Single and Multi-Well Synthetic Well Log Generation using Multivariate Analysis
Bibliographic record
Abstract
Abstract With the rapid development in machine learning (ML) and deep learning (DL) algorithms, computations power and availability of the massive amount of data, ML and DL approaches have gained a lot of interest in oil and gas industry and brought the data science and analytics into the forefront of this industry. Using traditional neural networks (NNs) to normalize log and generate synthetic well logs is not a new idea. However, the recent advancement in ML and DL methods encourages to further research and revisit the prediction power, discuss the methodological limits and further improve the approach and prediction algorithms in single and multi-well synthetic well log generation for reservoir characterization and formation evaluation. In this study a data-driven procedure was implemented based on deep neural networks for density generation using multivariate inputs of well log data. The density prediction was formulated as a depth series regression problem where multiple inputs including different open hole logs are used as the input of a reverse model that estimates density. Different recurrent DL structures including Long Short Term Memory, Gated Recurrent Units, and Bidirectional Recurrent Neural Networks were tested in this study to select the most time-efficient and high performance model for density log generation. The models were fed with different number of available curves to explore a relationship and potential predictive power of several variables on the target curve (density) to eliminate unnecessary and irrelevant curves and end up with a model that uses a few curves to generate the target curve. Training datasets consist of real well log data of 25 wells logged offshore Middle East. Different metrics (RMSE, MAE, and R2) were used to compare the performance of the models and stacked LSTM of three layers showed the highest performance. Using this approach companies could cut costs by generating high quality synthetic data with a faster turnaround and without the need of relogging.
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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".