Study on the Interpretation Method of Layered Flow Imaging Logging for Oil–water, Two-Phase Flow in Horizontal Wells
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
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.
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 | 0.002 | 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 it