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Summary Since the introduction of viscous/capillary concepts by Moore and Slobod (1956), several modifications and advancements have been made to the capillary number (Nc) so that it could have a better correlation with residual oil saturation (Sor) during enhanced oil recovery (EOR). In subsequent years, laboratory-scale studies have indicated that the viscoelastic polymers can influence the Sor reduction at relatively higher fluxes and Nc. Although the flux rate of at least 1 ft/D is reported to be needed for viscoelastic polymers to reduce Sor to a noticeable extent, significant Sor reductions were reported to occur only at higher fluxes that are likely to be seen in the reservoir closer to the wellbore. At similar levels of flux and Nc, the polymer solutions with significant elastic properties have shown higher Sor reduction than viscous polymer of similar shear rheology. However, the existing models used for correlating the polymer’s viscoelastic effect on Sor reduction relies on either core-scale Nc and/or the oscillatory Deborah number (De). De also has limitations in quantifying the polymer’s viscoelastic effects at different salinities. In this paper, a modified capillary number called an extensional capillary number (Nce) is developed using the localized pore-scale extensional viscosity. For viscoelastic polymer solutions, pore-scale apparent viscosity dominated by localized extensional viscosity is calculated to be significantly higher than core-scale apparent viscosity. We provide rheological insights using the variable-strain-rate concept to explain why and when the pore-scale apparent viscosity could become significantly higher, even at a flux of approximately 1 to 4 ft/D, and why it will not be reflected on the core-scale apparent viscosity or pressure drop. An exponential correlation was developed between Nce and Sor using the extensive coreflood experimental data sets extracted from various literature. Performance of Nce for predicting the viscoelastic polymer’s residual oil recovery is compared with conventional Nc, De, and a recent correlation. The results show that newly developed Nce can predict the Sor during polymer flooding for a wide range of operational and petrophysical conditions, including brine-salinity effects.

Summary In deep oil-sands deposits using the steam-assisted-gravity-drainage (SAGD) recovery process, real-time optimization (RTO) involves controlling optimum subcool to ensure steam conformance. Contemporary workflows use linear model predictive control (MPC) with oversimplified models that are inadequate to represent highly complex, spatially distributed, and nonlinear reservoir dynamics. In this research, two novel workflows using nonlinear MPC (NMPC) are proposed. The first workflow reduces an NMPC problem to linear MPC by estimating an equivalent linear model of a nonlinear black-box model in a mean-square-error sense. Another approach is to use nonlinear dynamic models explicitly for accurate prediction of the plant states and/or outputs. The resulting nonconvex, nonlinear cost optimization problem is solved using an interior-point algorithm at each control interval. Proposed workflows are tested using the history-matched, field-scale model of a SAGD reservoir located in northern Alberta, Canada. Qualitative and quantitative analysis of the results reveals that nonlinear black-box models based on system identification theory can successfully capture the nonlinearity of the SAGD process. Also, both workflows can control the subcool above the desired set-point while ensuring stable well operations. More than a 24% increment is achieved in net present value (NPV) using proposed NMPC workflows compared with the field operations with no closed-loop control. Overall, NMPC can successfully be used for improved RTO, energy efficiency, and greenhouse gas emissions while considering available surface facilities and well configurations.

Summary Several studies have tried to relate polymers’ enhanced oil recovery (EOR) potential to their viscoelastic characteristics such as onset, rheo thickening, extensional viscosity, and Deborah number (De). Contradictions prevail when it comes to reduction in residual oil saturation (Sor) during polymer flooding and the role of extensional properties. De calculated using the oscillatory relaxation time fails to explain the different pressure profiles exhibited by the viscous and viscoelastic polymers. Extensional viscosity has been ignored in many studies as the reason for additional Sor reduction based on the core-scale apparent viscosity and core-scale capillary number (Nc). In recent studies, a significant oil mobilization was shown by the viscoelastic polymers even before the critical Nc, which indicates that the capillary theory breaks out under specific conditions during polymer flooding. Moreover, the additional residual oil recovery caused by the high-salinity polymer solutions cannot be explained by the oscillatory De. In this paper, we compile and examine many such unresolved challenges from various literature with rheological and petrophysical insights. The uniaxial bulk extensional rheology is performed on the relevant polymers using a capillary breakup extensional rheometer to measure the extensional relaxation time, maximum extensional viscosity at the critical De, and strain hardening index. A detailed analysis signifies the role of extensional rheology on the viscoelastic onset, rheo thickening, and Sor reduction even under varying salinity conditions. The results also highlight the advantages of extensional rheology over oscillatory rheology and validate the capillary theory using modified capillary number.

Summary For heavy-oil-recovery applications, mobility control is more important than interfacial-tension reduction, and therefore importance should be given to the recovery of remaining mobile oil by enhanced sweep efficiency. Although the relative roles of polymer viscosity and elasticity in capillary-trapped residual light-oil recovery have been studied extensively, their roles in sweeping mobile viscous oil have not been explored. Injectivity is vital for heavy-oil-recovery applications, and polymer selection is performed solely using criteria that is based on shear rheology. In this paper, the influence of viscous (shear) resistance and elastic (extensional) resistance of viscoelastic polymer on mobile-heavy-oil recovery and injectivity is investigated through the combination of bulk shear/extensional rheology and single-phase and multiphase coreflood experiments at a typical reservoir-flooding rate of 1 ft/D. Two polymer solutions with different concentrations and salinities are selected such that a polymer with low molecular weight (MW) [hydrolyzed polyacrylamide (HPAM) 3130] provides higher shear resistance than a high-MW polymer (HPAM 3630). Extensional characterization of these two polymer solutions performed using a capillary breakup extensional rheometer revealed that HPAM 3630 provided higher extensional viscosity than HPAM 3130. The results show that the behaviors of polymers in extension and shear are completely different. Two multiphase and two single-phase experiments are conducted at low flux rate to investigate the roles of extensional viscosity on mobile-heavy-oil recovery and high flux rates on injectivity. After 1 pore volume (PV) of polymer injections, higher-concentration and lower-MW HPAM 3130 contributes to approximately 17% higher incremental recovery factor vs. lower-concentration and higher-MW HPAM 3630. The core-scale pressure drop generated by HPAM 3130 is more than twice the pressure drop generated by HPAM 3630. Under low-flux-rate conditions at the core scale, shear forces dominate, and displacing fluid with higher shear viscosity contributes to better sweep. HPAM 3630 exhibits a shear-thickening phenomenon and possesses the apparent viscosity of approximately 90 cp at the flux rate of approximately 90 ft/D. In contrast, HPAM 3130 continued showing shear thinning and has the apparent viscosity of approximately 70 cp at approximately 90 ft/D. This signifies the role of extension rheology on the injectivity at higher flux rates. Results revealed that while the extensional rheological role toward sweeping the mobile heavy-oil recovery at low flux is lesser compared with the shear role, its negative role on the polymer injectivity is very significant. Polymer-selection criteria for heavy-oil-recovery applications should incorporate extensional rheological parameters.

