Shale oil production time series forecasting for multi-fractured horizontal wells with optimized artificial neural networks integrating multi-source data
Bibliographic record
Abstract
Time series forecasting is crucial for guiding capital investment, production enhancement, and optimization in the oil and gas industry. However, conventional data-driven approaches for the production prediction fail to meet the industry's criteria. This paper develops a hybrid model combining bidirectional long short-term memory (Bi-LSTM) or bidirectional gated recurrent unit (Bi-GRU) with multi-layer perceptron (MLP) and self-attention (SA), termed Bi-LSTM/GRU-MLP-SA, to predict shale oil production rates. The SHapley Additive exPlanations (SHAP) method is applied to enhance the model's interpretability. The proposed model architecture consists of five key components: input layers, Bi-LSTM/GRU layers, MLP layers, SA layers, and output layers. The Bi-LSTM/GRU captures temporal dependencies from time-series data, while the MLP captures relevant information from non-sequential data. The SA mechanism allows the model to focus on the most salient parts of the data. Compared to traditional single-technique models like standalone Bi-LSTM/GRU, Bi-LSTM/GRU with SA (Bi-LSTM/GRU-SA), and Bi-LSTM/GRU combined with MLP (Bi-LSTM/GRU-MLP), our Bi-LSTM/GRU-MLP-SA model demonstrates superior performance. Specifically, the Bi-GRU-MLP-SA variant achieved an average root mean square error (RMSE) of 0.2763, a mean absolute error (MAE) of 0.2192, and a mean absolute percentage error (MAPE) of 0.0490, indicating a higher accuracy and stability. In summary, the Bi-GRU-MLP-SA model is the most effective among the evaluated methods for identifying underlying trends in shale oil production and accurately predicting production levels.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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".