Analyzing Production Data from Unconventional Gas Reservoirs with Linear Flow and Apparent Skin
Bibliographic record
Abstract
Abstract Many horizontal wells with multiple fractures, producing from unconventional gas reservoirs, have been observed to exhibit linear flow. Often it is the only flow regime, and it can last several years. Classically, this flow regime is characterized by a half slope on type curves, when there is no skin effect. However, most of the time, a skin effect is observed in these wells (caused by flow convergence or finite conductivity in the fractures). The existence of skin changes the shape of data points when plotted on log-log scales, and this can have a huge effect on the interpretation when using type curves. For example, a well with purely linear flow and with skin, in a reservoir that is infiniteacting, may appear like, and be interpreted as, a finite-acting reservoir, simply because of the "skin effect". This paper discusses different methods that can be used to eliminate the misinterpretation caused by the presence of skin when linear flow is being analyzed using type curves. First, it is shown that among all the derivative and integral functions that are currently used, only DER (welltest-style semi-log derivative) and pressure integral-derivative are not affected by the skin. However, these two functions have other issues that usually make them unusable for production data analysis. DER is noisy most of the time and the process of integration often introduces errors at early times, which can significantly distort the shape of pressure integral-derivative. Therefore, an easier method is presented to analyze the production data from shale gas reservoirs with extended periods of linear flow and significant skin. This method uses the square root-time plot to remove the apparent skin effect from the data. Then the data (excluding skin) are used for type curve analysis. This simple procedure prevents potentially significant misinterpretation.
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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) | 0.001 | 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".