Investigation of Pore Pressure on Seismic Amplitude Response: A Well-Based Modeling Case Study of Sojuko Field, Niger Delta
Bibliographic record
Abstract
The Sojuko field was discovered in 2001 in the eastern shallow offshore area of the Niger Delta, Nigeria. Three (3) exploration wells have so far been drilled in the field, two (2) of which are reasonably vertical and the third highly deviated. Three (3) key reservoirs which are laterally continuous across the wells have been identified with proven oil and gas reserves. Pore pressure data from repeat formation test (RFT) measurements acquired in the deviated well show that the wells are entirely hydrostatic to true depth (TD). This research focuses on investigating how seismic amplitudes change with offset/angle of incidence in relation to varying pore pressure regimes at the shale-hydrocarbon sand and shale-brine sand interfaces using well data. The aim is to aid quantitative interpretation in an on-going field-wide exploration drive to de-risk hydrocarbon exploration in the deeper plays in the area which are below TD, and are expected to be overpressured. The study is hinged on end-member shale elastic parameter substitution in which the shales are subjected to varying overpressure regimes while keeping the reservoirs (sands) at in situ (hydrostatic) condition. The end-member shale property substitution simulated shale compaction dis-equilibrium as the main overpressure generation mechanism in this study. The results show that top gas sands, top oil sands and top brine sands would be visible on seismic in the deeper plays where pore pressures are expected to be very high, but with distinctive seismic amplitude with offset/angle behavior. The top gas sands are visible as blue loop with small positive reflection coefficients at the near offsets/angles, but with polarity reversal to red loop with negative reflection coefficients which become more and more negative at the far angles at hard overpressure regimes. Top oil sands are recognized as blue loop with large positive reflection coefficients at the near angles; the coefficients becoming less and less positive at the far angles/offsets. The top oil sands may not be detected on seismic at the far angles/offsets unless at very hard overpressures. Brine sands have similar seismic response as oil sands at hard overpressures, but can be distinguished from oil sands based on their much higher amplitudes over the entire offset/angle range. The study is also aimed at removing uncertainty in seismic-based pore pressure quantification at the deeper targets where there is absence of well data for calibrating pore pressure effects at varying conditions.
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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".