Wavelet Modelling Approach for Reservoir Model Classification
Bibliographic record
Abstract
Abstract In oil and gas industry it is crucial to have reliable information on well, reservoir and boundary types and properties. Detailed information can be extracted from a proper interpretation of pressure and rate transients of well testing data. Though, there are times that even with an in-depth pressure transient analysis, a unique solution on well, boundary and especially the reservoir types cannot be obtained and makes it difficult or even impossible to extract correct information. In this study deep learning (DL) is used to tackle this problem by differentiate possible reservoir models and select the most appropriate model based on pressure derivative response. Accuracy of the classification model on real field data with known models is also explored. Reservoir models can be identified by measuring the downhole pressure data and analyzing the changes in trends in pressure curves and especially pressure derivative curves. In this study, different DL algorithms are used to identify the basic characteristics of pressure derivative curves to determine reservoir model. Several possible well/reservoir/boundary types are considered to select the best model that can be used for well/reservoir/boundary property estimation. Before feeding the networks, training data curves would be shrunk in size using wavelet transform (WT) which is able to sustain the pressure derivative features in a much-compressed form to accelerate algorithm training and testing. The technique used in this work is a time-efficient process that learns important signatures of pressure derivative curves to classify reservoir models. Unlike the conventional well testing methods in which models are determined from the visual inspection of the pressure and pressure derivative plots, the technique used in this study was trained with a dataset consists of hundreds of reservoir models generated by solving diffusivity equation under different well, reservoir, and boundary conditions. The procedure was applied to multiple field examples with known reservoir model and reservoir properties and proved the consistency and flexibility of the methodology for true reservoir model selection. DL-based models also shown to be very handy with excellent computational efficiency especially when dealing with the complex patterns on the pressure derivative curves. The study showed that the method has great capability to classify pressure derivative and can also tolerate noise when applied on real pressure data. Large dataset used in this study can increase the comprehensiveness of the training and test data sets. The big advantage of the DL-based approach was the improvement in the pattern recognition of the pressure derivative curves without the need of any feature handcrafting or any prior knowledge of well, reservoir, and boundary types. ML proved to be a reliable, fast, and accurate technique that can significantly improve the process of well, reservoir, and boundary type detection based on pressure derivative curves.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".