Bibliographic record
Abstract
Summary Complex geometry reservoirs can be encountered in the field for a variety of depositional and tectonic processes. For example, fluvial depositional environments may produce interbranching channel reservoirs or reservoirs consisting of relatively high-permeability channels in communication with low-permeability splays. This paper presents a general methodology for computing pressure responses and flow characteristics in complex geometry reservoirs. The proposed method consists of decomposing the original complex-geometry reservoir into a set of simple-geometry reservoirs, which interact with each other by transfer of fluid and equality of pressure over the regions where they are in hydraulic contact. Analytical solutions are written for each of the simple reservoir components in terms of the unknown pressures and fluxes at their boundaries, and the coupled systems are solved for the desired wellbore pressure responses. The method of sources and sinks is used to compute the pressure response in the Laplace domain, and the results are inverted numerically with the Stehfest Inversion algorithm.1 We present fast, accurate methods of taking numerical Laplace transforms of the source/sink solutions that make the computations reasonably fast and efficient. The proposed methodology can be extended to any system (infinite or bounded) in which the Laplace-space solution can be written easily in terms of integrals of real-space source/sink functions, including production at constant bottomhole pressure, wellbore storage effects, or naturally fractured systems. We demonstrate the applicability of the method by modeling branching channels and channel/splay systems.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".