A Novel Improved Oil Recovery Approach for Increasing Capillary Number by Enhancing Depth of Penetration in Abu Dhabi Carbonate Reservoirs
Bibliographic record
Abstract
Abstract During the last few decades, there has been a global increase in oil demand by 35%. Besides, the petroleum industry is faced with a number of challenges when considering the reservoir such as low sweep efficiency, formation damage and implementing costly techniques to enhance and improve the oil recovery. Electrokinetic Low-concentration acid IOR (EK LCA-IOR) is one of the emerging IOR technologies, which involves the application of the Low-concentration acidizing integrated with electrically enhanced oil recovery (EK-EOR). This research focusses on analyzing the effectiveness of the EK LCA-IOR process in Abu Dhabi carbonates, improving the capillary number and enhancing depth of penetration. Core-flood tests were conducted by saturating Abu Dhabi carbonate core-plugs with medium crude oil in a specially designed core-flood setup at Abu Dhabi reservoir conditions. After the waterflooding stage, EK LCA-IOR was applied using varying voltage gradients and acid concentrations upto 1.2% HCl injected at the anode and transported by EK to the target producer (cathode). Moreover, the capillary number change, oil viscosity change and Single Energy CT Scan (SECTS) results were analyzed in order to observe the effect on rock-fluid interaction to control rock adsorption capacity through interfacial tension and depth of penetration. Several correlations at reservoir conditions related to acid concentration, displacement efficiency and permeability enhancement have shown that the application of waterflooding on the carbonate cores yields an average oil recovery of 58%. An additional 17-28% oil recovery was enhanced by the application of EK LCA-IOR recording a maximum oil displacement of 88%. In addition, EK LCA-IOR was shown to enhance the reservoir's permeability by 53% on average across the tested core-plugs. EK LCA-IOR also improves the capillary number by 500% in Water-wet core plugs and 1500% in Oil-wet core plugs, mainly due to a decrease in interfacial tension. This indicates the decrease in acid adsorption as acid is precisely transported to the targeted production well through the tortuous path with an increased depth of penetration as proven by the SECTS results where EK LCA-IOR has penetrated 60% of core-length that revealed minor fractures, precisely delivering the acid front throughout the core-plug. Finally, EK LCA-IOR enhances capillary number along with an increased depth of penetration while allowing us to save on the OPEX by maintaining decreased power consumption while reducing the acid/water requirement upto 10 times. This study takes one step forward towards the development of EK Low-concentration acid IOR method feasible for Abu Dhabi oil fields in order to make smart waterfloods applicable for complex fractured reservoirs of UAE.
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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 | 0.000 | 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 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".