An Integrated Field and Simulation Study to Understand the Role of Shut-in on a Montney Well Performance
Bibliographic record
Abstract
Abstract In this paper, we analyze and simulate the production data before and after an extended shut-in period from a horizontal well completed in the Montney Formation. After flowback and early post-flowback production, the well was shut-in for 7 months due to facility completion. When the well was reopened, the hydrocarbon production rates increased significantly compared to the values before the shut-in. To investigate the reasons behind this enhancement, we simulated three-phase production rates and bottom-hole pressure using the actual reservoir geological model. To match the production data before the shut-in period, we had to account for the reduction in oil and gas relative permeabilities due to water blockage. This was done by using multipliers of interblock fluid-flow transmissibility near the matrix-fracture interface. We used these transmissibility multipliers as matching parameters, to achieve the match between measured and simulated production data. However, the best history match was achieved, when the values of transmissibility multipliers are increased by 6.5 times after the shut-in. This suggests a significant increase in oil and gas relative permeabilities due to reduction in water blockage near fracture-matrix interface during the extended shut-in period. Since the simulation model was not able to capture the imbibition process controlled by different driving forces, we used transmissibility multipliers to mimic this phenomenon and its corresponding effects on production rates. In addition, we performed sensitivity analyses to investigate the effects of shut-in on the well productivity and economic profitability in terms of net present value (NPV). The results show that for this well, a 6-month shut-in period is optimal for maximizing NPV and hydrocarbon production.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".