Bibliographic record
Abstract
This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 199981, “Use of Normalized Cumulative Production Curves To Estimate Ultimate Recovery of Unconventional Plays in North America,” by Ivan Olea, Hamed Tabatabaie, SPE, and Louis Mattar, SPE, IHS Markit, et al., prepared for the 2020 SPE Canada Unconventional Resources Conference, Calgary, 15-16 September. The paper has not been peer reviewed. Operators and investors are interested in finding better metrics to evaluate the production performance of unconventional multifractured horizontal wells (MFHWs). The complete paper discusses the use of cumulative production ratio curves normalized to a given reference volume in time for different unconventional plays in North America to investigate the median trend for each play and the median ultimate recovery per play. The paper discusses the choice of 12-month cumulative production for a reference volume as a normalization parameter. Introduction Many methods exist for forecasting the production rate from unconventional reservoirs, but all have limitations. Recently, several publications have appeared relating the expected ultimate recovery (EUR) to the initial rate or the cumulative production after 3, 6, or 24 months. In the complete paper, these publications are reviewed, and their learnings extended, to several unconventional reservoirs. Work in 2018 studied 147 MFHWs covering many formations in the Permian Basin and a wide range of input variables and determined EUR using rate transient analysis, numerical simulation, and decline-curve analysis. The authors of that work compared the EUR with various cumulative production intervals (3, 6, 12, and 24 months) and concluded that the correlation with 3 months was poor; 24 months’ cumulative production was an accurate predictor of EUR but was not considered to be an early-enough predictor. That work’s authors chose 12 months as a preferred early-time predictor of EUR and justified its use by stating that operating conditions have usually stabilized by that time. A universal type curve of cumulative production was created as a percent of EUR vs. years of production (e.g., after 1 year of production, 33% of the EUR has been produced). This type curve accounted for different well lengths and completions and both strong and weak wells. The universality of the type curve is consistent with the understanding that the factors that make a well a high- or low-rate producer affect the 12-month cumulative and the EUR proportionally. A different work from 2018 studied approximately 3,000 wells in the Delaware Basin to determine an early indicator of long-term performance. These authors used not only the EUR but also the actual cumulative production from 252 wells with 6 years of production history. Results showed that the correlation coefficient between the 6-year production and various early-time indicators (cumulative production at 30, 60, 90, and 180 days and 1, 2, 3, and 5 years) improves from 0.23 (for 30 days) to 0.73 (for 1 year) and only minimally thereafter. A 2012 work studied public production data from more than 6,000 wells from the Barnett, Fayetteville, and Woodford shales. These authors binned the data into quartiles and used a key performance indicator (KPI) that combines a confusion matrix and a cost matrix (penalty function). Results indicated that the prediction ability of various metrics ranged from 48% (3-month cumulative production) to 85% (2-year cumulative production) and that, in some cases, the peak month production KPI was only marginally lower than the 6- or 12-month cumulative.
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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 | 0.001 |
| 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 | 0.000 |
| 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".