Simulation of Enhanced Recovery using CO2 in a Liquid-Rich Western Canadian Unconventional Reservoir: Accounting for Reservoir Fluid Adsorption and Compositional Heterogeneity
Bibliographic record
Abstract
Abstract Liquid-rich unconventional reservoirs are currently popular targets for development by the industry. However, hydrocarbon liquid recovery in unconventional reservoirs can be very low, primarily due to low permeability, but also partly due to adsorption of heavier hydrocarbon components. Previous studies have demonstrated that the heaviest components (butane+) are the most strongly adsorbed while being the most valuable commodity. Therefore, the development of methods to enhance recovery of these strongly-adsorbed components is very appealing to operators. The purpose of this study is therefore to investigate the possibility of incremental recovery of oil in a low-permeability reservoir by injecting a non-hydrocarbon gas (CO2) into the reservoir using a huff-n-puff procedure. A feasibility study of CO2-enhanced production in a liquid-rich (volatile oil) low-permeability (tight) reservoir in Western Canada is conducted using rigorous compositional simulation combined with multi-component adsorption modelling. The simulation model used for a sensitivity analysis was previously calibrated using flowback data obtained from a multi-fractured horizontal well (Clarkson et al., 2016a). A unique aspect of that study was that multi-layer PVT and fluid properties in the reservoir were estimated using a novel procedure; however, adsorption of the reservoir fluids was ignored. In the current study, an innovative approach developed by Clarkson and Haghshenas (2016) was applied for estimating high pressure/temperature (in-situ) adsorption of reservoir fluid components and CO2 using a combination of low pressure adsorption data and the simplified local density model. This approach was required because, typically, the only reservoir samples available along horizontal wells are cuttings, which are not available in sufficient quantities for direct high pressure adsorption measurements. A general equation was also developed for defining the diffusivity coefficient in nanopores which can be directly applied in a commercial numerical simulator. Sensitivity studies were then performed for different huff-n-puff operating conditions, and for the range in different reservoir fluids obtained by Clarkson et al. (2016a). The huff-n-puff sensitivity study demonstrates that, for the operating conditions applied, results of CO2 injection are positive (incremental recovery over primary production) only when adsorption/diffusion effects are included in the model. Further, for the 1000 day evaluation period, the combination of shorter injection times (40 days) and longer soak periods (60 days) are required to yield incremental recovery. When uniform in-situ fluid compositions are assumed, lower saturation pressure fluids are more amenable to the CO2 huff-n-puff procedure than higher bubble point fluids. However, when fluid compositions vary by geologic horizon, as they do in this study, this heterogeneity must be considered in the analysis for an accurate assessment of CO2 EOR. To our knowledge, this is the first time that reservoir fluid component adsorption and reservoir fluid property variability by layer in an unconventional reservoir has been considered while planning for CO2-enhanced liquid recovery. This study provides some insight into the selection of optimal well operating conditions for CO2 injection while considering the effects of adsorption selectivity, pore wall-fluid molecular interaction, and thermodynamic behavior of the fluid.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".