Use of Pressure Transient Analysis for Monitoring SAGD Steam Chamber Development
Bibliographic record
Abstract
Abstract One of the major problems faced in steam assisted gravity drainage (SAGD) development is monitoring steam chamber growth and conformance in a cost-effective way. Currently, this is primarily done using 4D seismic. However, each survey costs several million dollars, can generally be taken once a year in winter, and is limited in resolution. This paper investigates the use of pressure transient analysis (PTA) to monitor steam chamber conformance as a low-cost and on-demand alternative. While PTA has been widely and successfully used in the conventional oil and gas industry, there has been little application to SAGD. A range of cases were investigated using reservoir simulation including steam flooding in a vertical well, 2D homogeneous and 3D heterogeneous SAGD cases. The heterogeneous cases included variations in permeability and shale layers along the injector. Pressure responses from different pressure gauge configurations such as a single gauge and multiple gauges along the injector were analyzed. Results indicate that PTA can estimate total chamber pore volume reasonably accurately, despite a number of non-ideal conditions such as heterogeneity in saturations and countercurrent flow. Results also indicate that PTA potentially can be used in a number of ways to monitor steam chamber growth. First, changes in total steam chamber volume potentially can be monitored by observing changes in pseudo-steady state behavior (PSS) over time. More importantly, the character of the derivative plot changes with the maturity/shape of the chamber, indicating that the derivative plot can be used as a qualitative tool for monitoring steam chamber growth. In particular, the derivative behavior at a pressure gauge is governed largely by local steam chamber conditions near the gauge so that multiple gauges down the horizontal section can be used to monitor how different parts of the steam chamber are growing. However, there are limitations on what can be discriminated; differences in chamber shape have to be major for the derivative responses to be significantly different.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".