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Summary Field development planning and economic analysis require reliable forecasting of bitumen production. Forecasting at the field level may be done using reservoir simulations, type curve analysis, and other (semi-)analytical techniques. Performing reservoir simulation is usually computationally expensive, and the nonuniqueness of a history-matched solution leads to uncertainty in the model predictions and production forecasts. Analytical proxies, such as Butler’s model and its various improvements, allow for sensitivity studies on input parameters and forecasting under multiple operational scenarios and geostatistical realizations to be conducted rather quickly, despite being less accurate than reservoir simulation. Similar to their reservoir simulation counterparts, proxy models can also be tuned or updated as more data are obtained. Type curves also facilitate efficient reservoir performance prediction; however, in practice, the performance of many steam-assisted gravity drainage (SAGD) well pairs tends to deviate from a set of predefined type curves. Historical well data is a digital asset that can be utilized to develop machine learning (ML) or data-driven models for production forecasting. These models involve lower computational effort than numerical simulators and can offer better accuracy compared to proxy models based on Butler’s equation. Furthermore, these data-driven models can be used for automated optimization, quantification of geological uncertainties, and “What If” scenario analysis due to their lower computational cost. This paper presents a novel ML workflow that includes a predictive model development using the random forest algorithm, clustering (to group well pairs by geological properties), Bayesian updating, and Monte Carlo sampling (for uncertainty quatification) for the forecasting of real-world SAGD injection and production data. The training data set consists of field data from 152 well pairs, including approximately 3 years of operational data. Each well pair’s data set involves data that are typically available for an SAGD well pair (e.g., operational data, geological, and well design parameters). This ML workflow can update predictions in real time and be applied for quantifying the uncertainties associated with the forecasts, making it an important step for development planning. To the best of the author’s knowledge, this is the first time ML algorithms have been applied to an SAGD field data set of this size.

Injection or co-injection of solvents has been proposed as an in-situ recovery mechanism for bitumen (Gupta et al., 2002). Solvent-based recovery schemes have several potential advantages over steam-based schemes such as SAGD. These benefits include lower GHG emissions and water requirements, lower capital intensity, and lower operating costs. Additionally, solvent-based recovery schemes have been proposed to improve the quality of produced bitumen (Jossy et al., 2008). The primary mechanism for in-situ upgrading during a solvent injection process is solvent de-asphalting. Solvent de-asphalting is a proven commercial process for processing and upgrading oil in surface facilities such as refineries and upgraders. Several laboratory studies have shown that solvent de-asphalting can also occur in situ, resulting in API improvement in the produced bitumen (AITF report, 2017 and 2018, Brons and Yu, 1995). In this work the economic benefits of upgrading bitumen in-situ will be studied. It will be shown that, at a given price environment, there exists an optimum level of upgrading (as measured by °API). Generally, too little upgrading reduces the value of bitumen due to blending and transportation costs, while too much upgrading reduces bitumen yield and ultimate recovery.

Field development planning and economic analysis require reliable forecasting of bitumen production. Forecasting at the field level may be done using reservoir simulations, type-curve analysis, and other (semi-)analytical techniques. Performing reservoir simulation is usually computationally expensive and the non-uniqueness of a history-matched solution leads to uncertainty in the model predictions and production forecasts. Analytical proxies, such as Butler's model and its various improvements, allow for sensitivity studies on input parameters and forecasting under multiple operational scenarios and geostatistical realizations to be conducted rather quickly, despite being less accurate than reservoir simulation. Similar to their reservoir simulation counterparts, proxy models can also be tuned or updated as more data are obtained. Type curves also facilitate efficient reservoir performance prediction; however, in practice, the performance of many SAGD well-pairs tends to deviate from a set of pre-defined type curves. Historical well data is a digital asset that can be utilized to develop machine learning or data-driven models for the purpose of production forecasting. These models involve lower computational effort compared to numerical simulators and can offer better accuracy compared to proxy models based on Butler's equation. Furthermore, these data-driven models can be used for automated optimization, quantification of geological uncertainties, and "What If" scenario analysis due to its lower computational cost. This paper presents a novel machine learning workflow that includes a predictive model development using the random forest algorithm, clustering (to group wells by geological properties), Bayesian updating, Monte Carlo sampling (for uncertainty quatification), and genetic algorithm for the forecasting of real-world SAGD injection and production data, and optimization. The training dataset involves field data that is typically available for a SAGD well-pair (e.g., operational data, geological, and well design parameters). Just as importantly, this machine learning workflow can update predictions in real-time, be applied for the quantification of the uncertainties associated with the forecasts, and optimize steam allocation, making it an important step for development planning and field-wide optimization. To the best of the author's knowledge, this is the first time that machine learning algorithms have been applied to a SAGD data set of this size.

