Novel 3D Reservoir Characterization Approach in High-Angle Wells by Means of Multiphysics Integration of Seismic and Advanced LWD Ultra-Deep Resistivity Inversions
Bibliographic record
Abstract
Abstract Historically, reservoir characterization in horizontal wells is accomplished through an integrated approach, combining geophysical evaluation and geomodelling with petrophysical assessment. However, the challenge consists of decreasing geomodels’ uncertainties to enable optimal trajectory to reach the sweet spots. Moreover, the main inputs of subsurface representation derived from measurements have their own spatial resolution and scale. For an effective multiphysics integrated approach, a technology capable to bridge from high-resolution borehole data to large scale seismic is required. This communication describes an innovative method to improve the delineation of reservoir geometry and properties surrounding high-angle wells. The novel procedure is divided into three stages. The first focuses on structural delineation using seismic attributes. The second uses advanced resistivity inversions from LWD very-deep directional resistivity tool to provide 3D mapping of the structure and fluid distribution. The third stage integrates the previous steps to build a comprehensive 3D reservoir model with high accuracy, tens of feet away from wellbore, while honoring the geological context and actual spatial resolution of measurements from borehole level up to seismic scale. The three-step methodology was successfully conducted on the horizontal section of an appraisal well. The automated seismic extraction workflow on the near and far angle-stack seismic cubes is used to interpret the main stratigraphic and tectonic events around the well vicinity. Within the drilling operation, the high-definition resistivity volumes are obtained from a special 3D interpolation of the 2D LWD EM azimuthal inversions, derived from the measurements of the very-deep directional resistivity tool. Such 3D resistivity mapping is used to determine lithological and structural features over tens of feet away from the wellbore. Then, by applying an integrated approach, key geological structures and detailed internal reservoir architectures were revealed, such as the throw and azimuth of a main fault and the spatial variations in lithologies within the reservoir zone. Finally, the respective workflow can be fully applied while drilling, enabling both the complete 3D reservoir mapping but also supporting strategic geosteering decisions to optimize the extension of the net-pay exposure.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".