Probing Methane Foam Transport in Heterogeneous Porous Media: An Experimental and Numerical Case Study of Permeability-Dependent Rheology and Fluid Diversion at Field Scale
Bibliographic record
Abstract
Summary This paper advances the understanding of foam transport in heterogeneous porous media for enhanced oil recovery (EOR). Specifically, we investigate the dependence of methane foam rheology on the rock permeability at the laboratory scale and then extend the observations to the field scale with foam modeling techniques and reservoir simulation tools. The oil recovery efficiency of conventional gasflooding, waterflooding, and water-alternating-gas (WAG) processes can be limited by constraints such as bypassing effects (including both viscous fingering and channeling mechanisms) and gravity override. The problem can be more severe if the reservoir is highly fractured or heterogeneously layered in the direction of flow. Foam offers the promise to address the three issues simultaneously by better controlling the mobility of injected fluids. However, limited literature data of foam-flooding experiments were reported using actual reservoir cores at harsh conditions. In this paper, a series of methane (CH4) foam-flooding experiments were conducted in three different actual cores from a proprietary reservoir at an elevated temperature. It is found that foam rheology is significantly correlated with the rock permeability. To quantify the mobility control offered by foam, we calculated the apparent viscosity on the basis of the measured pressure drop at steady state. Interestingly, the apparent viscosity was found to be selectively higher in the high-permeability cores compared with that in the low-permeability zones. We parameterized our system using a texture-implicit-local-equilibrium model (STARS™ simulator, Computer Modelling Group, Calgary, Alberta, Canada) to illustrate the dependence of foam parameters on rock permeability. In addition, we created a two-layered model reservoir using an in-house simulator called modular reservoir simulator (MoReS; Shell Research, Rijswijk, The Netherlands) to elucidate the role of different driving forces for fluid diversion at the field level. We took into consideration the combined effect of gravitational, viscous force, and capillary forces in our simulation. We show that the gravitational forces prevent the gas from sweeping the lower part of the reservoir. However, the poor sweep can be ameliorated by intermittent surfactant injection to generate foam. In addition, the capillary force which hinders the gas (nonwetting phase) from entering the low-permeability region can be effectively leveraged to redistribute the fluids in the porous media, resulting in better sweep efficiency. We conclude that foam if properly designed can effectively improve the conformance of the WAG EOR in the presence of reservoir heterogeneity.
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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".