Hydrocarbon reservoir definition and delineation using log analysis, petrophysics and rock physics in Y-Field, offshore Nigerian Niger Delta
Bibliographic record
Abstract
Purpose This study aims to characterize the reservoirs through well log analysis, intensive elastic log studies and cross-plotting (rock physics studies) of the Y-Field reservoirs to define the reservoirs hosting hydrocarbons in the basin with structural complexity. Poor evaluation of sand body has been part of the problem leading to wet wells and these above methods accounted for this gap. Design/methodology/approach The basic parameters needed for the characterization are P-wave (Vp), S-wave (Vs) and density (ρ) and understanding the relationship between these parameters with the pore-fluid and lithology is of utmost importance in the evaluation of sand body. This was achieved through elastic rock physics studies. Important among these elastic logs include P-impedance (Zp), S-impedance (Zs), Vp/Vs ratio, Poisson’s ratio, lambda-rho (λρ) and mu-rho (µρ), and are critical. Rock physics diagnosis involving elastic log generation and analysis accompanied by cross-plotting of the elastic logs color-coded with petrophysical properties were employed. Findings The findings from the elastic logs indicated that hydrocarbon-charged units are characterized by low Zp, Zs, Vp/Vs ratio, Poisson’s ratio, λρ and µρ resulting from a significant drop in P-wave and density because of hydrocarbon presence. The results also showed that some of the reservoirs with hydrocarbon accumulations exhibit high Vp/Vs and Poisson’s ratio as against their usual low values in hydrocarbon intervals which results from the presence of shale with high plasticity. The elastic logs cross-plots of Vp/Vs with Zp, Poisson’s ratio with Zp and λρ with µρ successfully distinguished between the hydrocarbon-charged zone and the brine-saturated units. All these outcomes are the benefits of this research. Originality/value It can be shown that the analysis/results have distinguished hydrocarbon-charged units from brine-saturated intervals and the originality lies in the integration of various sophisticated fluid identification templates.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".