Recently, carboxybetaine based zwitterionic surfactants (CnDmCB) have gained attention for surfactant aided recovery processes for unconventional oil-wet reservoirs due to their high salinity tolerance, wettability alteration potential and ultra-low IFT at extremely low concentrations. Several researchers have investigated the dominant recovery mechanisms among the wettability alteration and interfacial tension (IFT) reduction during surfactant flooding in unconventional, tight, oil-wet reservoirs. Most of the previous studies carried out using spontaneous imbibition fail to answer the dominance of prominent mechanisms, especially with respect to time and location.In this paper, these research gaps are addressed through physico-chemical interactions, and microfluidic studies carried out using carboxybetaine based zwitterionic surfactants (CnDmCB). Four zwitterionic surfactants corresponding to tertiary amines with different chain lengths of 12, 14, 16, 18 carbons were synthesized and characterized by 1H NMR. IFT measurements and rock wettability were investigated for wide range of salinities and surfactant concentrations. CnDmCB surfactant based on its ability to alter rock wettability and reduce interfacial tension reduction was selected for representative carbonate microfluidic studies.Experimental results demonstrated the IFT of carboxybetaine surfactants reduced with the increasing carbon chain length except for C18DmCB due to its poor solubilisation in the high saline brine system. The lowest IFT was up to 4*10-3mN/m for surfactant C16DmCB under the concentration of 0.025 wt% with produced high saline brine. However, this formulation called as F1 could change the zeta potential values of limestone only mildly from 3.07 mV to −3.79 mV. F2 and F3 formulations could change zeta potential value from 10.4 mV to −6.22 mV and −8.12 mV respectively. This signifies that higher wettability alteration potential of F3 and F2 when compared with F1 formulation. The IFT of F2 formulation is also ultralow (6.6*10-3mN/m), whereas the IFT of F3 formulation is relatively higher (0.115 mN/m). The observations of microfluidic studies are significant to emphasize that at early time F2 and F3 formulation corresponded to higher imbibition rate than F1 formulation due to its higher ability to alter the rock wettability from oil-wet to water-wet. As the front propagates to far location, F1 with ultra-low IFT begins to outperform F3 by providing better microscopic displacement and quicker front propagation throughout. The amount of residual oil trapped at far location was higher for F3 formulation than F1 due to its higher IFT.The results of the study signify that significant IFT reduction is needed at later stage while good wettability alteration is important during early stage of flooding and therefore this study holds significance in selecting or designing surfactant based fluid formulation for applications in unconventional tight reservoirs.

Since decades, steam-assisted oil recovery processes have been successfully deployed in heavy oil reservoirs to extract bitumen/heavy oil. Current resource allocation practices mostly involve reservoir model-based open loop optimization at the planning stage and its periodic recurrence. However, such decades-old strategies need a complete overhaul as they ignore dynamic changes in reservoir conditions and surface facilities, ultimately rendering heavy oil production economically unsustainable in the low-oil-price environment. Since steam supply costs account for more than 50% of total operating costs, a data-driven strategy that transforms the data available from various sensors into meaningful steam allocation decisions requires further attention.In this research, we propose a purely data-driven algorithm that maximizes the economic objective function by allocating an optimal amount of steam to different well pads. The method primarily constitutes two components: forecasting and nonlinear optimization. A dynamic model is used to relate different variables in historical field data that were measured at regular time intervals and can be used to compute economic performance indicators (EPI). The variables in the model are cumulative in nature since they can represent the temporal changes in reservoir conditions. Accurate prediction of EPI is ensured by retraining the regression model using the latest available data. Then, predicted EPI is optimized using a nonlinear optimization algorithm subject to amplitude and rate saturation constraints on decision variables i.e., the amount of steam allocated to each well pad.The proposed steam allocation strategy is tested on 2 well pads (each containing 10 wells) of an oil sands reservoir located near Fort McMurray in Alberta, Canada. After an exploratory analysis of production history, an output error (OE) model is built between logarithmically transformed cumulative steam injection and cumulative oil production for each well pad. Commonly used net-present-value (NPV) is considered as EPI to be maximized. Optimization of the objective function is subject to distinct operating conditions and realistic constraints. By comparing results with field production history, it can be observed that optimum steam injection profiles for both well pads are significantly different than that of a field. In fact, the proposed algorithm provides smooth and consistent steam injection rates, unlike field injection history. Also, the lower steam-oil ratio is achieved for both well pads, ultimately translating into ∼19 % higher NPV when compared with field data.Inspired from state-of-the-art control techniques, the proposed steam allocation algorithm provides a generic data-driven framework that can consider any number of well pads, EPIs, and amount of past data. It is computationally inexpensive as no numerical simulations are required. Overall, it can potentially reduce the energy required to extract heavy oil and increase the revenue while inflicting no additional capital cost and reducing greenhouse gas emissions.

