EOS Simulation for CO2 Huff-n-Puff Process
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Bibliographic record
Abstract
Abstract An EOS based simulation approach has been developed to examine various oil recovery mechanisms for CO2 huff-n-puff process and to carry out the production evaluation using this process for oil reservoirs. These recovery mechanisms include the swelling effect, viscosity reduction, relative permeability effect, miscibility effect, gas solubility, gas extraction and diffusion, CO2 impurity effect, gas penetration depth, and injection and production scheme considerations. The EOS huffn- puff simulation incorporates the relative permeability curves to account for mobility effect as well as the effects of different depletion schemes. A modeling procedure is described performing these mechanistic analyses and the huff-n-puff process simulation. The procedure provides quick evaluations for predicting the potential oil recovery, examining design parameters and optimizing the production scheme to maximize the oil recovery of the process. It also allows for quick screening of reservoir candidates for such a process. The results indicate that EOS simulation is a useful tool for evaluating gas Huff-n-Puff processes both in design stage and in production optimization. Introduction The enhanced oil recovery using CO2 injection has been applied in the petroleum industry for decades. Miscible CO2 flooding is the process of choice for many enhanced recovery projects and is well documented in the literature. The CO2 huffn- puff process has received a lot of attentions because the process is easy to implement and doesn't require a large up front capital commitments. This process is basically preformed by injecting CO2 into aproducing well and the well is opened for production after a relatively short period of soaking time to allow for CO2 interacting with the formation oil. The first CO2 huff-n-puff project reported in the literature was conducted in 1960's [1]. The interest in such a process was renewed in 1980's in an attempt to recover additional oil from water-flooded, light oil and heavy oil reservoirs through immiscible CO2 injection [2]. Laboratory research and field experience indicated that the process was quite positive economically for various types of reservoir. Production responses suggested that there are a number of mechanisms involved in the oil recovery process [3][4]. An optimal field implementation is very much dependent on the specific field situation and on understanding dominant factors in the oil recovery process by having them effectively work together. The challenges may include the availability of gas source, an excessive cost of delivering it to the wellhead, the control of corrosion rate in the surface and subsurface equipment, and recycling of production gas without releasing into atmosphere etc [5]. Thus, it is beneficial to have a quick evaluation methodology accessible to engineers for analyzing the feasibility of CO2 huff-n-puff project in terms of reservoir engineering. While the process is a proven enhanced oil recovery technology in different reservoirs, there is increasing interest in CO2 huff-n-puff injection into single wells and its variations to inject flue-gas, exhaust gas and CO2 rich gases. The reason is partly because of the process being relatively easy to apply and comparatively inexpensive [6], and partly because of the government incentives to reduce the green house gas release to minimize the environmental impact from the CO2 production.
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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) | 0.002 | 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 it