DST Design for Deepwater Wells with Potential Gas Hydrate Problems
Bibliographic record
Abstract
Abstract The main objective of this paper is to present a proper DST design for deepwater gas wells with potential gas hydrate problems because of low seabed temperature. Prior to discussing the DST procedures, the importance of selecting a proper mud for drilling and interval for testing are explained. Factors affecting gas hydrate formation are discussed. Then, requirements for gas hydrate prevention during DST are described. Actions required to prevent gas hydrate formation during DST startup, fluid sampling, well shut-in and restart are addressed. Finally, procedures for incorporating gas hydrate prevention in DST are outlined. At the end of the paper, a gas well with hypothetical data is used to assist in illustrating the DST procedures. Introduction Hydrates are physical combinations of water and natural gas formed at pressures and temperatures considerably above the freezing point of water[1-5]. For deepwater gas wells, the risk for gas hydrate formation always exists due to the low seabed temperature and the coexistence of gas and water inside the wellbore. Figure 1 shows one of the popularly used pressure-temperature-gas density correlations for gas hydrate predictions[2,6]. As it can be seen from this figure, the generally low temperature of less than 50 0F in the deepwater seabed, together with the normal gas well operating pressure of several hundreds to thousands of psi, will result in wellbore temperatures below the gas hydrate temperature curve. DST and Gas Hydrate Although a deepwater gas well may be operated at certain flow conditions to take advantage of the warm fluid coming out from the reservoir, the need for DST to shut in the well for pressure buildup tests or produce the well at low rates for fluid sampling often cool the fluid inside the wellbore to the extent that gas hydrate will form. For the deepwater DST, it has been well recognized that hydrate prevention is needed for flow assurance in order to complete the test[7-11].
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".