In SAGD operations, the steam chamber temperature can be as high as 250°C. Foam formulations that can withstand such high temperature are required for field deployment of foam as conformance control technique. An evaluation of 12 surfactant formulations for their viability as steam foam candidate at chamber temperatures as high as 250°C is presented. Unlike other studies in the literature that used nitrogen for foam generation, methane was employed in this project. This project reveals that strong foam can be formed at 250°C. Also, foam is only generated within a specific range of methane mass quality. Stronger foam was formed when steam and methane were injected simultaneously into the core. Lastly, the existence of residual oil in the core reduced the foam strength. We analyzed the effect of a few parameters on foam strength. Parameters considered include concentration of surfactant, presence of steam as well as methane, mass quality of methane, and residual oil saturation. Laboratory analyses comprising thermal stability, solubility, foam height, adsorption, and coreflood, were conducted. The learnings from this project has the ability to accelerate the application of foam systems in high-temperature SAGD operations to increase conformance, maximize steam usage, and ultimately lead to overall reduction in GHG emissions from SAGD projects.

Hydroxydicyclopentadiene (DCPD-OH) and some ester and ether derivatives were synthesized and used for the first time as ring-opening metathesis polymerization (ROMP) monomers to create cross-linked thermoset polymers with Ru-catalysts. The properties of this new family of polymers were measured mainly by means of TGA, DSC and DMA to compare them with those of polydicylopentadiene (pDCPD). The novel polymers showed thermal properties comparable to those of pDCPD; although glass transition temperatures were mainly reduced by the modifications. More importantly, tuneable physical properties, depending on the substituents attached, were obtained. Thus, a window towards many important applications is opened by making novel thermoset ROMP homopolymers and copolymers from monomers with significantly reduced odour.

Solvent Aided Process (SAP) combines benefit of using steam with solvents and has the potential to substantially improve SAGD performance with lower energy intensity and impact on the environment. Cenovus has been developing a SAP since 1996 and has conducted a few successful pilots (Senlac 2002, Christina Lake A0101 2004-2005, Christina Lake A0202 2009-2016). Cenovus has planned to implement a SAP in the Narrows Lake development when that project is sanctioned. The general practice for SAP is to co-inject solvent with steam after the SAGD peak rate is reached as steam is most effective for the vertical growth of steam chamber. However, by injecting solvent during the rise rate phase, there could be a large facility saving by not requiring an additional solvent line to each wellhead. Rather, solvent can be added to the steam header immediately after steam leaves the steam generation plant and distributed to all wells, regardless of their vintage. To de-risk the addition of solvent at the steam header, Cenovus implemented an early rise rate phase SAP pilot at Christina Lake A0201 well pair. The objective of this field pilot was to investigate the effects of butane injection during the SAGD early rise rate phase. This paper describes the implementation and results of the Christina Lake A0201 early rise rate SAP pilot. Presented in this paper are the results of this field test showing that no adverse effects on SAGD performance were observed when injecting solvent during the rise rate phase. This paper also shows the history match of field production data using CMG CMOST simulation. The findings of this investigation add to the knowledge base of information related to the optimal solvent injection timing in a SAP process. Insights into performing SAP history matches are also presented based on the simulation study that was undertaken.

