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 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., amount of steam allocated to each well pad.
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 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, 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.
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-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.
Associative polymer (AP) solutions in general exhibit higher resistance factors and subsequently lower injectivity than hydrolyzed polyacrylamide (HPAM) because of strong extensional flow characteristic in porous media despite having similar shear viscosity. From a scientific point of view, the challenge is to understand and quantify these properties in terms of the nature of their association in water. The kind of hydrophobic association (intramolecular or intermolecular) that AP exhibit is concentration dependent and will influence not only the shear but also the extensional properties and therefore elongational flow as well in the porous media. Therefore, the role of hydrophobic association on shear and extensional rheology and its effect on the injectivity in porous media requires comparative investigation over its counterpart non-associating HPAM.
Unlike shear rheology, measurement of bulk extensional properties for relatively low viscous enhanced oil recovery polymer solutions remains a challenge. In this study, extensional rheology measurements are performed using capillary breakup extensional rheometer (CaBER). The CaBER setup uses a stepstrain to stretch a droplet of liquid placed between the two plates and follows its midplane diameter that declines exponentially in the intermediate time scale where the filament breakup is governed by the balance between driving surface tension and resisting elastic force. The midpoint diameter is fitted with the upper-convected Maxwell model to determine the extensional relaxation time. Extensional viscosity calculated using the axial force balance at the critical Deborah number (
Extensional relaxation time of AP and HPAM solutions correspond to 1.2s and 1s at 2000 ppm, whereas at 1000 ppm these values are 0.45s and 0.53s, correspondingly. At
Characterization method presented here can be used for quantifying the elongation flow in porous media and predicting the injectivity behavior of associative and non-associative HPAM polymers. The method can be used for quick screening of polymers having similar shear rheology.
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.
Xie, Donghai (Enhanced Oil Recovery Institute, China University of Petroleum) | Hou, Jirui (Enhanced Oil Recovery Institute, China University of Petroleum) | Doda, Ankit (School of Mining and Petroleum, University of Alberta) | Trivedi, Japan J (School of Mining and Petroleum, University of Alberta)
Alkali is an important component for alkali/surfactant/polymer technology for enhanced oil recovery. The mechanism and advantages of traditional inorganic alkali for enhanced oil recovery (EOR) was reviewed in this paper. But the weakness of inorganic alkali, such as scaling, corrosion and high cost of water treatment, are significant too. This paper compares the use of a type of organic alkali ethanolamine for EOR with inorganic alkali (NaOH) for alkali-polymer (AP) and alkali-surfactant-polymer flooding. The solution of 0.1 wt% polymer (FLOPAAM 3130S) was mixed with different concentrations of ethanolamine and NaOH respectively. The rheological and dynamic properties of the combination of alkali and polymer were analyzed. The results show that the polymer solution with ethanolamine has better shear viscosity and elastic properties at room temperature. Surfactant (Alfaterra 123-8S-90) with concentration of 0.15 wt% was added into each alkali-polymer solution. No significant change was observed in rheological properties of alkali-polymer solutions with and without surfactant. Emulsification test shows that ethanolamine has better performance with oil. Injectivity tests were also conducted. The results indicated that RRF for ethanolamine-polymer solution is always higher at each flow rate tested in comparison to NaOH AP solution which is beneficial for oil recovery. Core flooding experiments were tested in homogeneous sand pack and the performance of ethanolamine-polymer and NaOH-polymer was compared. The pressure comparison during flooding shows that it has higher injection pressure in ethanolamine conditions which result in good sweep efficiency. The ethanolamine-polymer flooding showed a significant increase in oil recovery (15.33%) over NaOH-polymer flooding. After addition of surfactant, the total recovery improves by 14.8% for ethanolamine-polymer-surfactant flooding over its inorganic counterpart. The results of core flooding indicate that ethanolamine has better performance in EOR for AP flooding and ASP flooding. Ethanolamine can become a potential alkali and can replace NaOH for EOR.
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.
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 but is very prone to alkaline conditions as it hydrolyzes very rapidly under such environment.
To overcome this shortfall of conventional HPAM offer stability against alkali, a new co-polymer P(AAc-st-VP) was synthesized using Acrylic Acid (AA) and N-vinyl-2-pyrrolidinone (NVP) and proper initiator. 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.