Adaptive Impact Factor Research Concerning Effectiveness of the Introduction and use of Digital Twins for Oil and Gas Deposits
Bibliographic record
Abstract
In recent years, there have been significant changes in the conditions of oil production leading to an increase in its cost and wasteful use of resources. This necessitates the search for a system of adaptive factors that can adapt to environmental changes, affect the cost reduction and increase in the efficiency of oil and gas fields. Studies show that this problem needs to be solved on the basis of the creation of digital oil or gas fields being digital counterparts of existing enterprises, which allow preserving nature and use resources economically due to the existing field development at a new qualitative level. However, the transformation of existing fields through their transformation into digital oil or gas fields requires serious justification, and above all, from a financial and economic points of view. At the same time, one should in no case ignore the natural factor contributing to saving, restoring the used oil and gas resources and preserving the external space being the human environment. The purpose of this study is to develop recommendations for assessing the economic efficiency of the implementation of the project concerning a digital oil or gas field being a digital twin of oil or gas enterprise, and their use in practice. Such an assessment will be carried out based on the analysis of the ratio between capital investments and operating costs necessary to create a digital oil or gas field, as well as by comparing the expected costs and benefits derived from its use.
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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 | 0.012 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.001 |
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".