A surfactant alternating gas (SAG) process is often the injection method for foam, on the basis of its improved injectivity over direct foam injection. In a previous study, we reported coreflood experiments on liquid injectivity after foam flooding and liquid injectivity after injection of a gas slug following steady-state foam. Results showed that a period of gas injection is important for the subsequent liquid injectivity. However, the effects of multiple gas and liquid slugs were not explored. In this paper, we present a coreflood study of injectivities of multiple gas and liquid slugs in an SAG process in a field core. Nitrogen and surfactant solution are either coinjected or injected separately into the sandstone core sample. The experiments are conducted at an elevated temperature of 90℃ with a backpressure of 40 bar. Differential pressures are measured to quantify gas and liquid injectivities. Computed tomography (CT) scanning is applied to relate water saturation to mobility. During the injection of a large gas slug following foam, a bank in which foam completely collapses or greatly weakens forms near the inlet and propagates slowly downstream. During the subsequent period of liquid injection, liquid flows through the collapsed-foam bank much more easily than further downstream. Beyond the collapsed-foam region, liquid first imbibes into the whole cross section. In this region, liquid flows mainly through a finger of high liquid saturation. Our CT results suggest a revision of our earlier interpretation; the process of gas dissolution does not merely follow fingering but is evidently directly involved in the fingering process. Our results suggest that, in radial flow, the small region of foam collapse very near the well greatly improves injectivity. The subsequent gas and liquid slugs behave near the wellbore, affecting injectivity, in a way similar to the first slugs. Thus, the behavior and modeling of the first gas slug and first subsequent liquid slug is representative of near-well behavior in an SAG process. The trends observed in our previous work are reproduced in a low-permeability field core.@en

Surfactant-alternating-gas (SAG) is a favored method of foam injection, in part because of excellent gas injectivity. However, liquid injectivity is usually very poor in SAG. We report a core-flood study of liquid injectivity under conditions like those near an injection well in SAG application in the field, i.e., after a prolonged period of gas injection following foam. We inject foam [gas (nitrogen) and surfactant solution] into a 17-cm-long Berea core at temperature of 90 °C with 40 bar back pressure. Pressure differences are measured and supplemented with CT scans to relate water saturation to mobilities. Liquid injectivity directly following foam is very poor. During prolonged gas injection following foam, a collapsed-foam region forms near the inlet and slowly propagates downstream, in which water saturation is reduced. This decline in liquid saturation reflects in part liquid evaporation, also pressure-driven flow and capillary effects on the core scale. In the collapsed-foam region, liquid mobility during subsequent liquid injection is much greater than downstream, and liquid sweeps the entire core cross section rather than a single finger. Mobility in the region of liquid fingering is insensitive to the quality of foam injected before gas and the duration of the period of gas injection. This implies that at the start of liquid injection in a SAG process in the field, there is a small region very near the well, crucial to injectivity, substantially different from that further out, and not described by current foam models. The results can guide the development of a model for liquid injectivity based on radial propagation of the various banks seen in the experiments.@en

Surfactant-alternating-gas (SAG) is often the injection method for foam enhanced oil recovery (EOR) in order to improve injectivity. However, liquid injectivity can be very poor once foam is created in the near-wellbore region. In a previous study, we reported core-flood experiments on liquid injectivity after foam flooding and liquid injectivity after a period of gas injection following foam. Results showed the importance of the gas slug to subsequent liquid injectivity. However, the effects of multiple gas and liquid slugs were not explored. In this paper, we present a coreflood study of injectivities of multiple gas and liquid slugs in a SAG process. We inject nitrogen foam, gas and surfactant solution into a sandstone core sample. The experiments are conducted at a temperature of 90°C with 40-bar back pressure. Pressure differences are measured to quantify the injectivity and supplemented with CT scans to relate water saturation to mobility. We find that during prolonged gas injection in the first gas slug following foam, a collapsed-foam region forms near the inlet due to the interplay of evaporation, capillary pressure and pressure-driven flow. This region slowly propagates downstream. During subsequent liquid injection, liquid mobility is much greater in the collapsed-foam region than downstream, and liquid sweeps the entire core cross section there rather than a single finger. In the region beyond the collapsed-foam region, liquid fingers through foam. Liquid flow converges from the entire cross section to the finger through the region of trapped gas. During injection of the second gas slug, the liquid finger disappears quickly as gas flows in, and strong foam forms from the very beginning. A collapsed-foam region then forms near the inlet and slowly propagates downstream with a propagation velocity and mobility similar to that in the first gas slug. Behavior of the second liquid slug is likewise similar to that of the first liquid slug. Our results suggest that, in radial flow, the small region of foam collapse very near the well is crucial to injectivity because of its high mobility. The subsequent gas and liquid slugs behave like the first slugs. The behavior of the first gas slug and subsequent liquid slug is thus representative of near-well behavior in a SAG process.@en

