Reservoir Characterization and Simulations Studies in a Heterogeneous Pinnacle Reef for CO2 Flooding Purposes: A Case Study
Bibliographic record
Abstract
Abstract Carbon dioxide injection is an effective method for enhanced oil recovery. In this process, CO2 develops miscibility with the oil under reservoir conditions, and leads to additional oil recovery. Proper reservoir characterization has a significant influence on designing and implementing a successful CO2 flood in a reservoir. This paper presents the results of a reservoir characterization analysis and simulation in a relatively small reef located in Northern Alberta, Canada, which was selected as a candidate for a CO2 injection project. This reef has a thick oil column spanning a small area with steeply sloping sides, and two wells drilled on the same side of the reef. Open-hole logs and production history data were available for only one of the two wells. Data analyses disclosed a number of challenges that could affect the results of any simulation for predicting the performance of CO2 displacement in this field. These challenges included, but were not limited to, the existence of two no-flow barriers with unknown extensions, lack of other data such as relative permeability, and lack of information on lateral distribution of the reservoir properties. Material balance analysis indicated the maximum oil in place was 4.7 MMSTB with a weak water support. A fully compositional reservoir simulator (CMG-GEM™) was used with the aim of improving the understanding of the reservoir characteristics, and investigating the suitability of CO2 injection. An 8-component Peng-Robinson EOS was utilized to describe the phase behaviour of the reservoir fluids and injected CO2. History matching was done and the current reservoir pressure was found to be about 1000 psi below the minimum miscibility pressure. A simulation study was conducted and the model predicted an incremental oil recovery of around 1.04 MMSTB and about 0.424 Mt of injected gas stored in the reservoir.
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) | 0.000 | 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 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".