). We subjected data(s) obtained from five (5) heavy oil reservoirs located within the same field in the Niger Delta to the correlations, equations, assumptions and calculations proposed by our study model. This enabled the research team to carry out performance evaluations while considering in-situ combustion implementation using our proposed model. Our result outcomes were further validated with a foreign heavy oil reservoir having similar reservoir properties. Our study results show how viable and profitable (with possible commercial production) heavy oil production from unconventional reservoirs in the Niger Delta would be. Currently, most of the internally generated oil revenue by the Nigeria government is from cheap/light oil obtained from conventional reservoirs (which is fast declining globally).
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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 | 0.000 | 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 | 0.001 |
| 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".