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 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".