Updated Screening Criteria for Steam Flooding Based on Oil Field Projects Data
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Enhanced oil recovery (EOR) screening is considered the first step in evaluating potential EOR techniques for candidate reservoirs. Therefore, as new technologies are developed, it is important to update the screening criteria. Many of the screening criteria for steam flooding that have been described in the literature were based on data collected from EOR surveys biennially published in the Oil & Gas Journal. However, these datasets contain some problems, including outliers, missing data, inconsistent data and duplicate data, that could affect the accuracy of the results. Despite the importance of ensuring the quality of a dataset before running analyses, data quality has not been addressed in previous research related to EOR screening criteria. The objective of this current work was to update the screening criteria for steam flooding by using a database that had been cleaned. The original dataset included 1, 785 steam flooding field projects from around the world (Brazil, Canada, China, Colombia, Congo, France, Germany, Indonesia, Trinidad, U.S. and Venezuela). These projects had been reported in the Oil and Gas Journal from 1980 to 2012. After detecting and deleting the duplicate projects, only 626 field projects remained. To analyze and describe the results of the dataset, both graphical and statistical methods were used. A box plot and cross plots were used to detect and identify data problems, allowing for the removal of outliers and inconsistent data. Histogram distributions and box plots were used to show the distribution of each parameter and present the range of the dataset. New screening criteria were developed based on these statistics and the defined data parameters. The developed criteria were com-pared with previously published criteria, and their differences are explained in this paper.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it