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Inventing the PC details the invention and design of the MCM/70 computer and the prolonged struggle to bring it to market. Zbigniew Stachniak offers an insider's view of events on the front lines of pioneering work on personal computers. He shows what information and options PC pioneers had, how well they understood what they were doing, and how that understanding - or lack thereof - shaped both their engineering ingenuity and the indecisiveness and over-reaching ambition that would ultimately turn a very promising venture into a missed opportunity. Providing comprehensive historical background and rich photographic documentation, Inventing the PC tells the story of a Canadian company on the cutting-edge of the information age.

Mandated to foster a sense of national cohesion The National Film Board of Canada's Still Photography Division was the country's official photographer during the mid-twentieth century. Like the Farm Security Administration and other agencies in the US, the NFB used photographs to serve the nation. Division photographers shot everything from official state functions to images of the routine events of daily life, producing some of the most dynamic photographs of the time, seen by millions of Canadians - and international audiences - in newspapers, magazines, exhibitions, and filmstrips. In The Official Picture, Carol Payne argues that the Still Photography Division played a significant role in Canadian nation-building during WWII and the two decades that followed. Payne examines key images, themes, and periods in the Division's history - including the depiction of women munitions workers, landscape photography in the 1950s and 60s, and portraits of Canadians during the Centennial in 1967 - to demonstrate how abstract concepts of nationhood and citizenship, as well as attitudes toward gender, class, linguistic identity, and conceptions of race were reproduced in photographs. The Official Picture looks closely at the work of many Division photographers from staff members Chris Lund and Gar Lunney during the 1940s and 1950s to the expressive documentary photography of Michel Lambeth, Michael Semak, and Pierre Gaudard, in the 1960s and after. The Division also produced a substantial body of Northern imagery documenting Inuit and Native peoples. Payne details how Inuit groups have turned to the archive in recent years in an effort to reaffirm their own cultural identity. For decades, the Still Photography Division served as the country's image bank, producing a government-endorsed \"official picture\" of Canada. A rich archival study, The Official Picture brings the hisotry of the Division, long overshadowed by the Board's cinematic divisions, to light.

Determining stability, quantifying planned dilution, and estimating the potential dilution associated with hangingwall overbreak are critical in the process of stope design in sublevel open stoping mines. To satisfy these objectives, empirical stability graphs and numerical modelling are currently used in the mining industry. Empirical methods are limited to the database used to calibrate them. In the case of numerical modelling, some of the available criteria used to evaluate hangingwall overbreak do not include the intermediate principal stress around the stope and/or the rock mass geotechnical characteristics. In this study, a new criterion for numerical modelling is proposed to estimate the hangingwall overbreak in open stopes. This new criterion includes the intermediate principal stress around the stope and the rock mass geotechnical characteristics. To develop the criterion, several open stope numerical models are simulated considering different geometrical and geotechnical conditions. The criterion is calibrated to reproduce empirical case histories of hangingwall overbreak. Next, the criterion is verified with case histories of hangingwall overbreak that presented different conditions used to calibrate the criterion. The proposed criterion establish a significant influence and relationship between rock quality and the minimum and intermediate principal stresses on hangingwall overbreak. The criterion offers sufficient flexibility for application to a wide range of geometries, in situ stress conditions, and depth and rock mass properties.

Accurate surrogate models are essential for the application of computational methods such as Markov chain Monte Carlo (McMC) using numerical reservoir simulation. Previous studies have often focused on building surrogates to represent the misfit (or likelihood) function. However, building an accurate constrained surrogate for the likelihood/misfit is difficult for higher dimensions unless an overly large number of samples is simulated first. Fortunately, functional data analysis can provide a set of ensemble-based statistical tools which can be utilized to emulate the full simulation output (timeseries) rather than the misfit function itself. Consequently, the misfit can be easily calculated by the simulated timeseries. In this study, functional principal component analysis (fPCA) is utilized to reduce the dimensionality of the timeseries. In other words, each simulation output (e.g., oil rate) is represented in terms of a few optimal functional principal component scores (fPCS), which can be readily inverted to reconstruct the original timeseries. fPCA is employed herein to develop a new and efficient Bayesian history-matching workflow in that it is used to iteratively update the fPCA-based local timeseries surrogates and search for the extrema of the likelihood function. In this proposed process, a few initial random samples are generated, and the corresponding timeseries are processed by fPCA. The resulting fPCSs for each simulation output are modelled individually using random variable proxies such as kriging or random forest to relate the uncertain variables to projected scores. These proxies are then used to calculate the likelihood and optimized to suggest the next best samples until a convergence criterion is met. This workflow is applied to a set of data obtained from a near-critical gas condensate well from a Canadian shale reservoir. The proposed history-matching workflow results in a nearly 8-times faster average convergence rate compared to other population-based algorithms studied. The history-matching samples are combined with additional adaptive samples to enhance the exploration aspect and predictability of the local surrogate models. This leads to an improvement of the local timeseries proxies for different outputs (from history-matching) and helps to generate global timeseries proxies that can be used for the entire parameter space. The results demonstrate that clustering the timeseries and applying fPCA to each cluster separately can significantly improve the accuracy of the global surrogates. Finally, the global surrogates are utilized to obtain accurate posterior distributions quickly through an McMC algorithm. This study introduces an adaptive sampling method for the first time that can be used to generate a highly accurate surrogate for timeseries and conduct the optimization efficiently.

