Bibliographic record
Abstract
Abstract In drilling simulation of wells there exists a need for accurate determination of the percentage of the different lithologies as a function of depth to better predict the bit wear distribution for different drill bits. A simple approach using Gamma Ray (GR) log readings for shallow wells in Alberta, Canada was initially used for lithological determination. It is generally in good agreement with shale and sand lithology content and where there are no other probable lithological types as in deeper formations in the area where limestone, dolomite, coal and anhydrite exists. It is in many cases difficult to understand the exact behavior of log responses especially in complex lithologies. A new cross plot approach is used in this paper to take into account all the lithological possibilities. The cross plots are normally used in well log analysis for interpreting both for lithology type and quantity using two or more forms of data. It is also a convenient way to analyze the log responses together; and set the limitations for each curve with boundaries. In this paper, the neutron density cross plot is used to obtain the different type and percent lithology. All the lithological differentiations are calibrated as mathematical models using offset wells. The two methods (using only GR and the new cross plot model) are compared for use in central Alberta, Canada wells. In conclusion it is seen that the GR method match for shale and sand sequences and mixtures but that the cross plot approach matches both sand and shale sequences as well as formations where limestone and dolomite exist. Introduction During the past years, formation evaluation methods have been developed using log data for lithological determination. Well logs give a continuous survey of the formation crossed by the well and it has been shown that it gives a good idea of the lithology. Physical parameters can be recorded by logging tools for lithological description of the formation (i.e. Gamma ray spectrometry and photoelectric). A common method in log interpretation is to use different kind of cross plots for various log readings which are useful in simple formation types but become ambiguous for complex lithologies. The accuracy is also dependant on the geological history of the area.1, 2 Different types of log data can be used for lithological determination purposes with the help of the cross plots. Cross plots with new boundaries are used with log data to predict formation lithological percentages utilizing for the geological sequences in Central Alberta, Canada. The new cross plot approach is compared to drilled cuttings collected versus depth from wells in the area with good agreement. Technical Approach Subsurface lithological interpretations were historically obtained from collected drilled cuttings whereas today when using wireline logs all the detailed data for an entire interval or section is obtained. Different logs respond differently to matrix minerals and the analysis of the logs can be used to estimate the formation type and characteristics in most all lithologies.
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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 | 0.001 |
| Open science | 0.001 | 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".