New Data-Driven Method for Predicting Formation Permeability Using Conventional Well Logs and Limited Core Data
Bibliographic record
Abstract
Abstract The need for improved data accuracy, cost effectiveness and delivery time to assist in decision making has gained importance in reservoir characterization and evaluation. This paper presents a robust and inexpensive data-driven method for predicting the formation permeability profile from conventional well logs (CWLs) using the support vector regression (SVR) technique with limited core measurements. The method's feasibility and applicability are demonstrated on one field data set from a North Sea well contained a complete suite of logs and extensive core measurements. The relationship between formation permeability and well logs is often overwhelming complex and nonlinear. We use the SVR method to establish the correlation between CWLs and limited core permeability, thereafter building a permeability-prediction model as a function of selected well logs. The basic logging data used here include density, neutron, deep resistivity, compressional and Stoneley wave slowness. The permeability derived from these well logs generally compares well with the measured core permeability. Additional logging data including the clay weight fraction, thorium/potassium content, or nuclear-magnetic-resonance (NMR) bulk volume movable and irreducible are also separately integrated into those basic logs to determine if the prediction accuracy can be improved. There is no obvious difference among the predicted permeability profiles even these additional well logs are added, which could imply that the basic logs are sufficient to generate the permeability with good accuracy. SVR method could be used to improve the log interpretation accuracy as shown in this study. It can be easily adapted to predict other rock electrical, mechanical and petrophysical properties when only conventional logs and few core measurements are available. It is especially useful for unconventional reservoirs where traditional models may not be applicable and new methods are still evolving. Such new data analysis technologies could optimize our logging service and core analysis planning.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".