Assessment of CO2 EOR and Its Geo-Storage Potential in Mature Oil Reservoirs, Changqing Oil Field, China
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 Most oil reservoirs in the Changqing Oilfield area are low permeability and have entered middle development stages after several ten years of production, and they are suitable for applying CO2 EOR and carbon storage techniques. This study is aimed at assessing the potential of CO2 EOR and storage in Changqing oil fields based on the data of 261 mature oil reservoirs. The assessments include a regional geology assessment, storage site screening, and reservoir screening for CO2 EOR and EOR potential and storage capacity calculations. Of 261 reservoirs, 113 are suitable both for miscible or near miscible flooding EOR and storage while 148 reservoirs are found suitable for immiscible flooding EOR and storage. The total EOR potential could be 9,836.03!104t and the CO2 storage potential could reach 23,920.34!104t. The average incremental oil recovery rate in reservoirs suitable for miscible or near-miscible flooding could be 12.19%. The average incremental oil recovery rate in reservoirs suitable for immiscible could be 6.63%. The greater OOIP the oil reservoir has, the greater potential for CO2 EOR and storage it will have, and the more suitable for large-scale storage projects it will be. Those oil reservoirs suitable for CO2 EOR with large OOIP will be the preferred sites for CO2 storage. Introduction The mitigation of green gas emission, especially CO2, has drawn worldwide attention as the aggravation of global warming and climate change. CO2 geological sequestration in oil reservoirs can not only decrease CO2 concentration in atmosphere, but also enhance oil recovery by CO2 flooding (CO2-EOR). In North American countries, application of CO2-EOR have been maturely developed for decades. For those in China, where reservoirs are mainly characterized as heterogeneous layers and viscous crude oil, proper evaluation criteria for CO2-EOR and sequestration should be developed. Changqing Oilfield, which is also the second largest oilfield in China, is targeted to check newly established criteria and then recovery increment and sequestration potential can be researched. Characterized as low permeability, water flooding method benefits turned down during last decades in this field. Considering unique advantages of CO2 solvent over water, including feasible injectivity and high displacing efficiency, CO2 flooding in Changqing Oilfield is expected for extra oil production and sequestration as well. By then, an evaluation criterion of sequestration is developed based on 261 production layers and then potential benefits are predicted via that. 1. Evaluation criteria of CO2-EOR and sequestration in China oil reservoirs 1.1 criteria establishment JJ.Taber et al (1997) concluded screening criteria of CO2 flooding on the analysis of successful field application. Bradshaw J et al (2002) also suggested screening parameters over previous researches and ranked candidate reservoirs by setting optimum value and parametric weight, which proved ideal application in Alberta reservoirs. In China, similar researches (ZHENG Yun-Chuan et al,2005; LEI Huai-Yan et al, 2008; Zeng Shun-Peng et al, 2005; Zhang Liang, et al, 2009)have also been processed which is helpful for reservoir screening. Based on those above, screening criteria of CO2-EOR and sequestration in Changqing Oilfield can be obtained as in Table 1.
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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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