Transient Response in Arbitrary-Shaped Composite Reservoirs
Bibliographic record
Abstract
Summary Use of regular-shaped analytical models to history match geometrically complex reservoirs may provide misleading results, especially in situations where the transients are in the transition regime. In this regime, the transients are somewhere between infinite-acting and fully boundary-dominated flows. This means that some, but not all, of the reservoir boundaries influence flow dynamics. As a result, analytical models are unable to accurately capture the effects of the geometrically complex reservoir boundaries; the more irregular the reservoir or drainage area geometry is, the greater its influence on well performance, and, therefore, the greater the deviation of analytical models from field behavior. The boundary element method (BEM) has been used to model arbitrary-shaped reservoirs; its input data requirement is similar to that of analytical models. In this paper, we present the Laplace-transform BEM formulation within a framework that lends itself to developing the solution for arbitrary-shaped and/or composite reservoirs of increased complexity than is currently available in literature, thus eliminating the need for first-principle derivations. The focus here is on two-dimensional flow of a slightly compressible fluid. Our formulation is not only useful for investigating the influence of complex reservoir geometries on well performance, but also for history matching and forecasting multiwell performance, for studying the effect of large-scale reservoir heterogeneities, and for simulating mixed reservoir boundary conditions (including no-flow and constant-pressure boundaries). It permits modeling different well completion types, including hydraulically fractured vertical wells and multiply-fractured horizontal wells. In addition to modeling the response of rate-controlled wells, we also provide solution for pressure-controlled wells.
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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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 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 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".