Well Placement, Cost Reduction, and Increased Production Using Reservoir Models Based on Outcrop, Core, Well-log, Seismic Data, and Modern Analogs
Bibliographic record
Abstract
ABSTRACT Fluvial-estuarine channel complexes are significant producing reservoirs both onshore and offshore western Trinidad. These channel complexes are notoriously difficult to correlate in the subsurface. Numerous permeability baffles and barriers create complex reservoir heterogeneities that can result in significant bypassed hydrocarbons if the geometry and architecture of the channel bodies are incorrectly identified and not correlated in a rigorous sequence-stratigraphic framework. Outcrops of tidally influenced nonmarine channel complexes and modern deposi-tional analogs are used to determine architectural elements and bounding surfaces that impact reservoir continuity and heterogeneity, thus, highlighting subsurface correlation pitfalls. These elements and surfaces that are established from the outcrops are used for the examination of cores, well-log, and seismic data of strata deposited in analogous depositional systems. The subsurface and the outcrop geologic models are used in two reservoir-modeling scenarios: first, to refine subsurface reservoir models for horizontal well placement, leading to a more effective depletion strategy for the reservoir, and second, the modeling of a field simulation using outcrop exposures of a channel complex as a producing analog. The result of the simulation runs was a similar recovery from the “field” with far fewer wells, showing that substantial cost reductions are possible in drilling and completions, operations, and future well and field abandonment, including the potential risk and costs for environmental remediation.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".