Practices of Miscible Displacement of Oil by Gas on the Achim Deposit of Yamburg Project
Bibliographic record
Abstract
Abstract The era of production of readily available oil, which requires a relatively low level of capital costs, engineering solutions and the lack of use of new technological solutions, is over. At the moment, in the world and, specifically, in the perimeters of Gazpromneft, the bulk of the assets of hydrocarbon fields are complex in type and structure deposits with sufficiently high oil reserves, requiring both high capital costs and the adaptation of new technologies for effective development. Achimov formations Ach18-1 of the Yamburg deposit are bright representatives of the reservoir with a complex structure and very low, even for Achim formations, permeability. Average permeability of formation Ah 18-1 0.15 mD. At the moment, the only technology that allows achieving the profitability of the project is the use of wells with multi-stage hydraulic fracturing. However, a high share of hydrocarbon raw materials remains in the formation and for development, new technologies are being sought that will increase the level of net discounted income, as well as increase the oil recovery ratio. For consideration, the technology of mixing oil displacement with gas was chosen as one of the most promising technologies for the conditions of the Yamburg oil field. Gas injection technology was tested in a number of countries: the USA, Canada, Brazil, Venezuela and Norway, including was considered on some projects of Russia (USSR) (Figure 1). There are no direct analogues for Yamburg conditions, however, this technology allows us to obtain a REF increase of 1.5 to 2 times among the fields considered, on average, REF with the use of mixing displacement is about 46%, the increase relative to the standard Flooding is 7-12%. The promise of this method is that it can be used in deep-lying formations with low filtration-capacitive properties, high thermobaric conditions, the method allows not only to maintain formation pressure, but also to increase the oil recovery coefficient. Increase of oil recovery coefficient is achieved due to achievement of mixing pressure during gas injection, which leads to decrease of residual oil saturation and increase of phase permeability in gas-oil system. It is worth noting that the characteristics of the reservoir oil of the object under consideration (high gas content, a significant proportion of light fractions in the reservoir fluid composition) favor the use of gas methods to increase oil recovery, since the values of minimum mixing pressures for various types of injected gases (including pure methane and nitrogen) are lower than the initial reservoir pressure.Figure 1REF when applying mixing displacement technology. To assess the efficiency of mixing displacement at the Yamburg field, analytical calculations were carried out to determine the minimum mixing pressure with the formation oil of the Ach18-1 formation (the main development object). Based on the results of the calculations, the following conclusions were made:The mixing pressure for the formation oil of the Yamburg field varies from 417-500 depending on the composition of the injection gas.Mixing pressure practically does not depend on the degree of depressurization of reservoir oil (possibility of development in dissolved gas mode with switching to gas injection at any moment)To clarify the MMP values, it is necessary to conduct slim tube for different injection gases and on a larger number of oil samples.
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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 | 0.001 | 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 | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".