Evaluating Multiple Methods to Determine Porosity from Drilling Data
Bibliographic record
Abstract
Abstract Porosity can be obtained from drilling data by using different correlations that relate the porosity to the unconfined compressive strength (UCS), which is obtained from drill bit inverted rate of penetration (ROP) models. Knowing the porosity at a given depth can benefit in helping to define the formations being penetrated and to characterize variations in a reservoir, thereby benefitting in selective stimulation. In this paper, previous studies that present methods for calculating porosity from UCS values will be compared and evaluated with sections of porosity that have been calculated from log data taken from three wells in Alberta, Canada. The correlations that will be compared include: Onyia, Sarda, Erfourth, and the UCS-gamma ray methods. The Onyia, Sarda, and Erfourth correlations are previously published while the UCS-gamma ray method correlates UCS in conjunction with the gamma ray at the bit. The porosity values that are found through these correlations are then plotted and their trends compared to each other as well as to the porosity obtained from log data in different sections from the well in Alberta, Canada. This process will help to determine what formation types are best correlated to the individual correlation. Typical drilling data is used in an inverted ROP model to obtain UCS. The UCS and gamma ray values are then taken and related to the porosity through the correlations presented in this paper and compared to the porosity determined from log data. Examining the different correlations that have been analyzed in various types of formations yield information indicating which correlation is best correlated to a specific formation type. The comparison's show that the predictability for some correlations are reasonable for limited datasets and sections of the well. To reasonably predict porosity values for mixed lithologies or shale formations, the integration of gamma log data is necessary. The trends exhibited from the correlations show that the comparison between porosity in shale is better seen when using the integrated UCS-gamma ray correlation. Utilizing the new UCS-gamma ray model seemingly indicates that this useful new method can more accurately predict porosity variations in mixed lithologies and in shale reservoir sections. Bettering stimulation placement as well as minimizing logging in the reservoir can greatly reduce the overall cost of the operation. The improved selective stimulation process could also allow for higher production rates and/or potential reduced stimulation cost, thus increasing overall profit.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".