1D Hydro-Geomechanical Modelling of Pore Pressure on an Active Convergent Margin: East Coast Basin, New Zealand
Bibliographic record
Abstract
Abstract This study aims to understand the causes of anomalous pore fluid pressures within sedimentary sequences of an active tectonic basin through well log analysis, pressure data evaluation and thermo-hydro-geomechanical modelling. The study focuses on the East Coast Basin (ECB), New Zealand, an active convergent margin, where anomalously high pore pressures have been encountered in deep-water systems at burial depths as shallow as 200 m. A regional investigation including analysis of the Cretaceous to Holocene tectono-stratigraphy and diagenetic histories of the ECB, was combined with seismic and well log interpretation to understand the structural and sedimentation history of the ECB, and thus the main factors that were likely to contribute to overpressure generation/dissipation and porosity loss. 1D hydro-geomechanical models were then built to undertake a parametric study to investigate the effect on porosity and pore pressure evolution of different sedimentation and erosion rates, hiatus periods, different erosion thicknesses, and tectonic compression. The parametric models show that high overpressures can be preserved during rapid erosion events due to the relatively small timeframe for pore pressure dissipation, depending on sediment permeability. Furthermore, only recent erosion events are relevant to the present-day overpressure. In addition, high levels of tectonic compression (12.5 %) applied in recent events can produce both high pore pressure values and significant porosity reduction if the sedimentary column was undercompacted prior to the tectonic compression. Learnings from the parametric studies were used as a starting point to understand controls on the pore pressure and porosity in the Opoutama-1 well, located on the onshore area of the ECB. Results from the Opoutama-1 well show that the high pore pressure registered at shallow depths (< 1 km) in this well is significantly driven by tectonic compression as a result of high subduction rates (presently 48 mm/yr). Disequilibrium compaction also contributed to overpressure generation due to high sedimentation rates (up to ~3000 m/Ma). However, much of the disequilibrium compaction-related overpressure was dissipated during uplift, hiatus, and erosion. Where overpressure is preserved, it is related to thick (up to 1 km) mudstone packages deposited during the Cretaceous to Holocene, thin low permeable layers of limestones deposited during the Miocene to Pliocene and marl intervals with high content of smectite.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 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".