Shale Swelling Tests Using Optimized Water Content and Compaction Load
Bibliographic record
Abstract
Abstract Since preserved shale core is rarely available, swelling tests are usually performed by creating shale pellets using ground-up cuttings. Room-dry ground cuttings are mixed with water, and then compressed under load in a compaction cell. In this paper we study the effect of different water amounts and compaction loads for five different shales. We find that both the optimum amount of water to use, and the optimum compaction load, are functions of the shale porosity. Lower-porosity shales require less water and greater compaction load, compared to higher-porosity shales. Pore size distributions of pellets vs. intact samples, and pellet saturation calculations, reveal the reasons for this. We also find that the optimum amount of water further depends on the amount of smectite in the shale, because smectite-rich shales hold more water in the room-dry state. However, this held water is easily determined by comparing the weight of room-dry ground shale with the weight of oven-dry ground shale. Porosity is easily determined from shale in situ bulk density. We therefore present simple relationships for calculating the required water amount and compaction load. A ‘calculator’ is presented which is applicable to nearly all shales covering a large range of porosities and clay mineral contents. It requires only two inputs: the shale in situ bulk density (e.g. from a well log), and the oven-dried weight of the room-dry ground shale. This calculator allows one to easily determine the appropriate water amount and compaction load for creating an optimum shale pellet. These findings allow one to create optimum shale pellets for swelling tests, which come closest to reproducing the in situ shale and yet are also fully-saturated. With optimized pellets, drilling fluids and completion fluids can be confidently ranked in terms of ‘bestto-worst’ swelling for any particular shale, as shown by the example swelling results in this paper. Without using optimized pellets, test results can be affected by various testing artifacts, such as capillary swelling due to incomplete saturation.
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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 | 0.000 | 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".