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Abstract Activating naturally occurring nanoparticles in the reservoir (clays) to generate Pickering emulsions results in low-cost heavy oil recovery. In this study, we test the stability of emulsions generated using different types of clays and perform a parametric analysis on salinity, pH, water to oil ratio (WOR), and particle concentration; additionally, we report on a formulation of injected water used to activate the clays found in sandstones to improve oil recovery. First, oil-in-water (O/W) emulsions generated by different clay particles (bentonite and kaolinite) were prepared for both bottle tests and zeta potential measurements, then the stability of dispersion was measured under various conditions (pH and salinity). Heavy crude oils (50 to 170,000 cP) were used for all experiments. The application conditions for these clay types on emulsion generation and stability were examined. Second, sandpacks with known amounts of clays were saturated with heavy-oil samples. Aqueous solutions with various salinity and pH were injected into the oil-saturated sandpack with a pump. The recoveries were monitored while analyzing the produced samples; a systematic comparison of emulsions formed under various conditions (e.g., salinity, pH, WOR, clay type) was presented. Third, glass bead micromodels with known amounts of clays were also prepared to visualize the in-situ behavior of clay particles under various salinity conditions. The transparent mineral oil instead of opaque heavy oil was used in these micromodel tests for better visualization results. Recommendations were made for the most suitable strategies to enhance heavy oil recovery with and without the presence of clay in the porous medium; moreover, conditions and optimal formulations for said recommendations were presented. The bottle tests showed that 3% bentonite can stabilize O/W emulsions under a high WOR (9:1) condition. The addition of 0.04% of NaOH (pH=12) further improved the emulsion stability against salinity. This improvement is because of the activation of natural surfactant in the heavy oil by the added alkali—as confirmed by the minimum interfacial tension (0.17 mN/M) between the oil and 0.04% of the NaOH solution. The sandpack flood experiments showed an improved sweep efficiency caused by the swelling of bentonite when injecting low salinity fluid (e.g., DIW). The micromodel tests showed a wettability change to be more oil-wet under high salinity conditions, and the swelling of bentonite would divert incoming water flow to other unswept areas thus improving sweep efficiency. This paper presents new ideas and recommendations for further research as well as practical applications to generate stable emulsions for improved waterflooding as a cost-effective approach. It was shown that select clays in the reservoir can be activated to act as nanoparticles, but making them generate stable (Pickering) emulsions in-situ to improve heavy-oil recovery requires further consideration.
Baek, Kwang Hoon (The University of Texas at Austin) | Argüelles-Vivas, Francisco J. (The University of Texas at Austin) | Abeykoon, Gayan A. (The University of Texas at Austin) | Okuno, Ryosuke (The University of Texas at Austin) | Weerasooriya, Upali P. (The University of Texas at Austin)
Abstract A new class of ultra-short hydrophobe surfactants with co-solvent character was investigated as a sole additive to conventional polymer flooding for heavy oil recovery. No alkali was used for emulsification. The surfactants tested are composed of a short hydrophobe (phenol in this research) extended by a small number of propylene oxide (PO) and sufficient ethylene oxide (EO) units to achieve aqueous stability: phenol-xPO-yEO. Results are presented for the selection of ultra-short hydrophobe surfactants, aqueous stability, emulsion phase behavior, and oil-displacement through a glass-bead pack at 368 K. Results show that 2 wt% phenol-4PO-20EO was able to reduce the interfacial tension between oil and NaCl brine to 0.39 dynes/cm, in comparison to 11 dynes/cm with no surfactant, at 368 K. Water flooding, 70-cp polymer flooding, and surfactant-improved polymer flooding were conducted for displacement of 276-cp oil through a glass-bead pack that represents the clean-sand faces of a heavy oil reservoir in Alberta, Canada. The oil recovery at 2 pore-volumes of injection was 84% with the surfactant-improved polymer flooding, which was 54% and 22 % greater than the water flooding and the polymer flooding, respectively. Results suggest a new opportunity of enhanced heavy oil recovery by adding a slug of one non-ionic surfactant with co-solvent character to conventional polymer flooding.
Abstract 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.
