A Comprehensive Mathematical Model for Estimating Thermal Efficiency of Steam Injection Wells Considering Phase Change
Bibliographic record
Abstract
Abstract Improving thermal efficiency of steam injection wells is an important goal in the process of steam injection for heavy oil recovery. The main objectives of this paper are to establish a comprehensive mathematical model for estimating the thermal efficiency of steam injection wells and to make some suggestions on how to achieve the above goal. The mathematical model is composed of four sections. The first one is about prediction of thermophysical properties of injected steam considering phase change. In this section, if wet steam is not cooled to liquid water, slippage between gas and liquid phases is taken into account in calculating pressure drop based on momentum balance principle. In addition, a complete expression for steam quality distribution in wellbores is derived in detail. However, if phase change occurs, we can obtain an implicit equation for fluid temperature by combining energy balance and Coulter-Bardon equations. In the second section, steady-state heat transfer inside the wellbore and transient radial conduction in the formation are coupled at the cement/formation interface, based on which the wellbore heat loss rate is determined. Next, the thermal efficiency is estimated by using both direct and indirect methods. Finally, the mathematical model is solved iteratively for each segment and a detailed calculation flowchart is also provided. The proposed model is validated by comparing simulated steam pressure, temperature and quality with measured field data from Liaohe Oilfield, and the direct and indirect methods of estimating the thermal efficiency prove to be reliable. Then, using the validated model, we analyze the effects of wellhead steam quality, injection rate and thermal conductivities of insulation materials on thermal efficiency of steam injection wells. The results indicate that enhancing the wellhead injection rate and using low thermal conductivities of insulation materials can greatly improve the thermal efficiency. But it is not a good choice to achieve this goal by improving the wellhead steam quality. Moreover, the paper shows that our methods for estimating the thermal efficiency of steam injection wells can also be applied to concentric-dual tubing steam injection wells. In this paper, the comprehensive mathematical model for estimating the thermal efficiency of steam injection wells may be worthy of more attention, because it has not been widely reported in the literature. More important, phase change from steam/water two-phase flow to liquid water single-phase flow in deep wells is also considered in our study.
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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 | 0.000 | 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".