Dynamic stall was controlled on a double-bladed H-Rotor vertical axis wind turbine model using pulsed dielectric barrier discharge plasma actuators in a feed-forward control configuration. The azimuthal angles of plasma actuation initiation and termination, that produced the largest increases in power, were determined parametrically on the upstream half of the turbine azimuth in a low-speed blow-down wind tunnel at wind speeds of 7m/s. A mathematical model, together with instantaneous turbine speed, was used to estimate transient torque and power developed by the turbine under the influence of plasma actuation. Overall performance improvements were based on changes between the final actuated and initial baseline results. A remarkable result of this investigation was that a net turbine power increase of 10% was measured. This was achieved by systematically reducing plasma pulsation duty cycles as well as the plasma initiation and termination angles. Nevertheless, it was determined that further performance increases could be achieved by changing the actuator’s dielectric material, increasing the turbine radius and developing a method for control of dynamic stall on both the upwind (inboard of the blades) and downwind (outboard of the blades) halves of the turbine azimuth. Copyright © 2014 John Wiley & Sons, Ltd.

Dynamic stall was controlled on the blades of a small high-solidity vertical axis wind turbine, by means of dielectric barrier discharge plasma actuators installed on the blade leading-edges. A parametric study was conducted with the objective of increasing the net power output resulting from control in an open-loop manner. For the majority of experiments, the actuators were configured to control separation on the upwind half of the turbine azimuth while limited experiments were conducted on the downwind half. Turbine power was measured using a specially-designed dynamometer that allowed full characterization of its performance. Actuator duty cycle dependence observed previously on static airfoils was also observed on the turbine. However, optimum reduced frequencies showed substantially different dependence and this was traced to the importance of the plasma pulsation frequency relative to the turbine rotational frequency. Overall turbine power performance improvements of 38% and 20% were measured for upwind and downwind dynamic stall control, respectively. Based on the data acquired, up-scaling the turbine by a factor of 5 and 10, the percentage of plasma power required to produce comparable improvements was conservatively estimated at 3.3% and 1.7% respectively. A new switching-direction actuator was developed, together with high-speed plasma triggering, in order to produce combined upwind and downwind actuation that is phase-locked to turbine rotational frequencies. Flowfield measurements using particle image velocimetry are presently being performed.

OBJECTIVE: To assess the role of point-of-care (PoC) assessment of C-reactive protein (CRP) and white blood cell (WBC) count to identify bacterial illness in Tanzanian children with non-severe non-malarial fever. METHODS: From the outpatient department of a district hospital in Tanzania, 428 patients between 3 months and 5 years of age who presented with fever and a negative malaria test were enrolled. All had a physical examination and bacterial cultures from blood and urine. Haemoglobin, CRP and WBC were measured by PoC devices. RESULTS: Positive blood cultures were detected in 6/428 (1.4%) children and urine cultures were positive in 24/401 (6.0%). Mean WBC was similar in children with or without bacterial illness (14.0 × 109 , 95% CI 12.0-16.0 × 109 vs. 12.0 × 109 , 95% CI 11.4-12.7 × 109), while mean CRP was higher in children with bacterial illness (41.0 mg/l, 95% CI 28.3-53.6 vs. 23.8 mg/l, 95% CI 17.8-27.8). In ROC analysis, the optimum cut-off value for CRP to identify bacterial illness was 19 mg/l but with an area under the curve of only 0.62. Negative predictive values exceeded 80%, while positive predictive values were under 40%. CONCLUSION: WBC and CRP levels had limited value in identifying children with bacterial infections. The positive predictive values for both tests were too low to be used as single tools for treatment decisions.

Introduction: A significant percent of dental implants tends to lose bone support during the years. Gravity implants were designed to improve the mechanical bone adaptation in an attempt to overcome the problem. The present manuscript is a retrospective observation of 1 year of 25 single-loaded Gravity implants. Materials and Methods: This was a retrospective study of files of patients who completed their treatment and were loaded for 1 year. The measurement of changes in the bone level was done according to the X-ray documentations that were found in the files of patients treated in the clinic of Kibutz Bror Chail. The schedule of dental implants' follow-up in this clinic includes parallel periapical X-ray films at the stage of implant installation, the stage of loading, and at the end of a year of loading. A Student's t-test was used to compare the bone level at the stage of loading and after 1 year of loading. Results: Twenty-five cases of one single-loaded implant per patient were included in the study. It was shown that most of the bone loss that was documented at the end of the healing phase was reconstructed (72%) during 1 year of loading. Conclusions: According to this retrospective study, it can be concluded that implant design that enables bone loading by compressive stress may result in the reconstruction of the initial loss.

