Anisotropy from Deep Directional Resistivity LWD Measurements
Bibliographic record
Abstract
Abstract Deep directional resistivity LWD measurements have been shown to be sensitive to resistive transitions over a broad range of distances around the tool from tens to hundreds of feet. These detected transitional surfaces are primarily used to detect formation resistivity boundaries and assist with mapping geological profiles. The inverted formation dip and vertical resistivities are also resolved in the same search space. While the formation dip is used in conjunction with the reservoir-mapping interpretation results, the vertical resistivity, specifically the vertical and horizontal resistivity ratio, or anisotropy, has not received the same amount of attention. Resistivity anisotropy is useful when calculating the formation resistivity in layered formations, as conventional resistivity tools measure the resistivity in one direction, which is perpendicular to the tool axis. With conventional induction and propagation resistivity tools, the electrical current preferentially transits the conductive lithologies, resulting in an apparent resistivity measurement that does not represent the true sand resistivity. The petrophysical evaluation often results in an apparent high-water saturation, which can result in incorrect decisions to abandon a prospect. To understand two new fields located onshore Alaska, three horizontal appraisal wells were drilled with deep directional resistivity LWD technology. While the primary goal was to characterize the lateral resistivity profile and bed boundaries away from the wellbore, accurate water saturation calculations along the horizontal section are critical for making appropriate development decisions. A review on how and why deep directional resistivity LWD technology is sensitive to anisotropy and how anisotropy is derived from parametric inversions is presented with a comparison between deep directional resistivity LWD measurements, 3D petrophysical modeling of propagation, and offset well triaxial induction anisotropy measurements. Integrating 3D petrophysical processing and triaxial-induction technology into deep directional resistivity LWD measurements add to the strength of the anisotropy output. The comparison shows that deep directional resistivity LWD measurements can be used independently to give accurate anisotropy results. The result of this process provides a corrected resistivity measurement of vertical and horizontal resistivity in anisotropic formations for petrophysical models. Use of the corrected resistivity as a true resistivity (Rt) input for water saturation will ultimately drive better development decisions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".