Seismic reservoir characterization of Bone Spring and Wolfcamp Formations in the Delaware Basin — A case study: Part 1
Bibliographic record
Abstract
The Delaware and Midland Basins are multistacked plays with production being drawn from different zones. Of the various prospective zones in the Delaware Basin, the Bone Spring and Wolfcamp Formations are the most productive and thus are the most drilled zones. To understand the reservoirs of interest and identify the hydrocarbon sweet spots, a 3D seismic inversion project was undertaken in the northern part of the Delaware Basin in 2018. We have examined the reservoir characterization exercise for this dataset in two parts. In addition to a brief description of the geology, we evaluate the challenges faced in performing seismic inversion for characterizing multistacked plays. The key elements that lend confidence in seismic inversion and the quantitative predictions made therefrom are well-to-seismic ties, proper data conditioning, robust initial models, and adequate parameterization of inversion analysis. We examine the limitations of a conventional approach associated with these individual steps and determine how to overcome them. Later work will first elaborate on the uncertainties associated with input parameters required for executing rock-physics analysis and then evaluate the proposed robust statistical approach for defining the different lithofacies.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".