Bibliographic record
Abstract
To select an optimal and environmentally friendly technology for oil and gas development, it is necessary to estimate in advance the likely disfigurement processes of the surface terrain. To this end, it is recommended to develop predictive geodynamic models prior to start of field development, taking into consideration the geological characteristics and tectonic activity of the area under investigation, as well as the specific features of the reservoir. Research methods. In this paper, two models of subsidence of the ground surface in a hydrocarbon field are considered: a parametric spatial model developed at Delft University of Technology and a model based on the Knoté influence function developed at the Canadian Center for Geodetic Engineering. The first method is more suitable for describing a smooth and gradual subsidence in deep gas reservoirs and allows you to assess the spatial-temporal pattern of movement of the ground surface. In the second method, geodynamic processes are modeled based on the functional relationship between reservoir compaction and subsidence of the day surface, taking into account the location of the oil reservoir, physical and mechanical properties of rocks, changes in reservoir pressure and the results of surface disfigurement monitoring and is recommended for oil fields. Research results. A comparative analysis of these methods is carried out on the example of the Tengiz oil and gas field in Western Kazakhstan. An evaluation of the developed model accuracy is carried out by comparing the calculated values of soil subsidence with the data of radar interferometry, and estimates obtained by other researchers. Recommendations are given on the application of the considered methods in the generation of predictive models of oil and gas fields, the necessity of calculating the transfer coefficient of the reservoir compaction to the position of the day surface, taking into account the depth of the reservoir and the physical and mechanical properties of the rock massif, is indicated.
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.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".