Research on data‐driven model for soft sensing of natural gas production system
Bibliographic record
Abstract
Abstract In view of the problems of high cost and low reliability in obtaining operation information such as flow rate and pressure of offshore natural gas production system, research on soft sensing is carried out, and a dynamic data‐driven model bank is established, in purpose of estimating single‐well flow rate and wellhead pressure, providing convenience tool for online monitoring and system safety analysis. Combining dynamic and steady‐state samples, introducing black‐box identification techniques including orthogonal least square regression and deep learning along with parameter correction techniques such as bi‐objective least square algorithm and transfer learning, a series of nonlinear auto‐regressive models with exogenous inputs (NARX) are built, consisting of black‐box and gray‐box polynomial NARX (Poly‐NARX) models as well as deep neural network NARX (DNN‐NARX) models, approximately describing the dynamic performance of gas production well. Through realistic operation data, the simulation results of Poly‐NARX, DNN‐NARX, and multiple‐layer‐perception‐NARX models are compared. It is observed that gray‐box DNN‐NARX model shows the best performance with advantages of higher global applicability, better approximation ability, and stronger generalization ability. Proposed model bank is of high expansibility and engineering applicability for soft sensing problems in the petroleum industry, laying the ground work for building smart oil and gas field.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 | 0.000 |
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".