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In this paper, we introduce a method of generating bootstrap samples with unknown patterns of cross‐ sectional/spatial dependence, which we call the spatial dependent wild bootstrap. This method is a spatial counterpart to the wild dependent bootstrap of Shao (2010) and generates data by multiplying a vector of independently and identically distributed external variables by the eigendecomposition of a bootstrap kernel. We prove the validity of our method for studentized and unstudentized statistics under a linear array representation of the data. Simulation experiments document the potential for improved inference with our approach. We illustrate our method in a firm‐level regression application investigating the relationship between firms' sales growth and the import activity in their local markets using unique firm‐level and imports data for Canada. [ABSTRACT FROM AUTHOR]

This study developed a production-forecasting model to replace the numerical simulation and the decline curve analysis using reservoir and hydraulic fracture data in Montney shale gas reservoir, Canada. A shale-gas production curve can be generated if some of the decline parameters such as a peak rate, a decline rate, and a decline exponent are properly estimated based on reservoir and hydraulic fracturing parameters. The production-forecasting model was developed to estimate five decline parameters of a modified hyperbolic decline by using significant reservoir and hydraulic fracture parameters which are derived through the simulation experiments designed by design of experiments and statistical analysis: (1) initial peak rate ( P hyp ), (2) hyperbolic decline rate ( D hyp ), (3) hyperbolic decline exponent ( b hyp ), (4) transition time ( T transition ), and (5) exponential decline rate ( D exp ). Total eight reservoir and hydraulic fracture parameters were selected as significant parameters on five decline parameters from the results of multivariate analysis of variance among 11 reservoir and hydraulic fracture parameters. The models based on the significant parameters had high predicted R 2 values on the cumulative production. The validation results on the 1-, 5-, 10-, and 30-year cumulative production data obtained by the simulation showed a good agreement: R 2 > 0.89. The developed production-forecasting model can be also applied for the history matching. The mean absolute percentage error on history matching was 5.28% and 6.23% for the forecasting model and numerical simulator, respectively. Therefore, the results from this study can be applied to substitute numerical simulations for the shale reservoirs which have similar properties with the Montney shale gas reservoir. [ABSTRACT FROM AUTHOR]

Objective: To assess the prevalence of partially hydrogenated oils (PHO), hydrogenated oils (HO) and/or both in Canadian packaged foods in 2013 and 2017 and to determine the mean trans-fatty acid (TFA) content of products declaring such oils.Design: Repeated cross-sectional study of the Food Label Information Program.Setting: Food labels (n 32 875) were collected from top Canadian grocery retailers in 2013 and 2017. Proportions of products declaring PHO, HO and/or both in the Ingredients List were calculated by year and food category. The percentage contribution of TFA (g) to total fat (g) was calculated and compared against the voluntary TFA limits, defined as <2 % of total fat content for fats and oils, and <5 % for all other foods. Foods exceeding limits were identified. The mean TFA content (in g/serving and per 100 g) was calculated for products with these oils.Results: The use of PHO, HO and/or both significantly decreased in Canadian foods from 2013 to 2017 (0·8 to 0·2 %, 5 to 2·4 % and 5·7 to 2·6 %, respectively, for PHO, HO and/or both). The mean TFA content of products containing PHO increased (0·34 to 0·57 g TFA/serving); although it was not statistically significant, it is still concerning that TFA content increased. The TFA content significantly decreased in foods with HO (0·24 to 0·16 g TFA/serving, P < 0·05) during 2013-2017.Conclusions: Products with PHO continue to be present in the Canadian marketplace, despite voluntary efforts to eliminate them. Products with HO should also be monitored, as they can also contribute to TFA content in foods. [ABSTRACT FROM AUTHOR]

This study investigates the potential of a novel CO 2 -carbonated water-alternating-gas (CWAG) injection method for enhanced oil recovery (EOR) and geological carbon storage. The Weyburn fluid data acquired from Canada are used in a compositional reservoir simulation of a CO 2 -CWAG case study with seven cycles in order to analyze the effects of carbonated water (CW) upon the oil recovery and CO 2 storage capacity of a multi-phase CO 2 /brine/oil system. The study includes an assessment of the CO 2 plume propagation and retention via structural, residual, and solubility trapping mechanisms. The numerical results demonstrate that CO 2 -CWAG yields a 6.7% increase in oil recovery compared to continuous CO 2 flooding (from 68.4% to 73.0%) and a 1.4% increase compared to CO 2 -WAG (from 72.0% to 73.0%). During the CO 2 -CWAG operation, increasing the molarity of CO 2 from 0.0 mol/L to 1.2 mol/L in carbonated water results in a 6.7% increase in the amount of geologically stored CO 2 (from 6191 tons to 6608 tons). In brief, CO 2 -CWAG improves not only oil recovery but also CO 2 storage capacity in terms of the three trapping mechanisms, thus making it a promising low-carbon EOR method that could help reduce CO 2 emissions in the oil and gas industry. • This study proposes a novel CO 2 -carbonated water-alternating-gas (CWAG) injection method for enhanced oil recovery and geological carbon storage. • Compositional simulations based on the Weyburn W3 fluid model are conducted for the CO 2 -CWAG process composed of 3-year water flooding followed by 14-year WAG with 7 cycles (17 years in total). • For oil recovery and the total amount of geologically stored CO 2 , CO 2 -CWAG is more efficient than other improved oil recovery methods, such as water flooding, CO 2 flooding, and CO 2 -WAG, because CW facilitates oil swelling and decreases oil mass density while the cyclic approach of CWAG relieves gravity segregation in carbonated water flooding. • The results highlight the efficacy of CO 2 -CWAG for improving oil recovery and geological carbon storage simultaneously, thereby contributing to the goal of carbon neutrality. [ABSTRACT FROM AUTHOR]

