Reducing Formation Damage With Microbubble Based Drilling Fluid: Understanding the Blocking Ability
Bibliographic record
Abstract
Abstract Commonly, bridging materials used to reduce formation damage and mud losses in the near wellbore region consist of solid particles. These particles need to be removed after drilling through processes such as acidizing. Microbubble based drilling fluids utilize gas bubbles to bridge the pores instead of solid particles. These microbubbles can be easily removed during the initial stages of production thereby reducing the costs associated with stimulation processes. Although there has been some work done on the flow of the microbubbles through porous media, little is known as to under what conditions the microbubbles will or will not block the pores (e.g. foam quality, viscosity, fluid composition, pressure). Both the microbubble diameter and the rock pore size distribution play roles in determining sealing of the pores. In order to gain an appreciation of the pore blocking mechanism, experiments were conducted using micromodel cells to visually understand the blocking mechanism. The composition of the fluid is varied as well as the flow rate at which the fluid is injected. Through this, the pressure at which the microbubbles invade the medium and the optimum composition and quality of the fluid for pore blocking is determined. The average microbubble size at the invasion pressure is compared to the average pore size of the porous medium. By understanding the extent of the microbubble invasion under varying conditions, a greater comprehension of the pore blocking mechanism can be established. As well, the success rate of applying the microbubble system to various types of reservoirs can be evaluated. Introduction Common blocking agents used in the oil and gas industry to reduce formation damage and mud losses while drilling and to block highly permeable streaks in the reservoir during production can be composed of gels, solid particles, emulsions and foams (Seright and Liang, 1995). The ability of these fluids to block specific areas of the reservoir is determined through the reduction of permeability in high permeable zones and the ability to place the fluid in the correct area within the reservoir. Specifically, many studies have been conducted on the blocking ability of foam in porous media (Smith et al., 1969; Albrecht and Marsden, 1970; Hanssen and Dalland, 1990; Aarra and Skauge, 1994; Nimir and Seright, 1996; Khalil and Asghair, 2006). When discussing blocking ability, most of these studies are concerned with blocking the flow of gas (Albrecht and Marsden, 1970) Albrecht and Marsden (1970) found that with an increase in surfactant concentration, the blocking effect was greater. The efficiency foams to block gas propagation in porous media with crude oil was studied by Hanssen and Dalland (1990). They tested many different surfactants and found that there was no correlation between gas blocking and interfacial tension and that only a few foams blocked of the samples tested. They stated that the foam blocking is a complex phenomenon.
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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 | 0.000 | 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) | 0.000 | 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".