A two-level full factorial design and/or a central composite design are used to optimize the operation of a distillation column by manipulating three variables: the feed flow rate, the reflux ratio and the steam flow to the reboiler. The response variable of interest is a profit function where the objective of this experiment is to examine the effect of each of the three factors and their interactions on this profit function. This is accomplished in a sieve tray type continuous distillation column operated at ambient pressure to separate a methanol–isopropanol mixture. Students were asked to determine the significant variables and the optimal operating conditions for steam rate, reflux ratio and feed rate such that the profit is maximized. As a result, this experiment not only exposes students to an industrially relevant unit operation but it also helps complement the classroom teaching on design of experiment concepts.

The population of Salmonella enterica serovar Typhi (S. Typhi), the causative agent of typhoid fever, exhibits limited DNA sequence variation, which complicates efforts to rationally discriminate individual isolates. Here we utilize data from whole-genome sequences (WGS) of nearly 2,000 isolates sourced from over 60 countries to generate a robust genotyping scheme that is phylogenetically informative and compatible with a range of assays. These data show that, with the exception of the rapidly disseminating H58 subclade (now designated genotype 4.3.1), the global S. Typhi population is highly structured and includes dozens of subclades that display geographical restriction. The genotyping approach presented here can be used to interrogate local S. Typhi populations and help identify recent introductions of S. Typhi into new or previously endemic locations, providing information on their likely geographical source. This approach can be used to classify clinical isolates and provides a universal framework for further experimental investigations., Typhoid fever is caused by Salmonella enterica serovar Typhi (S. Typhi). This study examines ∼2,000 clinical isolates of S. Typhi to show highly structured/geographically restricted genomes except rapidly disseminating H58 subclade, and design a genotyping framework for tracking the disease.

Fast and accurate determination of lipid content in microalgal cultures is a required step for algal bioprocess development and optimization. An improved high-throughput Nile Red staining protocol using a microplate fluorescence reader is proposed, resulting in an increased correlation coefficient and a reduction in the relative standard deviation of 84% when compared to previously reported staining protocols. Differences in the staining efficacy of Nile Red among different algal strains were observed, even though the performance of the proposed method was found to be satisfactory for the different Trebouxiophyceae and Chlorophyceae microalgal cultures evaluated. Therefore, the proposed Nile Red method should only be used for evaluating lipid content variations in previously standardized strains. BODIPY 505/515 was evaluated as a potential substitute for Nile Red, but was found to be unsuitable as a quantitative stain for algal lipids in the microplate-based staining technique.

A dynamic systems model was used to generate parameters describing a phenotype of Hypothalamic-Pituitary-Adrenal (HPA) behavior in a sample of 36 patients with chronic fatigue syndrome (CFS) and/or fibromyalgia (FM) and 36 case-matched healthy controls. Altered neuroendocrine function, particularly in relation to somatic symptoms and poor sleep quality, may contribute to the pathophysiology of these disorders. Blood plasma was assayed for cortisol and ACTH every 10 min for 24h. The dynamic model was specified with an ordinary differential equation using three parameters: (1) ACTH-adrenal signaling, (2) inhibitory feedback, and (3) non-ACTH influences. The model was "personalized" by estimating an individualized set of parameters from each participant's data. Day and nighttime parameters were assessed separately. Two nocturnal parameters (ACTH-adrenal signaling and inhibitory feedback) significantly differentiated the two patient subgroups ("fatigue-predominant" patients with CFS only versus "pain-predominant" patients with FM and comorbid chronic fatigue) from controls (all p's

In this work, Raman spectroscopy and a machine learning technique known as support vector regression (SVR) are used for building an online sensor to monitor the heterotrophic algal culture conditions in a computer-interfaced bench-scale microalgal bioreactor system, for the production of bio-oil. Monitoring of process conditions in algal cultures is required to enable the use of different control strategies to maximize oil productivity. In order to correlate the Raman spectra with culture conditions, three independent experimental datasets are used. The effect of several preprocessing techniques, including Savitzky–Golay filtering, baseline correction, and standard normal variate transformation, on the goodness of fit is evaluated. A multivariate sensor for real time online monitoring of the concentrations of biomass, glucose and percentage oil content is successfully built and validated. The advantages of using the proposed real-time on-line sensor are illustrated in an experimental microalgal bioreactor system.

