Study on the Interpretation Method of Layered Flow Imaging Logging for Oil–water, Two-Phase Flow in Horizontal Wells
Bibliographic record
Abstract
In the production of oil and gas fields, horizontal wells can obtain larger reservoir drainage area. Single well production is large and the production cycle is long. Especially for the development of reservoirs with thin production layer, small porosity, and low permeability, it shows the incomparable effect of vertical wells. Due to gravity separation in horizontal wells, the distribution of fluids in horizontal cross sections is more complicated. There are many influencing factors, such as gas lock, water lock, and flow instability. In horizontal well oil–water two-phase flow, the flow separation in different flow zones complicates the reading of the turbine flowmeter due to the change the cross-sectional area of the fluid. A small deviation of the horizontal well inclination causes significant changes in holdup and flow velocity. Well deviation causes backflow and circulation. In this paper, the capacitance water holdup and turbine flowmeter data processing method of FILT in oil–water two-phase flow are studied by the oil–water, two-phase flow experiment. The stratified flow interpretation model of oil–water two-phase flow and the chart fitting calculation method are proposed and realized. This is the innovation of this paper. Through the verification of experimental data, the relative errors under various conditions are less than 10%. Only when the moisture content is 20%, the error is greater than 10%. The interpretation accuracy of oil–water two-phase flow can fully meet the actual needs of production. It provides a strong basis for accurately finding the producing water point and scientifically plugging water in horizontal wells. The findings of this study can help the better understanding of the oil–water two-phase flow stratification flow interpretation model and the chart fitting calculation method.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".