Fluid-Level Monitoring Using a Distributed Temperature Sensing System During a Methane Hydrate Production Test
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Temperature profiles and their transient behaviors obtained using a distributed temperature sensing (DTS) system were measured while monitoring wellbore fluid levels during a methane hydrate production test. The temperature data were obtained in 2008 as part of the JOGMEC/NRCan/Aurora Mallik 2007–2008 Gas Hydrate Production Research Well Program. Methane hydrate is known to be stabilized under low-temperature and high-pressure conditions. In the Gas Hydrate Production Research Well Program, a depressurization method using an electric submergible pump (ESP) was employed to dissociate the hydrate in the reservoir. For recovering the produced gas and fluid without resynthesizing the hydrate after the dissociation, it was essential that the temperature of the produced fluids flowing up the tubing be controlled. According to our numerical temperature simulation, the annulus fluid level around the tubing is one of important factors that govern the tubing fluid temperature during the methane hydrate production. If the annulus fluid level is high, the tubing fluid temperature becomes so low that methane hydrate can potentially be formed inside the tubing; thus, understanding fluid levels during methane hydrate production is important for flow assurance as well as bottomhole pressure control. The conventional method for estimating fluid levels in a wellbore employs an acoustic wave reflection technique; however, the accuracy of the survey is subjected to assumption of an acoustic velocity which depends on pressure, temperature, and gas types. On the other hand, estimating fluid levels with a DTS temperature profile is thought to be a more direct method. Although it is not widely known, several papers indicate that the feasibility of estimating fluid levels with the DTS system when the fluid level is static. In this paper, we demonstrate the feasibility of estimating dynamic fluid levels using a DTS system. The dynamic trend in estimated fluid levels with the DTS system shows qualitatively good agreement to that estimated with the pressure on the completion assembly and the pressure at the casinghead. The difference in fluid levels between the DTS temperature-based and the pressure-based methods quantitatively explains a void fraction in the two-phase flow of the fluid and the gas. The analysis presented in this paper is based on field data collected during methane hydrate production, but it is potentially applicable to any conventional production scheme that employs artificial lift.
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.
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 | 0.001 |
| 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 it