Does High Performance Water Based Mud Offer Superior Wellbore Strengthening Benefits Compared to Traditional Synthetic Based Muds?
Bibliographic record
Abstract
ABSTRACT: Depleted Fracture Gradients have been notoriously challenging for drilling, cementing and completions operations for several decades. Depletion continues to become more severe and we expect to need to deliver successful fluid systems for overbalances of between 12,000 and 14,000 psi within the next few years. Many solutions have been proposed, but the majority of deepwater drilling operations have been limited to Fracture Widths (FW) of between 1000-1400 microns when utilizing background Lost Circulation Material (LCM). The plugging of larger fracture widths have been routinely achieved when an open hole squeeze is performed across depleted sands that are exposed, but the operators experience is that properly calibrated models show that the current real time drilling limit is in this range. Additionally, when losses occur with a traditional SBM fluid system, the downhole pressure quickly falls to the minimum horizontal stress for the loss zone, and recovering full strength is very difficult. This paper will demonstrate how a High-Performance Water Based Mud (HPWBM) can be designed to withstand extreme overbalance (>9000 psi) and successfully inhibit against shales in the section. It will also discuss the execution in the reservoir sections of three deepwater wells where HPWBM fluid systems were successfully deployed. Lost circulation was induced in all three instances and this paper will demonstrate the benefits of using a HPWBM compared to an SBM for drilling through larger fractures induced by greater overbalances and recovering from major lost circulation events.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".