[en] Given enormous capital costs, operating expenses, flue gas emissions, water treatment and handling costs of thermal in situ bitumen recovery processes, improving the overall efficiency by lowering energy requirements, environmental impact, and costs of these production techniques is a priority. Steam-assisted gravity drainage (SAGD) is the most widely used in situ recovery technique in Athabasca reservoirs. Steam generation is done on surface and consequently, because of heat losses, the energy efficiency of SAGD can never be ideal with respect to the energy delivered to the sandface. An alternative to surface steam generation is in situ combustion (ISC) where heat is generated within the formation through injection of oxygen at a sufficiently high pressure to initiate combustion of bitumen. In this manner, the heat from the combustion reactions can be used directly to mobilize the bitumen. As an alternative, the heat can be used to generate steam within the formation which then is the agent to move heat in the reservoir. In this research, alternative hybrid techniques with simultaneous and sequential steam-oxygen injection processes are examined to maximize the thermal efficiency of the recovery process. These hybrid processes have the advantage that during ISC, steam is generated within the reservoir from injected and formation water and as a product of oxidation. This implies that ex situ steam generation requirements are reduced and if there is in situ storage of combustion gases, that overall gas emissions are reduced. In this research, detailed reservoir simulations are done to examine the dynamics of hybrid processes to enable design of these processes. The results reveal that hybrid processes can lower emitted carbon dioxide-to-oil ratio by about 46%, decrease the consumed natural gas-to-oil ratio by about 73%, reduce the cumulative energy-to-oil ratio by between 40% and 70% compared to conventional SAGD, and drop water consumption per unit oil produced. However, oil recovery is between 25% and 40% below that of SAGD. Design of successful hybrid steam-oxygen processes must take into account the balance between injected steam and amount of injected oxygen and combustion gas products that dilute injected and in situ-generated steam in the depletion chamber by lowering its partial pressure, and thus its saturation temperature which in turn impacts production rates and recovery

[en] Highlights: • Steam-Assisted Gravity Drainage (SAGD) has multiple time scales. • Time scale for steam flow in the reservoir can be immediate to 100s of days. • Time scale for bitumen mobilization, drainage, and production is up to 100s of days. • Time between steam stimulation and consequent bitumen response can be separated by 100s of days. Despite technical and economic success of Steam-assisted gravity drainage (SAGD), improvements of its thermal efficiency as reflected by the steam-to-oil ratio (SOR) present challenges. Given that the SOR is a measure of the ratio of the energy invested to the energy (chemical energy in oil) produced and emissions intensity, there is strong motivation to reduce the SOR since the lower the SOR, the lower is the energy invested and emissions produced per unit oil produced. However, there appears to be few directions for modifying SAGD to improve the SOR by adjusting the steam injection strategy or fluid production strategy alone. In this research, multiple steam components and multiple bitumen components are used in a thermal reservoir simulation model to understand the time scales of steam flow and bitumen mobilization, drainage, and production. The results reveal that immediate bitumen response is observed near the well and in the steam-trap liquid pool above the production well whereas the time scale between steam stimulation and bitumen response can be as high as hundreds of days. This brings into question the meaningfulness of the steam-to-oil ratio as a control variable for behaviours far from the well pair.

[en] Highlights: • With natural gas growth, predictive methane emissions models needed. • Proxy modelling complements measurement of emissions during shale gas development. • Shale gas regulations must account for uncertainties of emissions and economics. • In 2015, average emissions/well with flowback is 2347 MgCO₂e/completion in US. • In 2015, average emissions/well with flowback is 1859 MgCO₂e/completion in Canada. Environmental and economic impacts of methane escaping from the natural gas supply chain remain uncertain. Flowback emissions from hydraulically fractured natural gas wells are a key component of emissions from unconventional gas wells. While reduced emission completions in the United States are required by regulation, Canada’s proposed regulation will only be implemented in 2020 with the two highest producing provinces under exemption. To understand potential benefits of regulations, we use predictive modelling of well-level production data of 1633 hydraulically fractured shale gas wells in five plays to estimate pre-production emissions. The mean estimate for flowback emissions (2346 ± 95% confidence interval of 91 Mg CO₂e/completion) fall within the 95% confidence limits of measured potential emissions (2566 ± 777 Mg CO₂e/completion). Our results indicate that in 2015, the average emissions per shale gas well undergoing flowback was 2347 Mg CO₂e/completion in the U.S. and 1859 Mg CO₂e/completion in Canada. Mean potential profits from controlling methane emissions using reduced emission completions were US$17,200/well in the U.S. and US$11,200/well in Canada.

[en] Forecasts suggest that production of bitumen from oil sands reservoirs will increase by a factor of at least 2.5 times over roughly the next 15 years. Although a significant economic benefactor to the Canadian economy, there are challenges faced by oil sands operators with respect to greenhouse gas emissions and water consumption. For the Athabasca deposit, the current oil recovery process of choice is the SAGD (steam-assisted gravity drainage) method where high temperature and high pressure steam is injected into the oil sands formation. At present, there are more than ten SAGD operators in Alberta, Canada and results to date reveal that the geology of the reservoir impacts SAGD performance. Given the growth of the SAGD industry in Alberta, forecasting tools are required that can predict performance versus reservoir characteristics. Here, we present a neural network-based model to predict the SOR (steam-to-oil ratio) in oil sands reservoirs by using log and core data to characterize the reservoir porosity, permeability, oil saturation, depth and thickness. Our analysis confirms that the lower the porosity, permeability, and oil saturation of the reservoir, the worse the performance of the operation. In other words, the lower the quality of the reservoir, the lower the oil rate, and the higher the SOR. Our analysis also shows that well performance (i.e., SOR), is predictable with a relatively high degree of accuracy (R"2∼0.80) using log and core data via a neural network model. These results imply that the depth of the reservoir, gamma ray readings, and permeability are the most important determinants of the variation in SOR. - Highlights: • Artificial Neural Networks are used to predict well performance using reservoir characteristics. • Depth of the reservoir, gamma ray readings, and permeability are most important determinants. • The model uses publicly available data and has strong predictive performance. • Model can be used by stakeholders to inform investment, operating and policy decisions.

