Transient Nonisothermal Fully Coupled Wellbore/Reservoir Model for Gas-Well Testing, Part 2: Applications
Bibliographic record
Abstract
Summary After the development of a numerical fully implicit nonisothermal wellbore/reservoir simulator in Part 1 of this study (Bahonar et al. 2010), this simulator is implemented for a close and detailed study of gas-well pressure-drawdown (DD) and -buildup (BU) tests. Overall, the developed simulator is an accurate and strong tool for design and analysis of transient gas-well testing, particularly for high- pressure/high-temperature (HP/HT) gas reservoirs. Several numerical results will be presented. This includes demonstration of the behaviour of the wellbore-fluid pressure, temperature, density, and velocity and an overall heat-transfer coefficient during DD or shut-in tests for nonisothermal reservoirs and conceptual comparisons with the isothermal counterparts. Thermal effects on the behaviour of derivative plots and the sandface-flow rate of deep nonisothermal gas reservoirs will be studied. A significant effect of neglecting the heat capacity of tubular and cement materials on the wellhead-temperature simulation, and thus transient well tests, will be demonstrated. A sample case to show that neglecting the thermal effects in the gas-well tests of composite reservoirs leads to unreliable results in well-testing analysis will be presented. Several other numerical experiments, including the presence of a variable wellbore-storage coefficient, gas backflow from the wellbore to the reservoir, and other thermal effects during the gas-well tests, are also presented. Hundreds of millions of dollars are spent every year on well testing around the world (Hawkes et al. 2001). A proper design and truthful interpretation of these tests can be achieved by a reliable coupled wellbore/reservoir simulator, which in turn can save a large portion of the required costs.
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 | 0.002 | 0.001 |
| 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".