Best Practice of Using Empirical Methods for Production Forecast and EUR Estimation in Tight/Shale Gas Reservoirs
Bibliographic record
Abstract
Abstract Since 2008, a number of new empirical methods have been introduced to the petroleum industry, specifically for gas wells in tight and shale reservoirs. Among them, Valko’s Stretch Exponential Production Decline (SEPD) and Duong’s Rate Decline for Fractured Dominated Reservoir are two most mentioned methods by industry experts. These two methods are claimed to be accurate in both tight and shale gas wells. However, since the reservoir rock permeability in those reservoirs ranges from 0.1 to 0.0001 mD in tight gas to less than 0.0001 mD in shale gas, are these two empirical methods still applicable in all ranges of permeability? This paper presents the results of an evaluation study by applying these two empirical methods to tight and shale gas wells under a wide range of permeability generated based on both actual and synthetic production data. The key findings and recommended improvements resulted from the study are: Both SEPD and Duong’s Rate-Decline Methods are not applicable to tight gas reservoirs with a permeability ranging from 0.1 to 0.001mD. Duong’s Rate Decline Method will significantly over-estimate EUR whereas SEPD will most likely mismatch the production history as well as yielding a lower EUR while using its Recovery Potential Curve to find parameters.A Modified SEPD Method (YM-SEPD) developed by the author is a much easier and versatile method to use, and most importantly it will yield a more reliable production forecast and EUR estimation.With respect to reservoirs with rock permeability less than 0.01mD, a more rigorous step-by-step work flow using Duong’s Method has been proposed. Moreover, for tight gas reservoirs Duong’s Method can only be used for production forecast during early years, prior to pseudo-steady state (PSS) flow.Hundreds of horizontal wells including both oil and gas wells, from various formations (Cadomin, Montney, Notikewin, Cardium, etc. in Canada) and under different hydraulic fracturing conditions, have been analyzed using these three different methods. Results indicate that the new YM-SEPD Method will yield a more reliable EUR as well as production forecast in comparison with other two methods, especially when there is only less than 2~3 years of production history.For wells having less than 2 years of production history, the YM-SEPD Method will also be able to yield a reasonable prediction by coupling with Duong’s Decline Method
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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 | 0.008 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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".