Sensitivity Analysis of CO2 Minimum Miscibility Pressure Optimizes Gas-Injection EOR
Bibliographic record
Abstract
_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 216683, “Large-Scale, High-Throughput Sensitivity Analysis of CO2 Minimum Miscibility Pressure To Optimize Gas-Injection EOR Processes,” by Ali Abedini, SPE, ZhenBang Qi, SPE, and Thomas de Haas, SPE, Interface Fluidics, et al. The paper has not been peer reviewed. _ Performance of CO2 injection relies on accurate CO2 minimum miscibility pressure (MMP) and miscibility data at reservoir conditions. A slim tube is the most-reliable tool to measure MMP under different miscibility mechanisms; however, it is very time- and capital-intensive, making it impossible to provide high-throughput data to assess the effect of other gases. Rising-bubble apparatus and vanishing-interfacial-tension techniques are cheaper and easier to run, but these methods are unable to capture different miscibility mechanisms fully. In the case study presented in the complete paper, the authors present a highly efficient microfluidic platform to measure, in a faster and easier manner, high-quality MMP data of CO2 with various impurities significantly. Introduction Conducting miscibility tests at high pressure or high temperature with live oil samples and real gas mixtures requires a platform capable of handling complex fluid systems at reservoir conditions. An advanced microfluidic system was used to perform a large set of miscibility/MMP tests to investigate the role of different impurities on the MMP of pure CO2 with an oil sample from a depleted reservoir in Alberta. The results reported demonstrate the capabilities of the new microfluidic approach to provide fast and accurate high-volume miscibility and MMP data for a wide range of gas compositions unobtainable by conventional methods. Microfluidic Platform, Testing Samples, and Experimental Procedure The portable microfluidic platform integrates fluid-control, microfluidic, and imaging systems, enabling performance of a series of miscibility and MMP measurements (Fig. 1a). The platform is equipped with three high-pressure pumps to control gas injection, oil injection, and backpressure. The gas sample, oil sample, and effluent are stored in sample bottles heated with a heating jacket and connected to the pumps. The valves and tubing are placed in a valve box that heats up internally. The manifold is the holder for the microfluidic chip and consists of bottom and top pieces that sandwich the chip. The bottom of the manifold is controlled by a hydraulic pump. The time-lapse imaging is performed using a microscope equipped with a high-resolution camera. Fig. 1b shows the microfluidic chip and the porous media design. The serpentine porous media, with a total length of 57 cm, contains circular pillars to promote multiple contacts in the system. Table 1 of the complete paper contains the list of the gases used in this study. The composition of the recycled gas includes approximately 86% CO2, approximately 7.7% methane, and other impurities. To validate the accuracy of the microfluidic MMP, the data were compared with the MMP data obtained with the slim tube. The measurements were conducted with pure CO2 and a mix of CO2 with recycled gas. While the tests were not performed at exactly the same conditions and in the same time frame, the results showed that the microfluidic MMP data were in good agreement with those of the slim-tube tests.
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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 | 0.001 | 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 | 0.001 |
| 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 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".