Modeling Fractured Horizontal Wells As Dual Porosity Composite Reservoirs – Application to Tight Gas, Shale Gas and Tight Oil Cases
Bibliographic record
Abstract
Abstract Multiply-fractured horizontal wells are an efficient way to produce from tight gas, shale gas and tight oil formations. In this work, we present a linear composite model with a dual porosity inner zone to model production from a multiple fractured horizontal well. The composite solution uses linear dual porosity flow solution for the inner reservoir and a linear single porosity solution for the outer reservoir combined with continuity of pressure and flux at their interface. Solution to the problem was obtained in Laplace space. The solution that we have obtained is simple and fast, yet effective and can be applied to model production from fractured horizontal wells. For the cases of interest, we observe three linear flow periods in this model. The first linear flow is from the fractures into the wellbore, followed by linear flow in the matrix to the fractures and lastly linear flow in the outer single porosity reservoir to the inner reservoir. Each of these three linear flow periods is separated by a transition depending on the properties of the fracture, matrix and the outer reservoir. We use a numerical simulator to examine the validity of some of the assumptions made in the development of the work. While the model that we have made consists of only linear flow solutions, the numerical model accounts for two dimensional flows in all media. New solutions are presented in the form of type curves.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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 source (direct Gemma or distilled Codex), 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".