[en] Highlights: • Two novel high-capacity and high-stability anode materials for MXenes lithium-ion batteries. • A functional relationship that makes it easy to explore more MXenes materials. As an advanced battery technology, lithium-ion batteries have attracted extensive attention, especially in electric vehicles. However, low capacity and poor stability are two factors hindering more extensive application of lithium-ion batteries. Herein, we performed a screening study on MXenes including M₂C, MC₂, M₂N, MN₂ (M = Sc, Ti, V, Cr), in the search for promising lithium-ion battery anode materials by using density functional theory (DFT) calculations and ab initio molecular dynamic (AIMD) simulations. The theoretical capacities of Ti₂N and V₂N are 975 mAh/g and 924 mAh/g, respectively. Based on low deformation rate, and small energy variation, Ti₂N and V₂N have higher stability than that of graphite electrodes. The lower diffusion barrier accelerates the charging and discharging process of the battery. Considering their low diffusion barrier, excellent conductivity, high theoretical capacity, low deformation rate and high thermal stability, Ti₂N and V₂N are proposed as promising anode materials for lithium-ion batteries. The adsorption of lithium-ion belongs in the category of weak adsorption where the d-band center is negative. However, it is strong adsorption when the d-band center is positive. A linear relationship between the lithium-ion concentration and adsorption energy is developed for strong adsorption, which can be used to guide the design of high-capacity electrode materials.

[en] Aqueous supercapacitors show advantages of high safely, prolonged lifespan, and low cost, etc. but there have never been commercial market products, nor quantitative investigation of practical pouch devices of aqueous supercapacitors. Herein, to achieve their lab-scale to real-life manufacture, a unique MoO${}_x$ for supercapacitor use is constructed by a hydrothermal and annealing strategy suitable for industrialization, which plays three key functions, including precisely adjusted interlayer spacing, conductive flexible graphite carbon and abundant oxygen vacancies. As a result, the as-synthesized electrode yields an ultra-high specific capacitance of 717 F g${}^{-<!--\; ???\; -->1}$ at 1 A g${}^{-<!--\; ???\; -->1}$ and ultra-long cycling durability with no obvious capacitive loss even after 100 000 cycles. Realistically, the assembled asymmetric supercapacitor (MoO${}_x$-HDA-3//MnO${}_2$) exhibits extraordinary energy density of 78.2 Wh k g${}^{-<!--\; ???\; -->1}$, superior to many advanced supercapacitors reported to date. We have fabricated pouch devices, which can successfully run 3C products such as tablets and smartphones, and maintain stable electrochemical performance even after heavy strikes, fires, and pressures. Quantitative investigation results confirm that the pouch device delivers an excellent specific capacitance of 74.7 F g${}^{-<!--\; ???\; -->1}$ and a high energy density of 41.5 Wh k g${}^{-<!--\; ???\; -->1}$. This work enhances the confidence of pushing aqueous supercapacitors to realistic energy storage market. (© 2022 The Authors. Advanced Functional Materials published by Wiley‐VCH GmbH)

[en] Highlights: • The eXtreme Gradient Boosting Regression (XGBR) algorithm was first applied to build a robust and predictive machine learning (ML) model for perovskite materials. • The method of combining machine learning and DFT calculation used in this article can greatly save computing resources and time, and accelerate the discovery of renewable energy materials. • Two lead-free perovskite structures screened out by the combination of machine learning and DFT calculation have reasonable band gap values, high environmental stability and good optical absorption properties. To accelerate the application of perovskite materials in photovoltaic solar cells, developing novel lead-free perovskite materials with suitable band gaps and high stability is vital. However, laborious experiment and density functional theory (DFT) calculation are time-consuming and incapable to screen promising perovskites rapidly and efficiently. Here, we proposed a novel search strategy combining machine learning and DFT calculation to screen 5,796 inorganic double perovskites. The eXtreme Gradient Boosting Regression (XGBR) algorithm was first applied to build a robust and predictive machine learning (ML) model for perovskite materials. XGBR algorithm yielded a lower mean square error (MSE) than both Artificial Neural Network (ANN) algorithm and Support Vector Regression (SVR) algorithm. From the ML model, two novel lead-free inorganic double perovskites: Na₂MgMnI₆, K₂NaInI₆, were obtained, suitable direct bandgaps of 1.46 eV for K₂NaInI₆ and 1.89 eV for Na₂MgMnI₆, which are similar to the organic–inorganic perovskite (MAPI3) CH₃NH₃PbI₃ (E_g = 1.6 eV), high thermal stability and good optical properties were also confirmed by DFT calculation. The method of combining ML and DFT exhibits high accuracy and significantly speeds up the discovery of promising perovskite materials.