Investigation of Economic Uncertainties of CO2 EOR and Sequestration in Tight Oil Formations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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) | 0.003 | 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 it