Bibliographic record
Abstract
Abstract Attention in CO2 flooding for incremental oil recovery and greenhouse gas (GHG) sequestration has prompted the need for screening and ranking Alberta oil pools for this EOR process. In a previous paper by the same authors, over eight thousands of Alberta oil pools were ranked for CO2 EOR suitability using a new parametricranking software which utilized six essential reservoir properties: oil density, residual oil saturation, minimum miscibility pressure (MMP), reservoir temperature, net pay thickness, and porosity. This continuation paper describes the results of using an advanced method to estimate production forecasts for numerous candidate pools in Alberta. A Microsoft Excel program with VBA based on the modified Koval method (1963) by Claridge (1972) has been developed to predict CO2 flooding performance using the Alberta reserves database. The program estimates live oil and CO2 viscosities at reservoir conditions, oil MMP and reservoir heterogeneity based on the rock type, to predict oil recovery at any specific pore volume of CO2 throughput. Over 8,000 Alberta pools were first screened for CO2- flood suitability, and pertinent reservoir properties were used for the remaining 4,729 pools to calculate oil recovery. The predicted recoveries for all pools ranged from 1.2-13.9%, 6.3-18.7% and 11.8-27.1% at breakthrough and 0.25 and 0.5 hydrocarbon pore volume (HCPV) injection respectively. These values compared well to an average of 13% incremental oil recovery from the field experience of CO2 floods. More importantly, the results clearly identify the most suitable Alberta pools for CO2 flooding. Introduction The recent high oil price and interest in reducing GHG (Greenhouse Gas) emissions in response to global warming may have created new business opportunities to realize incremental value from depleted oil pools through CO2 flooding. Not all oil pools in Alberta are suitable for CO2 flooding. Thus, in a previous paper by the same Authors1, about 8,800 Alberta oil pools were ranked by using a newly-developed VBA program capable of retrieving pertinent information from the digitized Alberta reserve database and perform parametric technical rankings. Six parameters with different assigned weightings were used in the technical ranking. These include API gravity of oil, residual oil saturation, ratio between reservoir pressure and predicted minimum miscibility pressure (P/MMP), reservoir temperature, net pay thickness, and porosity. However, the screening software is not capable of providing production forecasts of CO2 flooding, which is the motivation of this study. Numerous active CO2 flooding projects in the United States and Canada have provided valuable theoretical and practical information on the technology. Desktop engineering prediction tools such as US DOE "CO2 Prophet" 2 have been developed for quick technical and economic assessment. These tools are based on sophisticated analytical equations derived from theoretical calculations, numerical simulation and field experience. However, we are not aware of any tools that are capable of evaluating the performance of large numbers of oil pools as reported in this paper. CO2 FLOODING PREDICTIVE MODELS The recovery efficiency prediction of CO2 flooding can be used to provide useful estimates of financial viability of the project.
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 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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 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 teacher head, 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".