Production Decline Analysis of Oil and Gas Resources with Robust Fit and Time Series Analysis
Bibliographic record
Abstract
Production decline analysis is widely used in petroleum industry for reserve estimation, well/field life span prediction, and economic analysis. In this paper, a comprehensive study is conducted to evaluate, enhance and compare the performance of both parametric and non-parametric models in terms of their application in production decline analysis. Instead of using ordinary fit, we propose to apply robust fit to train the parameters in the parametric models. Based on the production data collected from 11 wells, we demonstrate that robust fit can create more accurate linearisation and better model the trend of production data than the conventional ordinary least-squares fit in Duong's model (2011), although it gives comparable performance as the ordinary least-squares in Arps' exponential decline model (1945). Compared to the original Duong's model (2011) and the enhanced Duong's model (2011) with robust fit, our proposed ARIMA model can provide much higher accuracy in terms of P50 predictions. [Received: January 22, 2016; Accepted: October 20, 2016]
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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 | 0.001 | 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".