Improving Oil Recovery while Helping to Achieve Net Zero Emissions from Shale Reservoirs
Bibliographic record
Abstract
Abstract Shale reservoirs will help to meet oil demand that is forecasted to continue increasing for several years. Oil recovery from shales is low and has been reported to range between 5 and 10%. The objective of this paper is to show how oil recovery from shale can be improved while simultaneously reducing CO2 emissions, contributing thus to the goal of a Net-Zero future. The proposed methodology shows how oil recovery from shales can be increased while simultaneously storing CO2 in undepleted (as opposed to depleted) shale oil reservoirs, and consequently contributing to a future with Net-Zero emissions. The methodology is developed with the use of reservoir simulation, and is achieved by performing the following procedure: (1) start huff 'n' puff CO2 injection, 2 or 3 years after the well goes on oil production; thus, the shale reservoir is undepleted, (2) store CO2 gradually in the shale reservoir during the huff periods, and continuously once the huff'n'puff project is finalized. The simulation model includes a history match period with actual production data from a pilot horizontal well, and a forecast period with huff 'n' puff CO2 injection. Two cases, one with diffusion and one without diffusion are carried out for evaluating the molecular diffusion effect. The initial pressure is never exceeded. Our literature survey indicates that the methodology proposed in this paper has not been considered previously in the geoscience of petroleum engineering literature. The proposed approach will help to achieve Net-Zero emissions by storing CO2 in undepleted shale reservoirs while simultaneously increasing oil production. This win-win combination, to the best of our knowledge, is novel.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".