Stability of Microbubble-Based Drilling Fluids Under Downhole Conditions
Bibliographic record
Abstract
Abstract Colloidal gas aphrons (CGA) have the unique ability to form a bridge in the pores of reservoirs, which stops fluid invasion. Sizing microbubbles in accordance with the rock pore size distribution is imperative for effective sealing during drilling. The effects of time, temperature and pressure on the stability and size of the microbubbles needs to be better understood in order to design a fluid that will sufficiently block the pores of the formation for extended periods. In this study, the effects of time, pressure and temperature on the size of microbubbles and the stability of microbubble (CGA)-based drilling fluids were investigated. The change in the CGA diameter with time was determined by using a microscopic imaging technique. Effects of base fluid viscosity and surfactant concentration on the size and stability of the microbubbles were also investigated. Introduction CGA-based drilling fluids have been successfully used in high-angle and horizontal well drilling in highly depleted reservoirs(1). Microbubbles in CGA-based drilling fluids form a bridge in front of the pores of the rock. This bridge is believed to stabilize the rock while sustaining minimal damage to the formation. Stability of the microbubbles and how bubble size changes as a function of downhole conditions (i.e. temperature and pressure) are some of the major concerns associated with the application of CGA-based drilling fluids. A stable CGA structure requires maintaining an ideal film wall thickness of 4 to 10 microns(2). Another factor affecting CGA stability is the rate of transfer of the surfactant molecules between the viscous water shell and the bulk phase due to gravity drainage or temperature gradients. This leads to a surface tension gradient at the surface of the shell. As a result, the Marangoni Effect will counteract this deformation(3–4). Increasing the viscosity of the shell can help to minimize the transfer of surfactant molecules. Usually a biopolymer is added to adjust the shell viscosity(3). The third property that the CGA structure must have is low diffusivity, which is the ability of the air that is in the core to transfer to the aqueous shell. CGA bubble size and stability have been the subject of earlier studies(5–12). Longe(6) analyzed the bubble size distribution of CGAs for soil and groundwater decontamination applications. Longe's analyses included effects of surfactant concentration, surfactant type and electrolytes on the stability of the CGAs over the time. Jauregi et al.(7) also investigated the stability of CGAs as a function of surfactant concentration. Results from both studies indicated that the stability of CGAs increase with increasing surfactant concentration. Chaphalkar et al.(8) measured the size distribution of CGAs using a particle size analyzer. The CGAs were virtually non-existent after 20 minutes for three different types of surfactant. Roy et al.(9) reported similar results. Amiri and Woodburn(10) studied the rate of drainage, as well as the CGA bubble size, by recording the images of the CGAs over time. They reported that after 10 minutes, the bubble shape had changed from spheres to polyhedral structures.
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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 | 0.002 | 0.001 |
| 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".