The shale barrier (SB) tends to impede the steam chamber development because of the low permeability of shale during the steam-assisted gravity drainage (SAGD) process. The interpretation of 4-D seismic data in geostatistical reservoir modelling is traditionally used to understand better the uncertainty of distribution of SB. On the other hand, long-term 4-D seismic survey to monitor the steam chamber development incurs additional cost. In this study, machine learning (ML) approaches, such as multiple linear regression (MLR), Random Forest, and artificial neural network (ANN), were performed to predict the horizontal location, width, and length of the SB using the inflection points of the SAGD production data; oil rate, cumulative oil, steam-oil ratio, and cumulative steam-oil ratio and the reservoir and operating parameters; reservoir thickness, horizontal permeability, vertical-horizontal permeability ratio, oil saturation, and steam injection pressure without the application of 4-D seismic data. Data pre-processing was carried out to examine and select 38 parameters (5 reservoir and operating parameters, the vertical location and thickness of the SB, and 31 inflection points), and the dataset (2131 cases) was divided into 75% of the train set and 25% of the test set. The k-fold cross validation and grid search process for hyperparameter tuning were performed to optimize the ML models. A comparison of the ML models revealed the ANN models to have larger coefficient of determination (R2) values than those of the MLR and Random Forest models. In the optimal ANN model, the R2 values of the test set for predicting the width (0.869) and length (0.918), but the R2 value for predicting the horizontal location was as low as 0.569. For verification, the optimal ANN model was tested using the production data from Husky's Sunrise and Suncor's Firebag SAGD projects in Canada. The field production data and simulation results using the reconstructed SB showed a similar trend, and the similarity between the field data and simulation results was improved compared to the results reported by Kim and Shin (2018). • Machine learning (ML) approaches were performed to predict the shale barrier size and spatial location using the SAGD production data. • Hyperparameter tuning was performed using the k-fold cross validation and grid search process to optimize the ML models. • Optimal ML model was tested using the production data from Husky's Sunrise and Suncor's Firebag SAGD projects in Canada. [ABSTRACT FROM AUTHOR]

Abstract: In Canada, perfluoroalkyl acids (PFAAs) have been the focus of several monitoring programs and research and surveillance studies. Here, we integrate recent data and perform a multi-media assessment to examine the current status and ongoing trends of PFAAs in Canada. Concentrations of perfluorooctane sulfonate (PFOS), perfluorooctanoate (PFOA), and other long-chain perfluorocarboxylates (PFCAs) in air, water, sediment, fish, and birds across Canada are generally related to urbanization, with elevated concentrations observed around cities, especially in southern Ontario. PFOS levels in water, fish tissue, and bird eggs were below their respective Draft Federal Environmental Quality Guidelines, suggesting there is low potential for adverse effects to the environment/organisms examined. However, PFOS in fish and bird eggs tended to exceed guidelines for the protection of mammalian and avian consumers, suggesting a potential risk to their wildlife predators, although wildlife population health assessments are needed to determine whether negative impacts are actually occurring. Long-term temporal trends of PFOS in suspended sediment, sediment cores, Lake Trout (Salvelinus namaycush), and Herring Gull (Larus argentatus) eggs collected from Lake Ontario increased consistently from the start of data collection until the 1990s. However, after this time, the trends varied by media, with concentrations stabilizing in Lake Trout and Herring Gull eggs, and decreasing and increasing in suspended sediment and the sediment cores, respectively. For PFCAs, concentrations in suspended sediment, sediment cores, and Herring Gulls generally increased from the start of data collection until present and concentrations in Lake Trout increased until the late 1990s and subsequently stabilized. A multimedia comparison of PFAA profiles provided evidence that unexpected patterns in biota of some of the lakes were due to unique source patterns rather than internal lake processes. High concentrations of PFAAs in the leachate and air of landfill sites, in the wastewater influent/effluent, biosolids, and air at wastewater treatment plants, and in indoor air and dust highlight the waste sector and current-use products (used primarily indoors) as ongoing sources of PFAAs to the Canadian environment. The results of this study demonstrate the utility of integrating data from different media. Simultaneous evaluation of spatial and temporal trends in multiple media allows inferences that would be impossible with data on only one medium. As such, more co-ordination among monitoring sites for different media is suggested for future sampling, especially at the northern sites. We emphasize the importance of continued monitoring of multiple-media for determining future responses of environmental PFAA concentrations to voluntary and regulatory actions. [Copyright &y& Elsevier]