Abstract Reservoir development is increasingly moving towards the heavy oil resources due to the rapid decline in conventional oil reserves. With the production of conventional low gravity crude oil being surpassed by heavy oil production in Alberta, the vast fields of heavy oil have been considered an emerging source of energy to the growing demands for oil and gas. Although the applications of thermal methods have been successful in many enhanced oil recovery (EOR) projects, they are usually uneconomic or impractical in deep and thin pay zones reservoirs. Therefore, polymer flooding is a preferred EOR technique in such reservoirs. An application of polymer flooding in heavy oil reservoirs dates back to more than half a century ago. However, it has long been considered a suitable method for reservoirs with viscosities up to 100 centipoises only. Recently, this EOR technique has attracted great attentions and become a promising method for oil recovery from heavy oil reservoirs with viscosities ranging from several hundreds to several thousands of centipoises. The main reasons for such a widespread application of the technique in heavy oil reservoirs during the last two decades have been rises in oil prices, extensive use of horizontal wells and advances in the polymer manufacturing technology. This paper aims to review the advances and technological trends of polymer flooding in heavy oil reservoirs since the 1960s. Upon the review, complete data sets of the laboratory works, pilot tests and field applications are established. The database provides qualitative description and quantitative statistics regarding both scientific research and practical applications. Then suitable ranges of some crucial affecting reservoir properties and polymer characteristics for successful field applications are examined. Finally, new screening criteria are developed specifically for heavy oil reservoirs based on an analysis of the data. The criteria are compared with the previously established ones. The outcome of this paper can be used as guidelines for screening, planning, design and eventually implementation of future projects.
Abstract Thermal EOR has long been considered the sole Enhanced Oil Recovery method for heavy oil but this is no longer the case; several heavy oil polymer floods have proven successful and more are in the planning stages. In the US alone several billion barrels of oil could be targeted; in the rest of the world and in Latin America in particular the potential target is also probably large but mostly unknown at this point. Even though polymer flooding recovery is usually lower than with thermal methods, it is less capital intensive and may be the only economical solution for instance in thin reservoirs. As any EOR project, polymer flooding of heavy oil is done in stages – screening, feasibility study, pilot preparation, pilot execution and eventually full field deployment. Each of these stages requires care and attention to details and many pitfalls need to be avoided in order to reach the final stage of full deployment. This paper intends to provide guidelines on the whole process, based on practical experience and illustrated with actual field cases. This should allow operators to benefit from a better understanding of the challenges and potential of polymer flooding of heavy oil and open the door for more projects.
Abstract The development of any hydrocarbon resource should be planned to maximize the net present value (NPV) of the asset to stakeholders, subject to any imposed constraints. For example, in the evaluation of a single oil or gas well on primary production, assuming no additional constraints, maximization of the NPV may be obtained by maximizing recoverable volume, production rate, and realized product price, while at the same time minimizing capital and operating costs, royalties, and taxes. Maximization of the NPV of a thermal heavy oil project is significantly more involved than that of a single oil or gas well on primary production. This is due to the complex interplay of individual well production and injection profiles with field level production and injection constraints imposed by the central processing facility (CPF). In addition, for thermal heavy oil recovery methods such as cyclic steam stimulation (CSS), the scheduling of the production, soak, and injection cycles of the wells has a significant impact on the overall project NPV. This paper presents the results of a study to maximize the NPV of a greenfield CSS project by incorporating a recently developed horizontal CSS analytical model with a new surface model and economic evaluation model developed specifically for this purpose. The integration of the sub-surface, surface, and economic models allows for the optimization of input parameters simultaneously across the models to maximize the NPV of the entire project. The overall workflow and resulting optimized case will be summarized and discussed. In addition, stochastic simulation concepts are applied to the model to produce a distribution of results based on various input parameters. Stochastic simulations are already used in unconventional gas evaluations, and the authors believe that they will become an important tool to assist in the evaluation of thermal heavy oil projects due to the significant upfront capital cost and uncertainties associated with such developments.
Abstract The heavy oil resources worldwide are estimated at 3396 billion barrels. With depletion of light oil, we have to face the technical and economical challenges of developing heavy oil fields. The recovery of heavy oil reservoirs is often less than 20%, or even below 10%. Even though thermal methods have been successfully applied to many heavy oil fields, thin pay zones are not suitable for thermal recovery. Based on past experiences, polymer flood is not recommended for oil viscosity over 100 centipoises. In recent years, polymer flood becomes a promising technology for heavy oil recovery thanks to the widespread use of horizontal wells. This paper highlights the research advances in polymer enhanced heavy oil recovery since 1977. Based on laboratory tests, polymer can achieve tertiary recovery of more than 20% for heavy oil. A few field cases in China, Canada, Turkey, Suriname and Oman are reviewed and analysed. Some field pilots have shown positive results.
The Alberta carbonates are largely untapped and found in shallow, high oil saturation reservoirs of good thickness. The low gravity high viscosity oil is often located in close proximity to Alberta's major oil sands deposits. Efforts to exploit the carbonate deposits began more than 30 years ago but have faced significant technological challenges. A partnership between government and industry will be required to develop recovery technology. This paper will outline some of the initial concepts for a consortium to pursue technology development and demonstration efforts to exploit the massive untapped carbonate resource.