Summary Steam-assisted gravity drainage (SAGD) is a thermal-recovery process to produce bitumen from deep oil-sands deposits. The efficiency of the SAGD operation depends on developing a uniform steam chamber and maintaining an optimal subcool (difference in saturation and actual temperature) along the length of the horizontal well pair. Heterogeneity in reservoir properties might lead to suboptimal subcool levels without the application of closed-loop control. Recently, model-predictive control (MPC) has been proposed for real-time feedback control of SAGD well pairs based on real-time production, temperature, and pressure data along with other well and surface constraint information; however, reservoir dynamics has been represented using extremely simplified and unrealistic models. Because SAGD is a complex, spatially distributed, nonlinear process, an MPC framework with models that account for nonlinearity over an extended control period is required to achieve optimized subcool and steam conformance. In this research, two novel work flows are proposed to handle nonlinear reservoir dynamics in MPC. The first approach is adaptive MPC, and includes continuous re-estimation of the model at each control interval. This allows the evolution of the coefficients of a fixed-model structure such that the updated system-identification model in the MPC controller reflects current reservoir dynamics adequately. Another approach, gain-scheduled MPC, decomposes the subcool-control problem in a parallel manner, and uses a bank of multiple controllers rather than only one controller. This ensures effective control of the nonlinear reservoir system even in adverse control situations by using appropriate variations in input parameters based on the operating region. The work flows are implemented using a history-matched numerical model of a reservoir in northern Alberta. Steam-injection rates and liquid-production rate are considered input variables in MPC, constrained to available surface facilities. The well pair is divided into multiple sections, and the subcool of each section is considered an output variable. Results are compared with actual field data (in which no control algorithm is used), and are analyzed on the basis of two criteria: (1) Do all subcools track the set point while maintaining stability in input variables? and (2) Does the net present value (NPV) of oil improve with adaptive and gain-scheduled MPC? In general, we conclude that both adaptive and gain-scheduled MPC provide superior tracking of subcool set points and, hence, better steam conformance caused by adequate representation of reservoir dynamics by re-estimation of coefficients and multiple controllers, respectively. In addition, the results indicate stability in input parameters and improvement in economic performance. NPV is improved by 23.69 and 10.36% in case of adaptive and gain-scheduled MPC, respectively. The proposed work flows can improve the NPV of an SAGD reservoir by optimizing the well-operational parameters while considering constraints of surface facilities and minimizing environmental footprint.

Fractured reservoirs are highly heterogeneous and can be characterized by the probability distributions of fracture properties in a discrete fracture network model. The relationship between production performance and the fracture parameters is vastly nonlinear, rendering the process of adjusting model parameters to match both the static geological and dynamic production data challenging. This creates a need for a comprehensive history matching workflow for fractured reservoirs, which considers different local as well as global fracture parameters and leads to multiple equally-probable realizations of the discrete fracture network model parameters for uncertainty quantification. This paper presents an integrated approach for the history matching of fractured reservoirs. This new methodology includes generating multiple discrete fracture models, upscaling them for numerical multiphase flow simulation, and updating the fracture properties using dynamic flow responses such as continuous rate and pressure measurements. Available geological and tectonic information such as well-logs, seismic, and structural maps are incorporated into commercially available DFN modeling and simulation software to infer the probability distributions of relevant fracture parameters (including aperture, length, connectivity, and intensity) and to generate multiple discrete fracture network model realizations. The fracture models are further upscaled into an equivalent continuum dual-porosity model in the software using either analytical approaches or dynamic methods. The upscaled models are subjected to the flow simulation, and their production performances are compared to the true recorded responses. An automated history matching algorithm is implemented to reduce the uncertainties of the fracture properties. Components of vectors representing the principal flow directions and average fracture orientations are obtained by means of eigenvector decomposition of the permeability tensor and are optimized in the algorithm. In addition, both global fracture intensity and the local grid based intensity, which highly affect the fluid flow pattern and rate in different regions of the reservoir, are adjusted. A case study with various fracture sets is presented. The initial realizations were generated by means of Monte Carlo simulations, using the observed fractures at the well locations. Fracture intensity, orientation, and conductivity of different fracture sets were the uncertain parameters in our studies. Using the proposed methodology, parameters of different fracture sets were satisfactorily updated. Implementation of this automated history matching approach resulted in multiple equally probable discrete fracture network models and their upscaled flow simulation models that honor the geological information, and at the same time they match the dynamic production history.

Carbonate reservoirs hold significant reserves of heavy crude oil that can be recovered by nonthermal processes. Chemical-enhanced oil recovery from oil-wet carbonate reservoirs has focused on the use of surfactants to change wettability and enhance imbibition into the matrix; however, the fractured nature of carbonate formations makes oil recovery a challenging process. Recently, developments in foam/polymer-enhanced foam (PEF) injection for heavy oil recovery application have come about, but the process of PEF for carbonate reservoirs is still not fully understood. This paper introduced a new approach to accessing the heavy oil from fractured carbonate reservoirs. \(\hbox {CO}_{2}\) foam/PEF was used to decrease oil saturation after surfactant flooding. Three types of surfactants (nonionic, anionic, and cationic) were used for both surfactant and foam flooding. A specially designed fractured micromodel representing a porous media system was used to visualize pore-scale phenomena. In addition, the static performances of foam/PEF were analyzed in the presence of heavy crude oil. The results showed that in both static and dynamic studies, the PEF had higher stability than the foam. Nonionic surfactant generated the least stable foam in the presence of crude oil. Surfactant flooding slightly increased the oil recovery from matrix after water injection. This was more evident in the case of cationic surfactant with highest imbibition rate. Observation through this study proved that stable foam/PEF bubbles can significantly push the injected fluid toward untouched parts of the porous media and increased the oil recovery. Due to the liquid viscosity enhancement and bubble stability improvement, the effectiveness of PEF in heavy oil sweep efficiency was much higher than that of conventional foams. PEF bubbles generated an additional force to divert surfactant/polymer into the matrix.

