Experimental Investigation on Wellbore Strengthening in Shales by Means of Nanoparticle-Based Drilling Fluids
Bibliographic record
Abstract
Abstract Wellbore strengthening (WS) is the mechanism of increasing the fracture pressure of the rock at depth. Applications of WS in the drilling industry enable safe drilling by preventing mud losses, drilling in narrow mud windows, accessing reserves in depleted reservoirs, and also have the potential to reduce the number of casing strings. Most of WS applications have been done for sandstones. In fact, a common industry thought is that a permeable formation is the only medium that allows WS occurrence. WS in shale formations is a controversial topic in the drilling industry due to the poor understanding of the mechanism and limited field success on strengthening of low permeability formations. This paper presents an experimental research work where a significant fracture pressure increase was achieved in shale and the predominant WS mechanism was identified. The main implication of this work is that WS can occur in shale formations using oil based mud (OBM) with the addition of nanoparticles (NPs) and graphite. Fracture pressure increase was quantified by conducting hydraulic fracturing tests on 5 3/4″x9″ Catoosa shale cores. A 9/16″ wellbore was drilled, cased and cemented. Overburden and confining pressures were applied on the cores to simulate a normal-faulting regime. Two injection cycles were applied allowing 10 min for fracture healing after the first cycle. The fracturing pressure was increased by 30% when calcium-based NPs (NP2) were used, whereas iron-based NPs (NP1) resulted in 20% increase. The optimum NPs concentrations were experimentally identified. A strong relationship between WS and HPHT filtration values was observed. Optical microscopy, scanning electron microscope (SEM) and energy dispersive X-ray spectroscopy (EDX) analyses were conducted on the cores post-testing. The fractures were found to be completely sealed from wellbore to tip. The seal was developed due to the carrier fluid penetration through the induced fractures and NPs attachment on the fracture faces. This was corroborated by the estimation of the pore throat aperture of the shale at the testing pressure. Tip isolation by the development of an immobile mass was identified as the predominant WS mechanism. A 20 micron-seal containing homogenously distributed NPs and graphite was formed. According to the post-testing analysis of cores and injection pressure, WS initially occurred in a certain wellbore direction and a second injection cycle forced the fluid to follow a different direction creating a second vertical fracture. An average angle of 30° was observed between the hydraulic fractures.
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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".