A Material-Balance Equation for Stress-Sensitive Shale-Gas-Condensate Reservoirs
Bibliographic record
Abstract
Summary During the last few years, production of liquid hydrocarbons has been reported from the gas-condensate window of the Eagle Ford, Barnett, Niobrara, and Marcellus shale plays in the US. This paper presents a new material-balance equation (MBE) for estimation of original gas in place (OGIP) and original condensate in place (OCIP) in shale-gas-condensate reservoirs. This material-balance methodology allows estimating the critical time for implementing gas injection in those cases in which condensate buildup represents a problem. In addition, the proposed MBE considers the effects of free, adsorbed, and dissolved gas-condensate production, and also takes into account the stress-dependency of porosity and permeability. An extension of the methodology is implemented for estimating the optimum time for hydraulically refracturing shale-condensate reservoirs. The new MBE applies to shale-gas-condensate reservoirs by incorporating a two-phase gas-deviation factor (Z2) and total cumulative gas production (Gpt) that includes both gas and condensate. If a crossplot of P/Z2 (pressure/Z2) vs. Gpt is prepared for a conventional gas-condensate reservoir, a single straight line is obtained. However, when the single-phase gas-compressibility factor (Z) is used, a deviation from the linear behavior is observed after the reservoir pressure falls below the gas dewpoint. This methodology is applied in this study to unconventional shale-gas condensate. Because there are three characteristic stages of production in a shale-gas reservoir (production of free, adsorbed, and dissolved gas), the location of the aforementioned deviation will provide a hint of the production stage that will be affected by condensate buildup. For example, if the deviation point is in the region where production of free gas is predominant, then the production caused by desorption mechanisms will be negatively affected because condensation will have already occurred in the reservoir, resulting in reduction of effective permeability to gas. This methodology then allows estimating the critical time for implementing gas injection on the basis of the total cumulative gas production. The method also permits estimating the optimum time for refracturing. The refracturing can be of a normal size for a given shale (similar to the original fracturing job), or it can be a superfrac job. Results are presented as crossplots of (1) P/Z2 vs. Gpt, (2) Gpt vs. time, and (3) gas rate vs. time. It is concluded that estimation of the critical time for implementing gas injection is useful for improving the performance of those shale-gas-condensate reservoirs in which condensate buildup represents a threat that can negatively affect the gas-production rate. The novelty of this work resides on the fact that the combined effect of free, adsorbed, and dissolved gas-production mechanisms on stress-sensitive shale-gas-condensate reservoirs had not been considered previously in the literature for estimation of OGIP, OCIP, and reservoir performance with an analytical MBE. The inclusion of gas injection and refracturing had not been considered either.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".