Practical Insights and Benefits of Integrating Technology into Exploration, Appraisal and Development of Unconventional Gas and Liquid Rich Shale Reservoirs
Bibliographic record
Abstract
Abstract In the current high oil / low gas price North American environment, and considering the new options available for well completions technology in unconventional reservoirs, recent industry activities have turned their focus to the areas of Liquid Rich Shales (LRS) and Light Tight Oil (LTO) along with unconventional tight and shale gas (UG). Integrated workflows are important to the successful execution of this portfolio, i.e. systematic methodologies to screen and appraise opportunities, and cutting edge integrated technologies must be viewed as key enablers. It is also important to maintain a life-cycle mindset and leverage economies of scale to execute projects faster and more efficiently. This paper will discuss some of the advances and best practices that Shell has in each of the following disciplines, the value of R&D and applied technologies as well as integrated workflows used for exploration, appraisal and development of UG, LRS, and LTO plays: • Geological screening and sweet-spotting • Geomechanics evaluation and modeling • Reservoir engineering, including PVT sampling and characterization • Completions, stimulations and diagnostics • Artificial lift and operational considerations
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".