Enhanced oil recovery (EOR) from heavy oil reservoirs is challenging. The higher viscosity of oil in such reservoirs, add more challenges and severe the difficulties during any EOR method (i.e. high mobility ratio, inadequate sweep, reservoir heterogeneity) compared to that of EOR from light oil reservoirs. Foam has gained interest as one of the EOR methods especially for challenging and heterogeneous reservoirs containing light oil. However, the foam and especially polymer enhanced foam (PEF) potential for heavy oil recovery is less studied. The current study aims to evaluate the performance of CO2 foam and CO2 PEF during heavy oil recovery from both unconsolidated (i.e. sandpack) and consolidate (rock sample) porous media with the help of fluid flow experiments. The injection pressure profile, oil recovery, and CO2 gas production were monitored and recorded to analyze and compare the performance of CO2 foam and PEF for heavy oil recovery. A visual sandpack made of glass column and a core-flood system capable of measuring the pressure at different sections of the core were used in this study. Homogenous and fractured sandstone core samples, as well as a fractured carbonate core sample, were selected for the core-flood study. Static stability results revealed slower liquid drainage and collapse rates for PEF compared to that of foam even in the presence of heavy crude oil. The addition of polymer significantly improved the performance of CO2 foam flooding during heavy oil recovery in dynamic experiments. This result was inferred from faster propagation rate, higher dynamic stability, and higher oil recovery of CO2 PEF over CO2 foam injection. Moreover, the visual analysis demonstrated more stable frontal displacement and higher sweep efficiency of PEF compared to the conventional foam flooding. In the fractured porous media, additional heavy oil recovery was obtained by liquid diversion into the matrix area rather than gas diversion inferred from pressure profile and gas production data. The results obtained from this study show that CO2 PEF could significantly improve the heavy oil recovery and CO2 sequestration, especially in homogeneous porous media.

Steam injection rate through life cycle optimization (e.g., the constant rate for a long period of time) could lead to the sub-optimal performance of a thermal heavy oil recovery process. On the other hand, finding the optimal steam injection strategy (policy) represents a major challenge due to the complex dynamic of the physical phenomenon, i.e., nonlinear, slow, high order, time-varying, and potentially highly heterogeneous reservoirs. To address this challenge, the problem can be formulated as an optimal control problem that has typically been solved using adjoint state optimization and a model-predictive control (MPC) strategy. In contrast, this work presents a reinforcement learning (RL) approach in which the mathematical model of the dynamic process (SAGD) is assumed unknown. An agent is trained to find the optimal policy only through continuous interactions with the environment (e.g., numerical reservoir simulation model). At each time step, the agent executes an action (e.g., increase steam injection rate), receives a reward (e.g., net present value) and observes the new state (e.g., pressure distribution) of the environment. During this interaction, an action-value function is approximated; this function will offer for a given state of the environment the action that will maximize total future reward. This process continues for multiple simulations (episodes) of the dynamic process until convergence is achieved. In this implementation, the state-action-reward-state-action (SARSA) online policy learning algorithm is employed in which the action-value function is continually estimated after every time step and further used to choose the optimal action. The environment consists of a reservoir simulation model built using data from a reservoir located in northern Alberta. The model consists of one well pair (one injector and one producer) and production horizon of 250 days (one episode) is considered. The state of the environment is defined as cumulative, oil and water production, and water injection and for each time step; three possible actions are considered, i.e., increase, decrease or no change of current steam injection rate; and the reward represents the net present value (NPV). Additionally, stochastic gradient descent is used to approximate the action-value function. Results show that the optimal steam injection policy obtained using RL implementation improves NPV by at least 30% with more than 60% lower computation cost.

Carbon dioxide foam flooding is a conventional process to increase the quantity of extracting oil. The short-term stability and relatively low viscosity of CO2 foam motivate the researchers to find a more stable fluid. Colloidal gas aphrons (CGAs) are microbubbles confined by the surfactant multilayer and the viscous water layer. One of the most important characteristics of CGA is their gas-blocking ability. They increase the stability of the surfactant/polymer solution as well as reduce the mobility of CO2 gas. Accordingly, CGA has been recently used in the petroleum industry (drilling operation, production management etc.). The CO2 enhanced oil recovery and sequestration can be one of the major interests of CO2 gas microbubbles. The pressure–volume–temperature (PVT) relationship of CO2 microbubbles is of particular interest, due to the presence of gas in the form of microbubbles in the bulk of the fluid. This paper discusses the phase behavior, rheological characterization, and microbubble size analysis of CO2 microbubbles at different conditions. A PVT cell was used to analyze the stability of CO2 microbubbles after encountering elevated pressure and temperature. The rheological properties and microbubble size analysis of this fluid were performed both before and after the PVT study to demonstrate the effect of compression/decompression and temperature on CO2 microbubbles properties. Furthermore, macro- and micro-scale porous media experiment were performed to analyze the behavior of microbubbles during heavy oil recovery. Microbubble size analysis revealed that most of the initial microbubbles located within a diameter range of 100–120 µm. After PVT tests, fewer amounts of large microbubbles (due to the coalescence of small bubbles) existed compared to that of preserved samples under low pressure condition. This result demonstrates good capability of CO2 microbubbles to maintain their stability under the high pressure and temperature conditions. Compression/decompression of microbubbles revealed that microbubbles can survive up to at least a pressure of 2000 psi, demonstrating its potential for subterranean applications. However, above 50 °C (122 °F), the stability of microbubbles was decreased after compression/decompression up to 2000 psi. Higher temperatures decrease viscosity and elastic/viscous moduli of microbubbles and this study showed that temperature above 50 °C can be critical for rheological properties and the P–V relation of CO2 microbubbles. Furthermore, PVT studies showed that the lower compression/decompression rate drastically affects the stability of CO2 microbubbles and the higher temperature enhances this effect. Finally, the flow resistance characteristic of microbubbles as well as their favorable injectivity indicated the potential of this fluid to enhance heavy oil recovery, particularly in heterogeneous reservoirs with low sweep efficiency.