A small high-solidity vertical axis wind turbine, with dielectric barrier discharge plasma actuators installed on the blade leading-edges, was built and tested in a low-speed wind tunnel. The main objectives were to assess turbine performance enhancements resulting from the control of dynamic flow separation and project the viability of up-scaling the turbine. The actuators employed were configured to control separation only on the upstream half of the turbine azimuth. Turbine power was measured using a specially designed dynamometer that allowed full characterization of its performance. A parametric study showed that actuator duty cycle dependence observed previously on static airfoils was also observed on the turbine. Optimum reduced frequencies, however, showed substantially different dependence and this was traced to the importance of the plasma pulsation frequency relative to the turbine rotational frequency. When considering overall performance improvements, increases in turbine power of up to 38% were measured. Based on the data acquired, up-scaling the turbine by a factor of 5 and 10, the percentage of plasma power required to produce comparable improvements was conservatively estimated at 3.3% and 1.7% respectively. Further work will address phase-locking the plasma pulsation and turbine frequencies, assessing the performance benefits of downwind actuation, employing combined upwind and downwind actuation and measuring the controlled flowfield using particle image velocimetry.

OBJECTIVE: In sub-Saharan Africa, the use of malaria rapid diagnostic tests (mRDT) has raised awareness of alternative fever causes in children but few studies have included adults. To address this gap, we conducted a study of mRDT-negative fever aetiologies among children and adults in Tanzania. METHODS: A total of 1028 patients aged 3 months to 50 years with a febrile illness and negative mRDT were enrolled from a Tanzanian hospital outpatient department. All had a physical examination and cultures from blood, nasopharynx/throat and urine. Patients were followed on Days 7 and 14 and children meeting WHO criteria for pneumonia were followed on Day 2 with chest radiology. RESULTS: Respiratory symptoms were the most frequent presenting complaint, reported by 20.3% of adults and 64.0% (339/530) of children. Of 38 X-rayed children meeting WHO pneumonia criteria, 47.4% had a normal X-ray. Overall, only 1.3% of 1028 blood cultures were positive. Salmonella typhi was the most prevalent pathogen isolated (7/13, 53.8%) and S. typhi patients reported fever for a median of 7 days (range 2-14). Children with bacteraemia did not present with WHO symptoms requiring antibiotic treatment. Young children and adults had similar prevalences of positive urine cultures (24/428 and 29/498, respectively). CONCLUSION: Few outpatient fevers are caused by blood stream bacterial infection, and most adult bacteraemia would be identified by current clinical guidelines although paediatric bacteraemia may be more difficult to diagnose. While pneumonia may be overdiagnosed, urinary tract infection was relatively common. Our results emphasise the difficulty in identifying African children in need of antibiotics among the majority who do not.

The synthesis of novel sulfur-chelated ruthenium benzylidenes afforded latent catalysts with a wider range of activities and new isomeric forms. A ruthenium complex with a tridentate ligand displayed latency for even one of the most reactive ROMP monomers, dicyclopentadiene, while a room temperature latent trifluoromethyl-substituted thioether derivative was shown to be the most active sulfur-chelated precatalyst to date in several metathesis reactions at higher temperatures. These new complexes widen the spectrum of activity for this family of catalysts, enabling several practical applications and enhancing the understanding for the mechanisms of activation in strongly chelated ruthenium alkylidenes.

Biological processes are often characterised by significant nonlinearities, noisy measurements and hidden process variables. The dynamic behaviour of such processes can be represented by stochastic differential equations obtained from physical laws. We propose a Bayesian algorithm for parameter estimation in stochastic nonlinear biological processes with unmeasured (or hidden) variables. The proposed algorithm, involves drawing random samples iteratively from a posterior density functions of the parameters and the hidden variables. A Bayesian sampling techniques is used to approximate these posterior density functions. Both Metropolis–Hastings algorithm and Gibbs sampling are used for sample generation. The algorithm is extended to handle multiple data sets and missing observations. The algorithm is applied to an experimental data set collected from an algal bioreactor system. © 2011 Canadian Society for Chemical Engineering

Microalgal feedstocks have shown potential for the production of biofuels and fine chemicals. Recently, an optimal experimental input profile for the identification of parameters of a microalgal bioreactor, containing 6 states and 12 unknown parameters has been proposed. In this work, the proposed design is implemented and parameters are estimated. It was found that the parameter estimation procedure can be made more computational efficient by the use of a novel iterative non-linear model reparameterization algorithm. By applying the proposed algorithm to experimental data, a good degree of output prediction was achieved along with bounds on the parameter values. The final, validated, model can be used for optimal control and process simulation.

