GAMMA AND ELECTRO-LOGGING DURING WELL WORKOVER USING THE TECHNOLOGY OF RADIAL BRANCHING OF FORMATION
Bibliographic record
Abstract
The efficiency of oil recovery from oil-bearing reservoirs by modern, Abstract. industrially developed development methods in most oil-producing countries today is considered unsatisfactory, and the final oil recovery of reservoirs in various countries and regions averages from 25 to 40 %. For example, in Latin America and Southeast Asia, average oil recovery is 24–27 %, in Iran — 16–17 %, in the USA, Canada and Saudi Arabia — 33–37 %, in the CIS countries and Russia — up to 40 %, depending on the structure of oil reserves and the development methods used [1].Therefore, the task of managing the productivity of oil and gas reservoirs remains relevant at present.The article discusses a special technology for the use of gamma and electro-logging as part of the Perfobur technical system (hereinafter referred to as pyrolysis oil). These geophysical methods of investigation, at the end of drilling radial channels over a small diameter and radius of curvature, performed with trajectory control, make it possible to prove that drilling was carried out in a given reservoir, and provide more detailed information about its productivity.One of the areas of use of pyrolysis oil is enhanced oil recovery, intensification of inflow, restoration of normal operation of wells stopped or hindered due to falling at the bottom of oil field equipment, blocking or contamination of the perforation zone, including during initial penetration [2–5].Complex geological conditions are noted, such as high reservoir fragmentation, heterogeneity of the carbonate reservoir, fracturing, large lateral permeability variability, high oil viscosity and presence of washed zones, caverns after salt-acid treatment (SW) with subsequent steam injection. Under such conditions, it is difficult to develop a field, new well logging methods are required, as well as high-quality logging to determine saturated interbeds. Effective development of the field requires the determination of the current reservoir saturation with higher accuracy, which will help the subsoil user to better develop the field, eliminate stimulation of watered beds, and carry out better and more selective intensification of interbeds. It should be noted that the presence of a steel column practically eliminates the possibility of using electrical logging methods. Therefore, it is recommended to use such methods in an open hole. Drilling of radial channels using the Perfobur technology allows electro-logging with a special tool due to the possibility of repeated entries into the drilled channels [2–5].
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".