Forecasting Petroleum Production Using the Time-Series Prediction of Artificial Neural Network
Bibliographic record
Abstract
The purpose of this paper is to present a special back-propagation neural network (BPNN) with two techniques of the optimal learning time count (OLTC) and the time-series prediction (TSP) for forecasting petroleum production in Chinese oilfields, as well as algorithm applicability. In general, when different algorithms are used to solve a real-world problem, they often produce different solution accuracies, and an algorithm is used to solve real-world problems, it often produces different solution accuracies. Toward this issue, the solution accuracy is expressed with the total mean absolute relative residual for all samples, R (%); and it is proposed that an algorithm is applicable if R (%) ≤ 5 , otherwise this algorithm is inapplicable. Two case studies of China have been used to validate the proposed approach. The application results of this special BPNN are R (%) = 2.18 in Case study 1 while R (%) = 2.05 in Case study 2. From these results, it is concluded that: (a) this special BPNN for forecasting petroleum production in Chinese oilfields is feasible and practical; and (b) the definition of solution accuracy R (%), and the threshold of algorithm applicability ( R (%) ≤ 5 ) for an algorithm, are feasible and practical, too.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".