Seismic net-to-gross estimation for a geologic model update: A case study from a turbidite lobe reservoir in the deepwater of the Niger Delta
Bibliographic record
Abstract
Abstract Geologic model updates are routinely performed in mature fields to obtain improved descriptions of facies distributions and reservoir properties such as volume of shale, net to gross (NTG), and porosity, for better understanding of the static and dynamic behaviors of reservoirs for effective well placement, improved production, and monitoring. A simple but integrated seismic NTG estimation approach, using detuned seismic amplitudes is used in guiding NTG modeling during the geologic model update of a thin turbidite lobe reservoir in a mature oil field in the deep offshore Niger Delta. The objective is to address NTG overestimation and gross rock volume (GRV) uncertainty in a previous model arising from seismic amplitude tuning effects. A seismic NTG approach is chosen relative to sophisticated deterministic or stochastic inversion techniques to avoid tuning effects, which usually bias NTG estimates in thin turbidite reservoirs. The primary data set is a 2019 reprocessed prestack depth-migrated (PSDM) seismic data vintage, which had better resolution, higher signal-to-noise ratio, and more appropriate angle-stack apertures for amplitude variation with angle fidelity, than the older 2011 PSDM seismic data that are used in the previous geologic model. The methodology involved rock-physics analysis, seismic data quality checks (QCs), tuned area determination, detuning of composite seismic amplitudes of the top and base reservoir, and their direct calibration to NTG at wells. Good correlations are obtained between the detuned composite seismic amplitudes and NTG at wells. The seismic NTG map shows good calibrations at wells and provides a robust trend for net sand modeling in the oil pool and aquifer. Static model QCs and dynamic simulations prove that the seismic NTG attribute addressed the GRV uncertainty in the earlier model, thus giving confidence for using the updated model for the planning and geosteering of infill wells, sand completions, reservoir monitoring, and production.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".