Use of Normalized Cumulative Production Curves to Estimate Ultimate Recovery of Unconventional Plays in North America
Bibliographic record
Abstract
Abstract Operators and investors are interested in finding better metrics to evaluate the production performance of unconventional multi-fractured horizontal wells (MFHWs). This paper discusses the use of cumulative productionratio curves,normalized to a given reference volume in time (e.g. 12-month cumulative production) for different unconventional plays in North America to investigate the median trend for each play, and investigate the median ultimate recovery per play. The selection of using 12-month cumulative production as a reference volume as a normalization parameter is discussed. Historical production data from thousands of MFHWs in unconventional plays in the US (Bakken, Barnett, Eagleford, Fayeteville, Haynesville, Marcellus and Permian) and Canada (Duvernay, Montney and Horn River) was used to calculate normalized cumulative production curves for theprimary fluid, using different cumulative reference volumes at different points in time (e.g. 6, 12, 24, 36, 48 and 60 months). The observed trends for each of the selected plays werestudied using data analytics tools. A two-segment hyperbolic decline was used to match the median production trend to estimate the long-term performance of each play. Depending on the data variance, some plays exhibit more clear trends than others. By using normalized cumulative production curves, general profiles for each play were generated and compared. These Cumulative Production Ratio Profiles (CPRP) were extended using a two-segment hyperbolic equation to determine the Expected Ultimate Recovery Ratios (EURR) per play. Once a well in a region has been on production for a minimum duration equal to the reference time (e.g. 12 months), two results are readily determined: a) the EUR, and b) the production profile. The EUR is obtained simply by multiplying the appropriate EURR by the well's 12-month cumulative production; and the production profile is obtained by using the CPRP (cumulative production ratio profile) of the play and multiplying it by the 12-month cumulative production of the welland converting the results to daily rates. This cumulative plot serves as a normalized typewell for the region and can be used to guide the production forecasts of wells with a short production life.
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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 | 0.000 | 0.001 |
| 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".