Summary Advancements in horizontal-well drilling and multistage hydraulic fracturing have enabled economically viable gas production from tight formations. Reservoir-simulation models play an important role in the production forecasting and field-development planning. To enhance their predictive capabilities and to capture the uncertainties in model parameters, one should calibrate stochastic reservoir models to both geologic and flow observations. In this paper, a novel approach to characterization and history matching of hydrocarbon production from a hydraulic-fractured shale is presented. This new methodology includes generating multiple discrete-fracture-network (DFN) models, upscaling the models for numerical multiphase-flow simulation, and updating the DFN-model parameters with dynamic-flow responses. First, measurements from hydraulic-fracture treatment, petrophysical interpretation, and in-situ stress data are used to estimate the initial probability distribution of hydraulic-fracture and induced-microfracture parameters, and multiple initial DFN models are generated. Next, the DFN models are upscaled into an equivalent continuum dual-porosity model with analytical techniques. The upscaled models are subjected to the flow simulation, and their production performances are compared with the actual responses. Finally, an assisted-history-matching algorithm is implemented to assess the uncertainties of the DFN-model parameters. Hydraulic-fracture parameters including half-length and transmissivity are updated, and the length, transmissivity, intensity, and spatial distribution of the induced fractures are also estimated. The proposed methodology is applied to facilitate characterization of fracture parameters of a multifractured shale-gas well in the Horn River basin. Fracture parameters and stimulated reservoir volume (SRV) derived from the updated DFN models are in agreement with estimates from microseismic interpretation and rate-transient analysis. The key advantage of this integrated assisted-history-matching approach is that uncertainties in fracture parameters are represented by the multiple equally probable DFN models and their upscaled flow-simulation models, which honor the hard data and match the dynamic production history. This work highlights the significance of uncertainties in SRV and hydraulic-fracture parameters. It also provides insight into the value of microseismic data when integrated into a rigorous production-history-matching work flow.

Large scale physics-based reservoir models are employed routinely in the prediction of the behavior of steam assisted gravity drainage (SAGD) processes under different operational situations. However, parametric uncertainty persists in these models even after history matching with production data. This uncertainty, and the computational cost associated with the full-scale reservoir simulations, makes it challenging to use reservoir simulators in closed-loop control of reservoirs. As an alternative strategy, we present in this work a dynamic proxy model for the reservoirs based on system identification and the prediction error method using only injection and production data. These proxy models are validated against field data from a SAGD reservoir and simulated synthetic reservoir data and shown to be appropriate for use in model predictive control. We also provide evidence that the predictive power of these models can be improved by the appropriate design of input signals (injection rates and pressures).

The efficiency of a SAGD operation depends on developing a uniform steam chamber and maintaining an optimal subcool along the length of the well pair. Heterogeneity in reservoir properties may lead to suboptimal subcool levels. Recently, Model Predictive Control (MPC) based on the real-time production, temperature, and pressure data along with other well and surface constraint information has been proposed for a real-time feedback control of SAGD well pairs. Reservoir dynamics in MPC is represented using either linear step response model or one-dimensional ordinary differential equation. However, such simplified models are insufficient in MPC since SAGD is more complex and highly nonlinear process. Therefore, MPC framework that represents nonlinear behaviour of SAGD over an extended control period is required to achieve optimized subcool and steam conformance. In this research, two novel workflows are proposed to handle nonlinear reservoir dynamics in MPC. First approach known as Adaptive MPC includes recursive estimations at each control interval based on system identification theory. This allows evolution of the coefficients of a fixed model structure such that the updated system identification model in MPC controller reflects current reservoir dynamics adequately. Another approach, Gain-Scheduled MPC, decomposes the subcool control problem in a parallel manner and uses a bank of multiple controllers rather than only one controller. This ensures effective control of the nonlinear reservoir system even in adverse control situations by employing aggressive variations in input parameters. Suggested workflows are implemented using history-matched numerical model of a reservoir located in northern Alberta. Steam injection rates and liquid production rate are considered as input variables in MPC, constrained to available surface facilities. Well-pair is divided into multiple sections and subcool of each section is considered as an output variable. Optimum set-point for subcool is considered as 20°C. Results are compared with actual field data (in which no control algorithm is used) and analyzed based on two criteria: 1) Do all subcools track optimum set-point while maintaining stability in input variables and 2) Does net present value (NPV) of oil improve in case of Adaptive and Gain-Scheduled MPC? In general, we conclude that both Adaptive and Gain-Scheduled MPC provide superior tracking of subcool set-point and hence better steam conformance due to adequate representation of reservoir dynamics by recursive estimation of coefficients and multiple controllers. In addition, results indicate stability in input parameters and improvement in economic performance. NPV is improved by 23.69% and 10.36% in case of Adaptive and Gain-Scheduled MPC, respectively. Under current economic scenario, proposed workflows can improve the NPV of a SAGD reservoir by optimizing the well operational parameters while considering constraints of surface facilities and minimizing environmental footprints.

Aqueous based foam injection has gained interest for conventional oil recovery in recent times. Foam can control the mobility ratio and improve the sweep efficiency in oil reservoirs over gas flooding. However, due to the high viscosity of oil, its application in heavy oil reservoirs is challenging. Moreover, oil-wet nature of carbonate reservoirs makes it difficult for aqueous based foam to efficiently remove the heavy oil. On the other hand, hydrocarbon solvents have been used for decreasing the heavy oil viscosity and increase its recovery by diffusion and mixing mechanisms. However, low rate of diffusion/dispersion and inadequate sweep efficiently, especially in heterogeneous reservoirs, are of the main challenges during solvent injection. Combination of foam and solvent (solvent based foam) can overcome the challenges existing in the separate application of aqueous based foam and solvent injection for heavy oil recovery. The challenge is to understand how the combination of solvent and foam will help us to improve the heavy oil sweep efficiency. This paper introduced a new approach to increase sweep efficiency during heavy oil recovery with the help of hydrocarbon solvent-based CO2 foam. Foam was generated with the help of a fluorosurfactant in the hydrocarbon solvent. Static bulk performances of foam were analyzed at different concentrations of surfactant. Surface tension measurement was also performed to study the adsorption of surfactant into the liquid-gas interface and its effect on foamability and foam stability. A specially designed fractured micromodel (oil wet, representing fractured carbonate reservoirs) were used to visualize the pore scale phenomena during solvent based foam injection. A high quality camera was utilized to capture high quality images/movies. According to static experiments, although the value of the surface tension of hydrocarbon solvent was initially low, the addition of surfactant slightly decreased the surface tension further and surfactant adsorption at the interface improved the foam stability. This process was more evident in higher concentration of surfactant. In addition, dynamic pore scale observation through this study revealed that solvent based foam can significantly contribute to heavy oil recovery with different mechanisms. At initial stage, solvent diffuses and mixes with viscous oil and reduce the viscosity. Later, foam bubbles improve the sweep efficiency by diverting the solvent toward untouched part of the porous media. In addition, foam bubbles partially blocked the opening area in matrix/swept-area increasing the contact of solvent and heavy oil, providing better mixing. Therefore, oil is swept much faster and more efficiently from the grain in oil-wet porous media compared to that of conventional solvent flooding. Successful application of solvent based foam can significantly improve the heavy oil recovery in reservoirs with high heterogeneity and oil-wet matrix. Cooperation of diffusion/dispersion and mobility reduction will result in faster oil production and lesser amount of oil will leave behind improving the sweep efficiency.

