A Field Application of Nanoparticles For Improved Downhole Losses in Invert Emulsion Drilling Fluids
Bibliographic record
Abstract
Invasion of drilling fluids filtrate and solids into porous, permeable, fractured or vuggy zones can cause formation damage and presents a major source of drilling problems. Furthermore, downhole mud losses also increase environmental and financial risks associated with drilling operations, costing over $1B annually. This thesis investigates the use of in situ prepared calcium carbonate nanoparticles (CNP) for fluid loss prevention in invert emulsion drilling fluids. CNP at 5 wt% concentration were synthesized within a custom ‘carrier’ emulsion using a modified microemulsion approach. Subsequently, the carrier emulsion was used to deliver target concentration of NPs to a host drilling fluid of interest via volumetric dilution. High pressure, high temperature (HPHT) fluid loss experiments on commercial invert emulsion drilling fluids showed that CNP at concentration of 0.5 wt% provided a 20–50% improvement over conventional lost circulation materials (LCM). In addition, basic properties of mud samples were not affected significantly in the presence of the carrier emulsion. In order to evaluate performance of CNP under real-life conditions, six full-scale field tests were conducted in horizontal wells in Alberta, Canada. Industry-scale synthesis of CNP followed the lab-bench process and was implemented at a specialized mixing facility. The results suggested that the scale-up from 3×10-4 m3 (300 mL) to 20 m3 did not affect average particle size or final properties of the carrier emulsion. Furthermore, field HPHT data showed good agreement with the lab experiments, where the average fluid loss in the test wells was reduced by 20–30% compared to the control wells using conventional drilling fluids. Finally, analysis of mud losses revealed that the cumulative losses while drilling were on average 20–30% lower in the presence of 0.5 wt% CNP, which suggested that NPs help to reduce downhole losses.
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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) | 0.000 | 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 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".