Semi-Analytical Reservoir Modeling of Non-Linear Gas Diffusion with Gas Desorption Applied to the Horn River Basin Shale Gas Play, British Columbia (Canada)
Bibliographic record
Abstract
Adsorbed gas may account for a significant part of the gas resources in shale gas and coalbed methane plays. Understanding gas sorption behaviors and integrating gas desorption into analytical reservoir modeling and an associated transient performance analysis are important for evaluating a system’s gas desorption ability and further analyzing its CO2 injectability, utilization, and storage capacity. However, gas desorption, along with other pressure-dominated gas properties, increases a system’s non-linearity in theoretical studies. Few studies on analytical modeling have integrated the gas desorption feature into a non-linear system and validated the model’s accuracy. In this study, the desorbed gas due to pressure decay was treated as an additional source/sink term in the source-and-sink function methods. This method was combined with the integral image method in a semi-analytical manner to determine the amount of gas desorption. Fundamental reservoir and gas properties from the Horn River Basin shale gas play were chosen to evaluate the methodology and the performance of the associated production well. The results were compared with the commercial fine-gridding numerical simulation software, and good matches were achieved. The results showed that the desorbed gas released from rock will supply free-gas flow when the pressure significantly decreases due to gas production. The production wellbore pressure can be maintained at a higher level, and the production rate was higher than in cases where gas desorption was not considered, depending on the operating conditions.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".