The Ensemble Kalman Filter (EnKF) is a Monte-Carlo based technique for assisted history matching and real time updating of reservoir models. However, it often fails to detect facies boundaries and proportions as the facies distributions are non-Gaussian, while geologic data for reservoir modeling is usually insufficient. It is convenient to represent distinct facies with non-Gaussian categorical indicators; we implemented discrete cosine transform (DCT) to parameterize the facies indicators into coefficients of the retained cosine basis functions that are Gaussian. For highly complex and heterogeneous models, though observed data were matched, it failed to reproduce realistic facies distribution corresponding to reference variogram and facies proportion. In this paper we propose a new ensemble filtering method in-between of EnKF and PF, where EnKF as predictor combines the advantages of accurate large updates with small ensembles and corrector for non-Gaussian distributions followed by EnKF again for analysis step. Correction is performed by regenerating new realizations using a new pilot point method. The ensemble members that are more consistent with the early production history and the available geological information are considered as high weight particles and used for the applications. Combination of DCT-EnKF and regenerating new realizations using the new pilot point method demonstrates reasonable improvement and reduction of uncertainty in facies detection. Incorporating the new step in the procedure assists the filter to honor the reference distribution and experimental variogram during the history matching process and presents an important potential in improved characterization of complex reservoirs.

In the oil industry, dynamic spontaneous imbibition plays an important role in several flow processes in porous media. A numerical approach is developed to simulate dynamic spontaneous imbibition with variable inlet saturation and interfacial coupling. The inclusion of interfacial coupling effects invalidates the assumption that the interfaces (fluid/fluid and fluid/solid) act in the same way. The one-dimensional numerical simulation model is developed using a Lagrangian formulation discretized in time and saturation. The solution of the partial differential equations utilizes an iteration process that includes two material balance criteria to ensure the validity of the variable inlet saturation. Furthermore, an error analysis, the validation of the model and a sensitivity study on the optimal number of time steps and saturation grid cells are undertaken. The numerical simulation solution represents an accurate approach to investigate the effect of fluid and rock properties on dynamic spontaneous imbibition.

In oil recovery from fractured reservoirs, dynamic spontaneous imbibition (DSI) plays an important role. Conventional equations used for characterizing dynamic spontaneous imbibition neglect the effects of the driving forces acting across the wetting and non-wetting phases which are flowing in opposite directions. Such effects, defined as interfacial coupling effects (ICE), are known to cause a decrease in the calculated flow rate in drainage processes. Moreover, none of the numerical models have considered a variable inlet saturation (S*) for DSI. A new theoretical model has been developed using generalized transport equations to describe dynamic spontaneous imbibition for immiscible two-phase flow processes. The inclusion of interfacial coupling effects provides a more accurate way to describe dynamic spontaneous imbibition. Furthermore, the addition of variable inlet saturation allows one to establish whether the inlet-face saturation (S*) increases from the initial saturation to 1−Sro, or whether it can remain constant and equal to one minus the residual saturation to the non-wetting phase (1−Sro).

Distributed temperature sensing (DTS), an optical fiber down-hole monitoring technique, provides a continuous and permanent well temperature profile. In steam assisted gravity drainage (SAGD) reservoirs, the DTS plays an important role to provide depth-and-time continuous temperature measurement for steam management and production optimization. These temperature observations provide useful information for reservoir characterization and shale detection in SAGD reservoirs. However, use of these massive data for automated SAGD reservoir characterization has not been investigated. The ensemble Kalman filter (EnKF), a parameter estimation approach using these real-time temperature observations, provides a highly attractive algorithm for automatic history matching and quantitative reservoir characterization. Due to its complex geological nature, the shale barrier exhibits as a different facies in sandstone reservoirs. In such reservoirs, due to non-Gaussian distributions, the traditional EnKF underestimates the uncertainty and fails to obtain a good production data match. We implemented discrete cosine transform (DCT) to parameterize the facies labels with EnKF. Furthermore, to capture geologically meaningful and realistic facies distribution in conjunction with matching observed data, we included fiber-optic sensor temperature data. Several case studies with different facies distribution and well configurations were conducted. In order to investigate the effect of temperature observations on SAGD reservoir characterization, the number of DTS observations and their locations were varied for each study. The qualities of the history-matched models were assessed by comparing the facies maps, facies distribution, and the root mean square error (RMSE) of the predicted data mismatch. Use of temperature data in conjunction with production data demonstrated significant improvement in facies detection and reduced uncertainty for SAGD reservoirs. The RMSE of the predicted data is also improved. The results indicate that the assimilation of DTS data from nearby steam chamber location has a significant potential in significant reduction of uncertainty in steam chamber propagation and production forecast.

