A New Methodology to Predict Condensate Production in Tight/Shale Retrograde Gas Reservoirs
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Bibliographic record
Abstract
Abstract Due to suppressed natural gas price in the past several years in North America, liquid-rich retrograde gas reservoir development has been the main focus for many gas reservoir operators in Canada. Due to the subsurface complexity of PVT behavior for condensate, liquid (condensate) production forecast has been a challenge for operators. In addition, many liquid-rich retrograde reservoirs have also encountered extremely low permeability, which makes the liquid production forecast an even more challenging task for operators. Today, the most common methodologies to analyze production performance for retrograde gas reservoirs are limited to either numerical (simulation) or empirical (such as Arps’ decline). However, for numerical analysis, original PVT properties, special core analysis (SCAL) and pressure history are required as input data, which are usually very costly to obtain and they are, therefore, routinely ignored by operators. This paper presents a simple way to predict condensate production from the gas production by means of readily available early years’ production data. This simple methodology includes a new specialized plot to find related parameters for condensate production forecast without any costly PVT and pressure history data. Moreover, a set of diagnostic plots has been developed to identify the degree of the blockage to the gas production from the near wellbore oil-bank. This new methodology has been tested on more than one hundred horizontal wells that have been producing retrograde gas from several Western Canadian formations, such as the Notikewin, Glauconite, Montney, Falher as well as the Eagle Ford formation in the United States. All such tests were carried out by using only the early part of the production data to history-match the later part of the production history. The results have shown good agreement with the forecast based on the new methodology. Both synthetic and real well examples will be presented in this paper to illustrate the use of this new methodology.
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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 | 0.001 | 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 it