Combining Advanced Real-Time LWD Answers with Accurate and Flexible 3D Rotary-Steerable System for Proactive Reservoir Navigation
Bibliographic record
Abstract
Abstract Development of formation evaluation technologies for azimuthal, deep-reading measurements and accurate and flexible 3D rotary steerable drilling systems have made it possible to provide truly proactive well placement. Real time use of new and advanced LWD data in horizontal drilling gives the opportunity to extend the envelope for well planning and reservoir information gathered in a single run. Optimum interpretation from real time data acquisition facilitates a reformation of the way horizontal wells are planned and drilled. This paper shows a well where the information was extracted and included in the decision making process to an extent that sets a new industry standard. Applying an accurate 3D rotary steerable system with openhole sidetrack capabilities increase well design flexibility and the ability to act on the real-time LWD data. The bottom hole assembly used consisted not only the standard LWD services such as gamma ray, propagation resistivity, density, neutron porosity and LWD gamma ray and density imaging services, but also formation pressure testing while drilling and the newly introduced azimuthal propagation resistivity. The azimuthal propagation resistivity provides unique information of the approaching bed and fluid contacts. Verification or change of the geological model during drilling through comprehensive use of forward resistivity modeling and real-time geological interpretation based on both the newly introduced azimuthal propagation resistivity service and resistivity from multiple depths of detection as well as wellbore images from both density and gamma ray, give a solid and enhanced base for real time well placement in horizontal drilling. The horizontal interpretation of resistivity data together with structural interpretation of image data gives an unmatched understanding of the geology and aids in placing the well better. Horizontal real time wellbore placement is then accompanied by real time reservoir characterization through real time formation pressure measurement to validate any fluid movements and fluid compartmentalization of the reservoir. This paper is based on wells drilled in a chalk field in the North Sea where real time data acquisition plays a significant part and illustrates to what level LWD data can facilitate proactive well placement and comprehensive reservoir characterization.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".