New and Improved Methods for Performing Rate-Transient Analysis of Shale Gas Reservoirs
Bibliographic record
Abstract
Abstract Multi-fractured horizontal wells are currently the most popular method for exploiting low-permeability tight and shale gas reservoirs. Production data analysis is the most widely used tool for analyzing these reservoirs for the purpose of reserves estimation, hydraulic fracture stimulation optimization, and development planning (Ambrose et al., 2011). However, as pointed out by Clarkson et al. (2011), a fundamental problem with the application of conventional production data analysis to ultra-low permeability reservoirs is that current methods were derived with the assumption that flow can be described with Darcy’s law. This assumption may not be valid for tight/shale gas reservoirs, as they contain a wide distribution of pore sizes, including in some cases nanopores (Loucks et al., 2009). Therefore, the mean-free path of gas molecules may be comparable to or larger than the average effective rock pore throat radius causing the gas molecules to slip along pore surfaces. This results in slippage non-Darcy flow, which is not accounted for in conventional production data analysis. Clarkson et al. (2011) modified the pseudo-variables used for analyzing gas reservoirs in production data analysis to account for slippage. They demonstrated that if the effect of slippage is not considered, it leads to noticeable errors in reservoir characterization. Clarkson et al. (2011) also mentioned that even after using the modified pseudo-variables, the values for permeability and fracture half-length do not exactly match the input data to simulation. In this paper, a methodology to properly analyze the production data from a fractured well in tight/shale gas reservoir producing under a constant flowing pressure in the presence of desorption and slippage is presented. This method uses new pseudo-time definition instead of conventional pseudo-time currently being used in production data analysis. The method is validated using a number of numerically-simulated cases. It is found that the newly-developed analytical method results in a more reliable estimate of fracture half-length or contacted matrix surface area, if permeability is known.
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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 | 0.000 |
| 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".