Evaluation of Continuous Fluid Injection and WAG Techniques for Enhanced Oil Recovery in a Tight Oil Reservoir in Alberta, Canada
Bibliographic record
Abstract
Abstract Multi-fractured horizontal wells (MFHWs) completed in low-permeability (‘tight’) oil reservoirs recover only a small fraction of the oil in place using the primary recovery scheme. The main objective of this study is to investigate various continuous injection techniques for enhanced oil recovery (EOR), including water and CO2 flooding schemes, as well as water-alternating-gas (WAG) injection, to increase production from a MFHW completed in a tight oil reservoir in Alberta, Canada. This study employs numerical compositional simulation using two model types: a single porosity (SP) model with an enhanced fracture region (EFR), and a local dual-permeability, dual-porosity (L-DP-DK) model with a limited enhanced fracture region (LEFR). The SP and L-DP-DK models incorporate laboratory-derived rock and fluid data and are utilized to history-match production data from a MFHW operated under primary depletion. Multiple history-matched models are obtained to account for variability in the core- measured matrix permeability (ranging from 30 to 300 μd). The calibrated models are then utilized to predict incremental oil recovery using continuous injection and WAG EOR schemes over a 30-year period. Finally, a sensitivity analysis is conducted to study the impact of matrix permeability, fracture half-length, and fracture geometry (i.e., tip-to-tip and parallel overlapping fractures) on the simulated incremental oil recovery. The results demonstrate that the L-DP-DK model predicts a greater oil recovery (79% higher on average) than the SP model. This increase is attributed to an improved mixing and extraction process predicted by the L-DP-DK model due to more effective communication between the fracture network and the matrix. Simulation results using the L-DP-DK reveal that the CO2-gas flooding scheme has a higher recovery than WAG and water flooding techniques. CO2-gas flooding provides the greatest incremental recovery factor (i.e., 53%) when the history-matched model includes the largest permeability (300 μd) and the smallest fracture half-length (150 ft). In addition, the L-DP-DK model predicts 24% incremental recovery using the matched model with the smallest matrix permeability (30 μd) and the largest fracture half-length (500 ft). Gas flooding provides a better recovery because the oil viscosity and surface tension are two and ten times lower, respectively, than those calculated from the WAG. Moreover, the water cycle of the WAG scheme incurs blockage, leading to a quicker gas breakthrough for up to four months, and lower sweep efficiency. Finally, the sensitivity analysis reveals that the selected completion method and assumed fracture pattern significantly influence the recovery factor, with the staggered zipper completion (parallel, overlapping fractures) providing 140% greater oil recovery compared to a tip-to-tip geometry. This study provides practical insights into the impact of model uncertainty and completion methods on the design and performance of various continuous injection and WAG EOR schemes in tight oil reservoirs. Wherever possible, customized laboratory data, such as relative permeability collected for low-permeability rock samples, have been utilized.
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 | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".