A New Azimuthal Deep-Reading Resistivity Tool for Geosteering and Advanced Formation Evaluation
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Drilling services and oil companies have long been interested in acquiring the capability of landing a well accurately in a hydrocarbon reservoir and remaining in it for optimal drainage. Although traditional logging-while-drilling (LWD) propagation resistivity tools can help to achieve this goal, their overall effectiveness is not satisfactory because they lack azimuthal sensitivity. Ideally, geosteering and advanced formation evaluations, such as anisotropy calculations, require azimuthally sensitive measurements. This paper discusses a newly developed propagation resistivity tool that is designed to be azimuthally sensitive for use in geosteering and formation evaluation while drilling. It uses the tilted antenna concept to produce directionally sensitive measurements that are lacking in traditional LWD propagation tools. This paper also discusses the theory and the development of this tool, as well as the experimentation and numerical modeling data used to characterize its azimuthal capability. Advanced application algorithms used to calculate the horizontal and the vertical resistivity (anisotropy calculation), as well as dipping angle will be explained in detail. Finally, the paper presents and discusses field examples to demonstrate that this newly developed tool is a two-in-one service: geosteering and advanced formation evaluation. The azimuthal deep-reading resistivity is shown to bear the promise for use in optimization of well trajectory and well placement, as well as in advanced formation evaluation while drilling. This newly developed tool is superior to traditional propagation tools in locating bed boundaries and in keeping the well in the desired pay zone. In addition to providing traditional multi-depth of investigation resistivity measurements, this new tool provides multi-depth of investigation azimuthal resistivity measurements.
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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 | 0.001 | 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 | 0.001 |
| 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 it