3D Enhanced Subsurface Data Visualization and Integration for Effective Horizontal Well Multi Transverse Fracture Development on a Clastic Tight Gas Field in Oman
Bibliographic record
Abstract
Abstract Horizontal wells with multiple transverse fractures were defined as the key well architecture and completion strategy oriented to develop clastic tight gas accumulation otherwise impossible to be developed with vertical wells. However, a detailed evaluation during early deployment indicates the need for an integrated subsurface platform (ISP) covering geophysics, geology and geomechanics, to support well placement, well orientation and hydraulic fractured design. Detailed subsurface characterization was used to build the ISP. Geomechanical logs estimated using the drilling data, as wells as, wireline logs are used to define engineered completion strategies. The ISP provide us with three dimensional properties maps capturing lithogical, petrophysical and geomechanical properties distribution, this allow the identification of the properties anisotropy coveing key variables including, elastic properties, in-situ stress variation, stress rotation across the field and stress anisotropy, thecombination of the predicted stimulated reservoir volume and the dynamic model, both part of the ISP, were used to access potential production forecast for selected well locations. The ISP support the identification of geological sweet spot to definelanding zones and optimizing the hydraulic fracturing to improve the production performance. We will discuss how the geomechanical evaluation provides us the spatially varying stress magnitude and stress orientation and strain across the tight reservoir units. The use of the geological and geomechanical data within the ISP can be used to estimate geomechanical half lengths that are used to improve fracture design. We will also discuss how completion optimization and number of perforation clusters can be defined to maximize gas production based on a better understading of the special variation of petrophysical, geomechanical and lithological properties across reservoir units. The described integrated subsurface platform can be used to help optimize horizontal well placement, well orientation and fracture completion design. It will be discussed the procedures and process of integration geophysical, geological and geomechanical reservoir properties into the ISP, as well as, how this was used to support the continuous development of these tight gas accumulation in Oman.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".