Permeability Model for Nigerian Oil Sand as Candidate CO 2 Storage Reservoir
Bibliographic record
Abstract
This research examines the challenges associated with storing captured CO 2 in the Niger-Delta reservoirs by examining the effect of the stored gas on the reservoir rock sample. Mitigation against increasing CO 2 in the atmosphere is uppermost in environmental research due to its negative effects and therefore there is need to explore all possible reservoirs, apart from abandoned crude oil reservoirs, for CO 2 storage. In this research, a model was developed to study permeability variation during CO 2 injection to oil sand as a candidate CO 2 storage reservoir. Four existing permeability models of Tixier, Timur, Coates-Dumanoir and Aigbedion were employed together with a proposed model. The proposed model was a combination of irreducible water saturation equation from Timur model and the Coates-Dumanoir permeability equation. The proposed model took cognizance of changing porosity phases, since the injected CO 2 is reactive and affects the properties of the reservoir rock. The model equation obtained is the model gave permeability value ranging from 16.94 to 2.74 mD for Imeri oil sand. In comparison, the Timur model gave permeability values from 1.45 to 0.002 mD; Tixier value ranges from 42.85 to 0.01 mD; Coates-Dumanoir value of 287.72 to 7.49 mD while value given by Aigbedion rangs from 9.01 to 2.6 mD. In the course of the research it was discovered that Imeri oil sand formation, though has very high porosity which could be a pointer to early stage leakage, is highly reactive with the injected CO 2 . This reactivity is a good condition for permanent storage of the injected gas and is therefore recommended, with reservation, as a potential CO 2 storage reservoir. The proposed model will also give the expected CO 2 gas mobility with increasing period of injection. Key words: CO 2 storage; Oil sand; Porosity variation; Permeability; Permeability model
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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".