Before implementing an HNP pilot in the field, reservoir studies are usually conducted, and compositional numerical simulations performed to assess the impact of uncertainty on HNP design parameters. In a previous study by the authors, the effect of parametric uncertainty on designing a single-well HNP was demonstrated using effective single-porosity models. However, recent studies have shown that a limited region of complex fracturing is likely to be created during the hydraulic fracturing process. In this study, we expand on the earlier work and address the impact of model uncertainty on designing an optimal HNP for a Duvernay shale example. In particular, the complex fracture regions are represented by local dual-porosity dual-permeability (DP-DK) models near the primary hydraulic fractures. Further, a multi-well HNP design is utilized to assess the impact of fracture communication during the cyclic gas injection scenarios. A unified framework is required to conduct Bayesian history matching and perform HNP simulations using the Markov chain Monte Carlo process. This task is achieved by implementing new adaptive sampling designs and employing some surrogate modeling techniques (Gaussian processes) to obtain the distributions for probabilistic HNP forecasts. The simulation results demonstrate that, for an equivalent calibrated DP-DK model, the efficiency of HNP, for both lean and rich gas injection scenarios, can be substantially higher than that predicted with the calibrated single-porosity model. In particular, lean gas injection, projected to have a low efficiency using single porosity models, is predicted to result in substantial incremental recovery in DP-DK models. The history matching and HNP simulation results demonstrate that DP-DK models provide the highest efficiency during early cycles with a reduced performance for later cycles. For single porosity models, the efficiency is much lower than the DP-DK models and is relatively constant across most of the cycles. The high efficiency of the DP-DK models is related to an enhanced mixing and extraction process due to pervasive communication (contact area) between the fracture network and the matrix. Additionally, the compositional simulations demonstrate that hydraulic communication between nearby wells through primary hydraulic fractures can substantially reduce the HNP performance. This study provides a novel workflow to accurately assess the impact of model uncertainty on HNP design for unconventional shale and low-permeability light oil reservoirs.

The process of reservoir history-matching is a costly task. Many available history-matching algorithms either fail to perform such a task or they require a large number of simulation runs. To overcome such struggles, we apply the Gaussian Process (GP) modeling technique to approximate the costly objective functions and to expedite finding the global optima. A GP model is a proxy, which is employed to model the input-output relationships by assuming a multi-Gaussian distribution on the output values. An infill criterion is used in conjunction with a GP model to help sequentially add the samples with potentially lower outputs. The IC fault model is used to compare the efficiency of GP-based optimization method with other typical optimization methods for minimizing the objective function. In this paper, we present the applicability of using a GP modeling approach for reservoir history-matching problems, which is exemplified by numerical analysis of production data from a horizontal multi-stage fractured tight gas condensate well. The results for the case that is studied here show a quick convergence to the lowest objective values in less than 100 simulations for this 20-dimensional problem. This amounts to an almost 10 times faster performance compared to the Differential Evolution (DE) algorithm that is also known to be a powerful optimization technique. The sensitivities are conducted to explain the performance of the GP-based optimization technique with various correlation functions.

The brown marmorated stink bug, Halyomorpha halys Stål (Hemiptera: Pentatomidae), is an invasive pest that has expanded its range outside of its original confinements in Eastern Asia, spreading through the United States, Canada and most of the European and Eurasian countries. The invasiveness of this agricultural and public nuisance pest is facilitated by the availability of an array of suitable hosts, an r-selected life history and the release from natural enemies in the invaded zones. Traditional monitoring methods are usually impeded by the lack of time and resources to sufficiently cover large geographical ranges. Therefore, the citizen science initiative “BugMap” was conceived to complement and assist researchers in breaking down the behavior of this invasive pest via a user-friendly, freely available mobile application. The collected data were employed to forecast its predicted distribution and to identify the areas at risk in Trentino, Northern Italy. Moreover, they permitted the uncovering of the seasonal invasion dynamics of this insect, besides providing insight into its phenological patterns, life cycle and potential management methods. Hence, the outcomes of this work emphasize the need to further integrate citizens in scientific endeavors to resolve ecological complications and reduce the gap between the public and science.

This paper presents the application of genetic algorithm (GA) to the history-matching problem. As history matching of VAPEX (vapour extraction) experiments is a complex, highly nonlinear, and non-unique inverse problem, a modified GA was developed to assist the history-matching process. Compared to conventional GA, the computational time in this modified GA approach was reduced by 71 %, and an excellent match between the simulation data and experimental data was achieved, with the error being less than 1 %. This study is focussed on automatic history matching of the VAPEX heavy oil recovery process.

This paper evaluates the application of geothermal energy by numerically modeling the heat extraction that would result from the injection of cold water into an artificially fractured hot dry rock (HDR). The HDR that would be utilized in Alberta is expected to be granite with a network of pre-existing natural fractures. However, to ensure a continued flow of injected water from the reservoir to the production wells, creation of additional fractures is required. Thus, the properties of these fractures are of prime importance to the efficiency of geothermal energy production. The fracture networks for the simulations were created using a numerical code and were converted into a grid format to be used in a commercial thermal simulator. A new approach to embed a complex fracture system into the numerical model was applied. Various properties of the fractures such as aperture, length, and spacing were changed and their absolute and relative effects on energy production were quantified and the results are presented in this paper. This modeling technique was also verified by comparison with the conventional dual porosity model and by performing a history match with real field data obtained from literature. The applicability of this approach to provide heat for oil sands extraction was investigated using the volumes of water currently needed in northern Alberta. Based on these constraints, numerical simulations were run to evaluate the optimum well spacing that would be required using a three-well configuration. In this simulation, the fracture parameters (density and aperture) were kept fixed assuming that they are not affected by cold water injection. The results of this study suggest that geothermal energy has a potential to be a sustainable form of thermal energy for oil sands extraction in northern Alberta.