Production Forecasting for Shale Gas Reservoirs With Fast Marching-Succession of Steady States Method
Bibliographic record
Abstract
In this paper, we introduce fast marching-succession of steady-states (FM-SSS) method to predict gas production from shale gas formations. The solutions of fast marching method (FMM) will describe the dynamic drainage boundary, and the succession of steady-state (SSS) method is applied to predict the gas production within the drainage boundary. As only the grids within drainage need to be taken into calculation at each time-step, this approach works much more efficiently than the implicit finite difference method, especially, at the early stage of production when the drainage is relatively small.We combine FMM with SSS to conduct reservoir simulation and predict gas production in shale gas reservoirs. The pressure profiles of transient flow are approximated with the pressure profiles of steady-state flow in our approach. The difference between the proposed method and the conventional SSS method is that we provide an efficient method to characterize the boundary conditions. In the conventional SSS method, the boundary pressure has to be measured, which is inconvenient for simulation purposes, whereas FM-SSS method takes the dynamic drainage as a changing boundary and approximates the drainage boundary pressure with initial reservoir pressure, such that the boundary condition can be numerically characterized. A major advantage of our approach is that it is unconditionally stable and more efficient than the implicit finite difference method because much smaller-scale linear equations need to be solved at each time-step.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".