Advancements in horizontal well drilling and multistage hydraulic fracturing have made gas production from tight formations economically viable. Reservoir simulation models play an important role in the production forecasting and field development planning. To enhance their predictive capabilities and capture the uncertainties in model parameters, stochastic reservoir models should be calibrated to both geologic and flow observations. In this paper, a novel approach to characterization and history matching of hydrocarbon production from a hydraulic fractured shale gas is presented. This new methodology includes generating multiple discrete fracture network (DFN) models, upscaling the models for numerical multiphase flow simulation, and updating the DFN model parameters using dynamic flow responses. First, measurements from hydraulic fracture treatment, petrophysical interpretation, and in-situ stress data are used to estimate the initial probability distribution of hydraulic and induced micro fractures parameters, and multiple initial DFN models are generated. Next, the DFN models are upscaled into an equivalent continuum dual porosity model using either analytical (Oda) or flow-based techniques. The upscaled models are subjected to the flow simulation, and their production performances are compared to the actual responses. Finally, an assisted history matching algorithm is implemented to assess the uncertainties of the DFN model parameters. Hydraulic fracture parameters including half-length, shape, and conductivity are updated together with the length, conductivity, intensity, and spatial distribution of the induced fractures are optimized in the algorithm. The proposed methodology is applied to facilitate characterization of fracture parameters of a multi-fractured shale gas well in the Horn River basin. Fracture parameters and stimulated reservoir volume (SRV) derived from the updated DFN models are in agreement with estimates from micro-seismic interpretation and rate transient analysis. The key advantage of this integrated assisted history matching approach is that uncertainties in fracture parameters are represented by the multiple equall-probable DFN models and their upscaled flow simulation models, which honor the hard data and match the dynamic production history. This work highlights the significance of uncertainties in SRV and hydraulic fracture parameters. It also provides insight into the value of micro-seismic data when integrated in a rigorous production history matching workflow.

The problems associated with current chemical flooding technologies are based around inadequate sweep efficiencies and unfavorable mobility ratios which leave much of the recoverable oil left untouched in the pores of the reservoir. In order to address the low sweep efficiency and unfavorable mobility ratio issues, numerous formulations of polymer and surfactant base fluids have been used for enhanced oil recovery (EOR) applications with varying degree of success. The use of Colloidal Gas Aphrons (CGA) as an alternative chemical EOR technique is investigated in this study. Colloidal Gas Aphrons (CGA) are described as micro-bubbles which are 10 to 100 microns in size with a gas containing inner core encapsulated by a thin surfactant film. Aqueous CGA fluids are comprised of water, polymer and surfactant solutions. An experimental study was conducted to determine the optimum surfactant and polymer concentrations which would yield stable micro-bubbles. The formulations of stable micro-bubbles were analyzed in terms of rheology, bubble size distribution and time stability. In order to determine the displacement efficiency of CGA fluid in the EOR process, flooding experiments were conducted using a 2D linear model and 3D radial model, both packed with glass beads and saturated with mineral oil. Flooding experiments were performed using a) water, b) aqueous polymer solution, c) aqueous polymer and surfactant solution mixed at low shear rate, d) CGA fluid, e) water followed by CGA fluid, and f) water followed by polymer solution. Efficiency of oil recovery using the CGA fluid was compared to that of other fluids. All experiments were repeated to ensure consistent results. Less than 3 % variation in results was observed in all cases. Pressure drop, ultimate recovery and injected fluid retention time data were measured during the flooding experiments. In addition, time-lapse images taken at regular intervals were analyzed to study frontal displacement patterns observed in 2-D experiments. The results indicated that the CGA fluids showed more stable frontal displacement as compared to water flooding. The cumulative oil recovery performance of CGA fluids was comparable but slightly less than that of aqueous polymer solutions. CGA fluids, however, required significantly lower injection pressure as compared to aqueous polymer solutions. The breakthrough time of CGA fluids was longer than that of any of the other fluids tested indicating that CGAs have longer retention time. Results from preliminary experiments encourage the further investigation of colloidal gas aphrons as an alternative EOR technique. The results will also be useful in designing an EOR process as an alternate to polymer, surfactant-polymer or WAG flood with particular importance to carbon sequestration as CO2 / flue gas can also be used in micro-bubble generation in place of air.

Polymer flooding has been the most widely used enhanced oil recovery technique in both sandstone and carbonate reservoirs. Ample polymer flooding projects have been conducted with different level of success ever since the technique was introduced 50 years ago. It is a usual practice to select a polymer based on viscosity range, concentration and molecular weight without getting into rheological characterization and its effect on oil recovery. However, in recent times the application of polymer flood has gained critical mechanistic insight with advancement in understanding the role of elasticity on sweep efficiency. Therefore, selecting the type of polymer and understanding how its fluid rheology affects oil recovery are probably among the most critical factors involved in designing a successful polymer flood job. To deal with this, a systematic approach for screening a polymer based on rheological characterization was adapted. Three different polymers, partially hydrolyzed polyacrylamide (HPAM) and Polyoxyethylene (PEO) were first used for fluid rheology study using a cone and plate rheometer and then for oil recovery through a special core holder designed to simulate radial flow through a sand pack – saturated with mineral oil. Effects of various rheological parameters such as; a) Newtonian vs. non-Newtonian rheology (constant shear viscosity vs. shear thinning), b) shear viscosity vs. elasticity, and c) average molecular weight vs. molecular weight distribution (polydispersivity) on oil recovery were investigated. Finally, a parametric characterization study was performed to develop a screening criteria and to correlate oil recovery prediction. The approach could lead to a successful screening process of polymer based on various characteristic parameters such as average molecular weight, polydispersity and Trouton ratio.

With the increasing demand for energy production and environmental concerns, the focus has been shifting towards new green recovery techniques for heavy oil complex reservoirs such as naturally fractured carbonate reservoirs (NFCRs). After the success of VAPEX as an alternate to SAGD for heavy oil recovery, it has been studied for fractured reservoirs in recent years. Moreover, CO2 has been a successful EOR agent in fractured light-medium oil reservoirs and thus can be used as a higher soluble, low cost, and environment friendly alternate to solvent. Our aim is to show that CO2 with VAPEX could be a feasible choice for NFCRs. This work is focused on investigating the applicability and optimization of CO2 based solvent extraction process for heavy oil recovery and greenhouse gas sequestration from NFCRs. A series of numerical experiments were performed to study the role of a) solvent type, b) operating condition, c) oil and gas diffusion, and d) extraction process; in various single/multiple fracture configurations while injecting CO2 - solvent in different ratios. The optimal injection rate and CO2 - solvent concentration ratio were obtained considering maximum oil recovery and CO2 storage. A parametric study was also performed to analyze the influence of various operational and reservoir parameters such as fracture properties, fracture configurations, oil viscosity, solvent type, and injection/production constrains. The results suggested that CO2 or conventional VAPEX alone may not be able to achieve higher recovery in high pressure NFCRs, but CO2 based VAPEX can provide efficient technique to recover heavy oil with emphasis on sequestration.