In this study, we present a history matching framework for oil production forecast based on synthetic and real production data developed using the stochastic Discrete Well Affinity (DiWA) model. With the increase in the complexity of the geological model and the uncertainty in the geological data, it becomes more difficult (sometimes infeasible) to conduct model inversion and production based on conventional technique. To address this problem, we proposed a stochastic DiWA model with unstructured low-resolution mesh to represent the location of wells and reservoir fluid dynamics. With this method, we can efficiently train the forward model based on production data and a stochastic ensemble of property realization. The performance of forward evaluation benefits from the Operator-Based Linearization (OBL) technique and the adjoint method for gradient calculation. Before the model training, a large ensemble size of stochastic DiWA models is generated based on the permeability statistics of the real reservoir, and those models are then filtered using the misfit between the true production data and the DiWA model response. The filtered models have the best fit with the production history of the real reservoir, while they also contain the basic geological information of the real field. The proposed method is tested first on a synthetic data ensemble for production forecast and then applied to a real field. Based on real observations, we use the DiWA model for data quality diagnostic and identify certain flaws in the collected data and model assumptions. Based on these findings, the original assumptions and data observations have been adjusted and the resulting DiWA model was successfully trained. The prediction quality of the trained DiWA model is comparable to conventional simulation techniques based on detailed geological models and has the advantage of a much more efficient and faster ability to update and maintain the subsurface model when continuous updates in production data become available. This study shows that the proposed method can provide the history matching results with high accuracy and low computational costs. Furthermore, the performance of the stochastic DiWA model can be further improved using more comprehensive and geologically constrained initial and boundary conditions.@en

Surfactant alternating gas (SAG) is often the injection strategy used for injecting foam into a reservoir. However, liquid injectivity can be very poor in SAG, and fracturing of the well can occur. Coreflood studies of liquid injectivity directly following foam injection have been reported. We conducted a series of coreflood experiments to study liquid injectivity under conditions more like those near an injection well in a SAG process in the field (i.e., after a period of gas injection). Our previous experimental results suggest that the injectivity in a SAG process is determined by propagation of several banks. However, there is no consistent approach to modeling liquid injectivity in a SAG process. The Peaceman equation is used in most conventional foam simulators for estimating the wellbore pressure and injectivity. In this paper, we propose a modeling approach for gas and liquid injectivity in a SAG process on the basis of our experimental findings. The model represents the propagation of various banks during gas and liquid injection. We first compare the model predictions for linear flow with the coreflood results and obtain good agreement. We then propose a radial-flow model for scaling up the core-scale behavior to the field. The comparison between the results of the radial-propagation model and the Peaceman equation shows that a conventional simulator based on the Peaceman equation greatly underestimates both gas and liquid injectivities in a SAG process. The conventional simulator cannot represent the effect of gas injection on the subsequent liquid injectivity, especially the propagation of a relatively small region of collapsed foam near an injection well. The conventional simulator’s results can be brought closer to the radial-flow-model predictions by applying a constant negative skin factor. The work flow described in this study can be applied to future field applications. The model we propose is based on a number of simplifying assumptions. In addition, the model would need to be fitted to coreflood data for the particular surfactant formulation, porous medium, and field conditions of a particular application. The adjustment of the simulator to better fit the radial-flow model also would depend, in part, on the grid resolution of the near-well region in the simulation.@en