Using Drilling Data to Calculate Porosity & Permeability
Bibliographic record
Abstract
Porosity and permeability are two important parameters for reservoir characterization, formation evaluation, and stimulation design. Typically, the methods for obtaining these two parameters are not only costly and time consuming, but can have a high degree of uncertainty. Drilling data can provide great insight into downhole occurrences, but it is commonly overlooked as a source of information. In this work, drilling data and an inverted rate of penetration (ROP) model are utilized to determine unconfined compressive strength (UCS) values at any point where drilling data has been collected. Using UCS and porosity values collected from laboratory measurements, a porosity representative correlation for sandstone and shale formations is established. Additionally, taking gamma ray measurements into consideration, a porosity correlation for mixed lithology zones is developed based on field data from three previously drilled wells in Alberta, Canada. Porosity and permeability data found through laboratory testing for various sandstone and shale formations has been collected and used to establish a correlation between the two parameters for the individual formations. Using the sandstone and shale porosity-UCS correlations, the collected laboratory porosity data is used to determine UCS for the individual sandstone and shale formations. The permeability is plotted with the corresponding UCS values for the various formations and a correlation between the two parameters is developed. Determining UCS from drilling data allows for both porosity and permeability data to be found and could potentially have real-time application potential.Knowledge of porosity and permeability in unconventional horizontal wells could be beneficial to stimulation design. Having the ability to find the porosity and permeability from drilling data would mean that these two parameters are known throughout a lateral and ultimately allow for optimization of selective stimulation. This could potentially reduce the need for logging, laboratory testing, and overall cost.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".