Correction of Induction and Laterolog Charts for Evaluation of Gas Reservoirs
Bibliographic record
Abstract
Abstract One of the main parts of reservoir characterization is the estimation of porosity, lithology and water saturation. These are done in the pay zones by utilizing formation evaluation and logging tools consisting of sonic, resistivity, and radioactive logs. For this purpose, induction and laterolog charts in conjunction with the extent of conductivity of drilling fluid are used to decide whether Dual Induction Log (DIL) or Dual Laterolog (DLL) tools to be employed. In numerous gas reservoirs of Iran, water-based drilling fluids with low conductivity (salinity) are used to drill these reservoirs. Based on existing literature, DIL is the logging tool of the choice for evaluation of these reservoirs. However, comparison of variety of DIL and DLL results showed that this is questionable and is not always the case. To clarify these differences, more logging jobs were closely studied. It was observed that, regardless of the salinity of the drilling fluid, DLL gives better evaluations. This result is not in full agreement with the prediction of the reference DIL and DLL charts. Therefore, these charts need some modification. In this paper, the corrected charts developed from the field data which result in better predictions are given. Employing new corrections causes the appropriate tools to be used. This results in lowering the number of logging tool runs and the rig time which both lead to reduction of costs and lowering the probability of stuck logging tools. Moreover, using these new charts helps to distinguish all oil and gas layers which may be missed if the previous procedure is used.
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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".