Multivariate Time Series Modelling Approach for Production Forecasting in Unconventional Resources
Bibliographic record
Abstract
Abstract Evaluating the potential of the unconventional resources is a key for the development of this type of reservoirs. The currently adopted models for the well production forecast including decline curve analysis often fail to capture the complexity of flow performance by over-simplifying it and cannot produce reliable results due to the operational problems and most importantly the inadequate production history. In this study, a deep learning approach is developed to predict the long-term well performance based on a moderate duration of production data. A data-driven procedure was implemented based on deep neural networks for flowrate predication using multivariate inputs. The production forecast was formulated as a time series regression problem where multiple inputs including tubing-head pressure and bottom-hole temperature are used as the input of a reverse model that estimates flow rate. Different recurrent neural networks (RNNs) 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 accurate model of production forecasting. The method presented in this paper provided a time efficient process which learned multi-domain sequence and was used to forecast production in unconventional resources. The developed deep learning networks did not require any feature handcrafting and could learn directly form the raw data. Reconstructed and predicted flowrates using deep learning was also used to estimate missing flowrate history. The study showed that deep neural networks have great capability to tolerate noise and optimize computation when multivariate input is used. The technique can also be applied to other type of forecasting problems of prediction of pressure and rate in conventional reservoirs, prediction rate from temperature, and multi-well production forecasting.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".