Research and Development of an Empirical Method for Predicting Oil Recovery During EOR Studies
Bibliographic record
Abstract
The coefficient of oil recovery (RE) depends on both connate water saturation as well as the residue oil after water, gas, or chemical flooding. In turn, the respective initial and residual fluids saturation depends on petrophysical and reservoir conditions like temperature, reservoir pressure, and overburden pressure. The current study is aimed at developing an empirical model for predicting the coefficient of oil recovery based on the above mentioned parameters, as measured in commercial laboratories during routine and special core analysis studies. The preliminary studies, based on a 100 sandstone- and carbonates-samples database, yielded an excellent diagnostic correlation with R2 > 0.95. The correlation is characteristic for each given reservoir, and hence the ultimate goal of this study is to develop oil recovery coefficient signature charts for each reservoir. The signature charts will be useful in the design of water, gas, and chemical flooding in improved oil recovery projects. Klinkenburg and Forchheimer corrections have been applied, in order to extend the correlation application to different reservoir fluids and formations. The correlation provides a tool for determining a reservoir's RE, where data is scanty.Literature Review. The knowledge of absolute and effective permeability is very useful in drilling and completion design, as well as in designing the stimulation and production method. During the completion, the only data available is mainly routine core petrophysical data. There is no reliable empirical correlation for predicting the core's permeability, initial water saturation, and porosity at the net overburden pressure(1 –3). The existing correlations do not show any generalized results which could be applied in reservoirs in a regional scale. This research is based on core data from different oil and gas pools in Alberta, Canada. The formation types and the number of samples pertinent to each formation are shown in Table 1.Mathematical DevelopmentThe Concept of Storetransmission Coefficient. Consider a unit volume of a fractured core as shown in Figure 1. The fluids in this fractured core are stored and transmitted through the pores and fractures, respectively. Therefore the sumof matrix (porosity x permeability) plus fracture (porosity x permeability) is equal to the total (porosity x permeability) storage and transmission capacity: i.e., storetransimission capacity. Mathematically this relationship can be expressed as per Equation (1).Equation (Available In Full Paper)Equation (5) is used to develop empirical correlations for describing the relationships of permeability, porosity, reservoir net overburden pressure, and connate water saturation in both fractured and nonfractured reservoirs.From laboratory measurements, it is evident that absolute gas permeability at net overburden pressure (KN) is directly proportional to both routine gas permeability (Kgr) and porosity (φr). However it is inversely proportional to reservoir net overburden pressure (PN) In mathematical notation, this can be expressed as follows:Equation (Available In Full Paper)Equation (4) can be changed into an equation by multiplying its right-hand side by a coefficient of propotionality C1. Equation Available In Full PaperEquation (10) can be modified to include connate water saturation.
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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".