Microalgal bioprocesses are of increasing interest due to the possibility of producing fine chemicals, pharmaceuticals, and biofuels. In this work, the parameter estimability of a first principles ODE model of a microalgal bioreactor, containing 6 states and 12 unknown parameters, is investigated. For this purpose, the system input trajectories are computed using the D-optimality criterion. Even by using a D-optimal input, not all parameters were found to have a significant effect on model predictions. Linear and non-linear transformations are used to partition the parameter space into estimable and inestimable subspaces. For the linear re-parameterization, a set of four directions in the twelve dimensional parameter space, along which a significant change in the output occurs, is identified using singular value decomposition of the parameter covariance matrix. The non-linear re-parameterization utilizes the three system rate functions as pseudo-outputs in order to perform a non-linear transformation which reduces the dimension of the parameter space from twelve to three. Both the proposed re-parameterization methods achieve a good degree of output prediction at a greatly decreased computational cost.

Many latent olefin metathesis initiators have been described in the recent years. In this symposium manuscript, we present our contribution to the field, based on the use of different heteroatoms; namely sulfur, nitrogen, phosphorous and selenium; as chelators in ruthenium bidentate benzylidenes. The steric and electronic parameters were thoroughly studied and used in order to prepare not only latent pre-catalysts but also thermo- and photo-switchable systems ― effectively granting control over both reaction initiation and interruption.

A short overview on the structural design of the Hoveyda-Grubbs-type ruthenium initiators chelated through oxygen, nitrogen or sulfur atoms is presented. Our aim was to compare and contrast O-, N- and S-chelated ruthenium complexes to better understand the impact of electron-withdrawing and -donating substituents on the geometry and activity of the ruthenium complexes and to gain further insight into the trans-cis isomerisation process of the S-chelated complexes. To evaluate the different effects of chelating heteroatoms and to probe electronic effects on sulfur- and nitrogen-chelated latent catalysts, we synthesised a series of novel complexes. These catalysts were compared against two well-known oxygen-chelated initiators and a sulfoxide-chelated complex. The structures of the new complexes have been determined by single-crystal X-ray diffraction and analysed to search for correlations between the structural features and activity. The replacement of the oxygen-chelating atom by a sulfur or nitrogen atom resulted in catalysts that were inert at room temperature for typical ring-closing metathesis (RCM) and cross-metathesis reactions and showed catalytic activity only at higher temperatures. Furthermore, one nitrogen-chelated initiator demonstrated thermo-switchable behaviour in RCM reactions, similar to its sulfur-chelated counterparts.

A non-invasive digital imaging procedure has been successfully developed to quantify the strain history during electric resistance welded (ERW) pipe forming. A curve fitting method was used to reconstruct a continuous and smooth pipe profile from digital image data. Three B-spline approximation methods (one-dimensional, two-dimensional, and trigonometric B-spline) were evaluated using the same set of digital image data, and validated against the strain gauge measurements. Trigonometric B-spline provides superior curve fitting and strain prediction. Using the trigonometric B-spline approximation method, a 3D strain history contour plot was generated to optimize the mill setup and identify the strain reversal.

Reverse engineering of complex gene networks is a significant step towards understanding various biological processes. In this work, a novel algorithm for reverse engineering gene networks on a genome-wide scale using a noisy gene expression data is proposed. Under the proposed scheme, the challenges in reverse engineering of gene networks from these gene expression datasets are highlighted. Also, the parameter space describing gene interaction is partitioned into estimable and inestimable linear subspaces. The estimable linear subspace is obtained by using principal components analysis (PCA), the Akaike information criterion (AIC), and jackknifing. Furthermore, the approach is tested and validated using a simulated gene network model.