The Ensemble Kalman Filter (EnKF) has gained popularity over recent years as a Monte-Carlo based technique for assisted history matching and real time updating of reservoir models. The EnKF procedure utilizes an ensemble of model states (e.g. realizations of reservoir properties such as porosity and permeability) to approximate the covariance matrices used in the updating process. EnKF works efficiently with Gaussian variables and linear dynamics, but it often fails to preserve the reference probability distribution of the model parameters and to achieve an acceptable production data match where the system dynamics are strongly nonlinear, especially of the type related to multiphase flow, or if non-Gaussian prior models are used. In order to alleviate these drawbacks, we investigated various weighted averaging techniques for computing the ensemble mean by introducing a weighting factor to each ensemble; two new formulations were implemented. The first weighting factor was calculated based on the mismatch in entropy of the model parameters, a normalized measure of the spread of a given probability distribution. The second weighting factor was computed using the forecast mismatch. In addition, both weights could be applied at a single updating step for reducing the forecast mismatch and maintaining the prior distribution simultaneously. The performance of traditional EnKF and these weighted EnKF methods were evaluated by performing various simulation studies with different reservoir heterogeneity. The qualities of the final matching results were assessed by computing the experimental histogram and variograms of the final ensemble, as well as the Root Mean Square Error (RMSE) of the predicted data mismatch. The results reveal that reasonable improvement in the efficiency of the EnKF is achieved by suggested weighted techniques. The RMSE of the predicted data is improved, and the quantity of spurious model parameters is reduced at each updating step. Taking advantage of the entropy based weighting factor assists the filter to preserve the reference distribution. The improvement indicates that the Entropy weighted EnKF (EWEnKF) has a significant potential to resolve the shortfall of traditional EnKF in reservoir characterization and history matching of challenging reservoirs with non-Gaussian distributions.

Productivity in deep-basin tight gas reservoirs can be improved significantly by natural fracture enhanced permeability. Therefore, deviated and horizontal wells are often drilled to intersect highly fractured formations. Unfortunately, fractured reservoirs are highly heterogeneous, often characterized by probability distributions of fracture properties in a discrete fracture network (DFN) model. In addition, the relationship between recovery response and model parameters is vastly non-linear, rendering the process of conditioning reservoir models to both static and dynamic (production) data challenging. In the current paper, a novel approach is presented for uncertainty assessment and characterization of fractured reservoir model parameters using data from diverse sources. First, Monte Carlo based techniques were used to generate multiple DFN models conditioned to geological and tectonic information, accounting for the uncertainty associated with static data. Next, each model or realization was upscaled for flow simulation. Finally, Ensemble Kalman Filter (EnKF), a data assimilation technique that has been used for assisted history matching, was employed to update the DFN models using production data. In order to ensure positive definiteness of the updated permeability tensors, to reduce the size of model parameter space, and to eliminate the redundancy between parameters for improved convergence, principal component analysis was performed such that only the main principal components of the full permeability tensor and sigma factors were updated through EnKF algorithm. The qualities of the history-matched models were assessed by comparing the spatial distribution of the updated model parameters with the initial ensemble, as well as the Root Mean Square Error (RMSE) of the predicted data mismatch. The results clearly demonstrate that, characterization of fractured reservoirs combining DFN modeling with updating principal components of the upscaled model parameters through EnKF has the potential to resolve the shortfall of traditional techniques for history matching of such complicated reservoirs. The proposed approach can be used effectively to update reservoir models and optimize development plans in unconventional gas reservoirs using continuous flow and pressure measurements.

Heavy oil reservoirs in western Canada with viscosities ranging from 1,000 cp to 10,000 cp are being exploited using chemical enhanced oil recovery techniques. The most widely used polymer in enhanced oil recovery applications is hydrolyzed polyacrylamide (HPAM). The primary reason for its vast use is its higher viscosity in an aqueous solution and resistance to bio-degradation however is very prone to alkaline conditions as it hydrolyzes very rapidly under such environment. To overcome this shortfall of conventional HPAM, a new co-polymer P(AAc-st-VP) was synthesized using Acrylic Acid (AA) and N-vinyl-2-pyrrolidinone (NVP) and proper initiator, that can offer stability against alkali. In the research presented herein, currently available conventional HPAM polymers were examined against new synthesized P(AAc-st-VP) co-polymer with improved properties by including different weight percent of N-vinyl-2-pyrrolidinone monomer for use with a focus on highly alkaline environment. Rheological properties were compared in terms of viscosity and elasticity under various NaOH concentrations and aging time, for typical alkali-polymer flood operations. The core flooding experiments of alkali-polymer (AP) flooding was conducted for oil samples collected from a heavy oil reservoir in Alberta. The results were analyzed for tertiary heavy oil recovery performance, residual resistance factor, and residual oil distribution. No significant change in rheological properties of P(AAc-st-VP) co-polymer was observed in presence of alkali even for longer aging times while the conventional HPAM showed much higher viscosity loss, becoming less effective for AP or ASP heavy oil recovery operations. Due to stable rheological characteristic under alkali condition, the new synthesised P(AAc-st-VP) co-polymer showed improved performance over conventional HPAM polymer in terms of injectivity and residual resistance factor. Analysis of the results indicates that AP flooding using P(AAc-st-VP) co-polymer could effectively overcome the drawbacks of conventional HPAM polymer and improve the recovery efficiency for the heavy oil with higher injectivity.