Investigation of Economic Uncertainties of CO2 EOR and Sequestration in Tight Oil Formations
Bibliographic record
Abstract
Abstract Advancement in drilling and production technologies, such as horizontal drilling with multi-stage fracturing, has enabled commercial production from more challenging reservoirs, namely, tight oil formations. However, high capital costs and relatively low recovery narrow the profit from such reservoirs. CO2 EOR has provided not only an excellent opportunity to unlock more oil production, but also a chance to sequestrate more CO2 to reduce environmental footprint. However, profitability of CO2 EOR processes could rely heavily on market conditions. While CO2 EOR reserves and CO2 storage can be quantified through compositional simulation, thorough economic analyses need to be conducted to evaluate the viability of a CO2 EOR project. The complexity of this study can be reduced significantly through experimental design. Randomized economic uncertainties, such as commodity prices, royalty scheme and incentives, CO2 sequestration credits, capital and operating cost structure, CO2 price, etc. can also be investigated with Monte Carlo simulation. This coupled approach allows us stochastically to sensitize the probability of each parameter and quantify their financial impacts on CO2 EOR projects. This methodology is extremely valuable in the assessment of risks in business, especially when uncertainties are high or the problem is rather complex, such as CO2 EOR/sequestration in tight oil reservoirs. The remaining oil in tight oil formation, after primary and water flood, is still significant. Hence, CO2 EOR has attracted attentions from industrial partners and government regulatory bodies. This paper provides a rigorous workflow for the industry on how to appraise such projects, as well as a perspective for the governing bodies of how to transform their policies and incentives when market conditions change.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".