Bibliographic record
Abstract
Abstract Drilling performance for North America Land operators has been evolving quickly to achieve lower cost per foot wells. Rotary Steerable System (RSS) adoption has reduced the number of bottom hole assemblies required (BHA) for a well. Matching bits with RSS systems enhances steering consistency and drilling performance to deliver single run build and lateral sections. The authors have developed a method of combining independent Bit and BHA models to determine achievable dogleg severity (DLS) while limiting borehole quality problems. Steering requirements define the BHA, which is then modeled to find the bit tilt requirements for achieving the directional objectives. The bit is modeled and designed to meet those requirements for directional control, Rate of Penetration (ROP), durability, and dysfunction mitigation. The BHA model incorporates high-fidelity BHA and hole geometry to establish boundary conditions, and outputs contact locations of forces acting on BHA. The Bit model computes the relationship between Bit Tilt and Bit Side Force. The Bit-BHA relationship is then defined by comparing the required Bit Tilt from BHA to the Bit Tilt Capability of the bit at a given side force. The field case histories illustrate results from the Bit-BHA combined modelled outputs that achieved the steering requirements of high DLS curve-lateral application across unconventional basins in North America. The steering capability was enhanced with a matched RSS system increasing the DLS capability by 2°/100’ in the build section while continuing to drill in the lateral to total depth with the same BHA assembly. The combined model methodology was validated in different hole sizes in both the Appalachian and Permian Basin. The drill bit compatibility to the application will vary depending on a particular BHA geometry stiffness, steering requirements, rock characteristics and planned drilling parameters. The Bit Tilt Capability (BTC) plot helped to explain the phenomenon of how lower depth-of-cut (DOC) is effective in improving dogleg capability of a system. Additionally, the field data validates shorter bit make-up lengths, longer bit gauge pad lengths and optimal gauge pad reliefs for the application. The case studies showcase the consistency achieved with the new methodology compared to previous methods where the models had less weightage on the key factors affecting bit steerability to match RSS systems. The methodology utilizes high-resolution models for both the bit and BHA, rather than often-used simplified models for one or the other. This produces more accurate Bit Tilt vs Side Force data of what is required by the BHA and what the bit is capable of and allows for optimizing the bit design to the RSS.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".