Reconciling Retort versus Dean Stark Measurements on Tight Shales
Bibliographic record
Abstract
Abstract Due to the complexity of tight shale reservoirs, core analysis has become an increasingly important source of data for evaluating these systems. However, as there are no generally agreed upon testing protocols, there are competing methods for obtaining such primary data as fluid saturations and porosity. The two most commonly employed commercial methods are Dean Stark toluene combined thermal and solvent extraction and thermal extraction by retort. However, the impact of these protocols on the rock and its fluid phases is different, primarily due to the abundance of clays. While the Dean Stark extraction produces a total porosity and total water saturation, data suggest significantly elevated values of these parameters relative to what is measured through the retort process, resulting from significantly higher amounts of water recovered. This distinction is fundamentally important for using core analyses in shale for calibrating logs and/or determining reserves, as both methods claim to report the same parameters. To understand this effect, we have assembled a data set of compatible core analyses from various laboratories from several wells for a tight-gas shale reservoir in the United States. In addition, we conducted thermogravimetric analysis and Karl Fischer Titration with methanol extraction on splits of the same samples. The retort, thermogravimetric, and Karl Fischer data generally agree in the amount of water eluted from the samples (per gram of rock), while the Dean Stark data show significantly more water. We suggest this excess water could be a portion of the structural water in the clays, which should not contribute to porosity and saturation. Additionally, there is a relationship between this excess water and the total clay content from XRD. This correlation to XRD analyses suggests that a correction can be determined, leading to more accurate porosity and saturation values necessary for proper reserves estimations.
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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".