Novel Deliverability Test Method for HPHT Sour Gas Wells
Bibliographic record
Abstract
Abstract Sour gas reservoirs with H2S and/or CO2are widely spread in the whole world, especially in France, Canada, America and China. Most of sour gas reservoirs in China are HPHT tight gas reservoirs which require long testing duration to reach steady state when conducting deliverability test while long testing time leads to huge operation risk because testing tools are used close to critical temperature and pressure condition. In order to resolve the contradiction among testing duration, operation risk and interpretation results' accuracy, a new deliverability test method is proposed by using pressure buildup transient data. The deliverability calculation model is built by integrating wellbore fluid flow and formation seepage considering fluid flow from formation to wellbore after well shut-in. The formation parameters are interpreted by one pressure buildup transient data, based on which, four daily production rates are designed to simulate the wellbore flow pressure until the well reaches steady state flow, and then the deliverability equation can be derived from the pressure & daily rate points under steady state flow. Filed case comparison is made between interpretation of actual test data and data from the new method. Results show that the new deliverability test method can get more accurate deliverability equation and AOF by only 1 time pressure buildup and greatly reduces the duration for testing, so it is very practicable because it can greatly decrease the operation risk and increase interpretation accuracy for HPHT sour gas reserviors. This new deliverability test method has wide application prospect in HPHT sour gas reservoirs and can be extended to other oil/gas reservoirs' application for quick AOF determination, rate allocation, and economic evaluation.
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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 | 0.001 |
| 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".