Fluid Flow, Pore Pressure, Wettability, and Leakage in Mudstone Cap Rocks
Bibliographic record
Abstract
Abstract This chapter considers some of the issues surrounding the modeling of one- and two-phase fluid flow in mudstones. For single-phase flow, key relationships include those between porosity and (1) effective stress, (2) permeability, and (3) capillary breakthrough pressure. All three relationships are strongly influenced by the grain-size distribution or clay fraction of mudstones, but a quantitative description is currently only available for the porosity-effective stress relationship. The importance of lithology or clay fraction as a control on the key flow properties of mudstones indicates the practical significance of estimating clay fraction directly from geophysical logs. This chapter illustrates how artificial neural networks can be used to perform this task. Having considered some of the basic flow properties of mudstones, the second part of the chapter discusses aspects of two-phase flow through mudstone pore systems. Rates, mechanisms, and pathways of petroleum leakage through mudstone pore systems remain poorly constrained. In this chapter, field and experimental data is combined with theoretical arguments to suggest that once a water-wet cap rock is breached, the leak path will become more oil wet as a result of sorption of hydrophilic and ultimately hydrophobic organic compounds onto mineral surfaces. Oil-water partition of hydrophilic organic compounds in reservoirs, followed by diffusion into cap rock pores may even create oil-wet pathways into cap rocks and permit leakage. In these cases, cap rocks simply retard the vertical migration of petroleum, and column height is a function of the rates of petroleum supply and loss. Modeling the rate of loss of petroleum requires a better understanding of mudstone relative permeability.
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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) | 0.001 | 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 | 0.001 |
| 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".