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Cet article a un erratum : [https://doi.org/10.2516/ogst/2019053]
Publié en ligne
2 Materials and methods
3 Results and discussion
Equivalent alkane carbon number of crude oils: A predictive model based on machine learning
Benoit Creton1,3*, Isabelle Lévêque1,3 and Fanny Oukhemanou2,3
1 IFP Energies nouvelles, 1 et 4 avenue de Bois-Préau, 92852 Rueil-Malmaison, France
2 Solvay-Laboratory of the Future, 178 avenue du Dr. Schweitzer, 33600 Pessac, France
3 The EOR Alliance, www.eor-alliance.com
* Corresponding author: benoit.creton@ifpen.fr
Received: 1 October 2018
Accepted: 8 January 2019
In this work, we present the development of models for the prediction of the Equivalent Alkane Carbon Number of a dead oil (EACNdo) usable in the context of Enhanced Oil Recovery (EOR) processes. Models were constructed by means of data mining tools. To that end, we collected 29 crude oil samples originating from around the world. Each of these crude oils have been experimentally analysed, and we measured property such as EACNdo, American Petroleum Institute (API) gravity and , saturate, aromatic, resin, and asphaltene fractions. All this information was put in form of a database. Evolutionary Algorithms (EA) have been applied to the database to derive models able to predict Equivalent Alkane Carbon Number (EACN) of a crude oil. Developed correlations returned EACNdo values in agreement with reference experimental data. Models have been used to feed a thermodynamics based models able to estimate the EACN of a live oil. The application of such strategy to study cases have demonstrated that combining these two models appears as a relevant tool for fast and accurate estimates of live crude oil EACNs.
© B. Creton et al., published by IFP Energies nouvelles, 2019
Symbols and acronyms
API: American Petroleum Institute
CCC: Concordance Correlation Coefficient
cEOR: Chemical Enhanced Oil Recovery
EA: Evolutionary Algorithms
EACN: Equivalent Alkane Carbon Number
EACNdo: Dead Oil EACN
EACNg: Gas EACN
EACNlo: Live Oil EACN
EOR: Enhanced Oil Recovery
GA: Genetic Algorithm
GC: Gas Chromatography
GP: Genetic Programming
HLD: Hydrophilic–Lipophilic Deviation
MAE: Mean Absolute Error
MGGP: Multi-Gene Genetic Programming
MPLC: Medium Pressure Liquid Chromatography
n-CV: n-fold Cross-Validation
PCA: Principal Component Analysis
QPPR: Quantitative Property Property Relationship
QSPR: Quantitative Structure Property Relationship
RMSE: Root Mean Square Error
S*: Optimal Salinity
SARA: Saturates, Aromatics, Resins, Asphaltenes
SG: Specific Gravity
SRK: Soave-Redlich-Kwong
SVM: Support Vector Machine
Microemulsions are commonly encountered in many of products or formulations dedicated to various domains such as pharmaceuticals, cosmetics, or petroleum applications. Among these latter, crude oil extraction after applying primary and secondary recovery methods can be roughly estimated to half of the initial oil reservoir content, according to the considered field [1]. The development of tertiary recovery methods – Enhanced Oil Recovery (EOR) – has gained interest especially with the increase of crude oil prices [2]. The Chemical Enhanced Oil Recovery (cEOR) technique involving combinations of alkali, surfactants and/or polymers aims at decreasing water/oil Interfacial Tension (IFT) in order to mobilize the oil trapped by capillary forces. Optimizing these combinations or formulations to form an efficient microemulsion is a challenging and time-consuming task considering that each potentially eligible reservoir exhibits different conditions such as the oil composition, brine salinity and hardness, pressure, temperature.
The Hydrophilic–Lipophilic Deviation (HLD) concept as proposed by Salager has been applied in numerous studies to mimic phase behavior of {brine/surfactant/oil} systems [3, 4]. When HLD value is zero, the Salager relation linearly correlates the optimal salinity (S*) – the logarithm of the optimum salinity in g/L – with alcohol amount and type (f(A)), the temperature and the Equivalent Alkane Carbon Number (EACN), see equation (1) [5].
where Tref is set to 298.15 K, α is a temperature coefficient, Cc – the characteristic curvature – and K are parameters related to the surfactant chemistry [6]. The concept of EACN is commonly considered during surfactant formulation design. It assumes that the EACN of an oil is equal to the number of carbon atoms of the n-alkane exhibiting a similar phase behavior. EACN of the dead oil (EACNdo) – oil degassed at standard conditions – are experimentally determined by means of test tubes and salinity scans. It consists in identifying the n-alkane matching {brine/surfactant/dead crude oil} and {brine/surfactant/n-alkane} phase behaviors [7, 8]. However, depending on the crude oil composition several weeks may be necessary to reach the thermodynamics equilibrium.
Bouton et al. proposed a Quantitative Structure Property Relationship (QSPR) for the prediction of the EACN of hydrocarbons by means of two theoretical descriptors, i.e., the average negative softness and the Kier A3 [9], with EACN values in between −4 and +35. More recently, Lukowicz et al. proposed a QSPR based on COSMO-RS σ-moments to predict EACN of polar hydrocarbon oils [10] and then extended their model to the case of aprotic polar oils [11]. To determine EACN of hydrocarbon mixtures, Cayias et al. [12] and Cash et al. [13] proposed the use of a mixing rule in which individual hydrocarbon EACNs are weighted according to corresponding mole fractions. In the case of live oils – oils containing dissolved gases at specific temperature and pressure conditions – we recently proposed an approach to predict the EACN of live oil (EACNlo) on the basis of volumetric fractions of oil and gas [14]. Indeed, Marliere et al. have experimentally shown that EACN linearly varies with the gas volumetric fraction [7]. Our EACNlo model necessitates the a priori knowledge of the gas composition, gas to oil ratio, temperature and pressure conditions, and the EACN of the dead oil. The volumetric fractions of light hydrocarbons are estimated using the Soave-Redlich-Kwong (SRK) equation of state [15] with the volume correction as proposed by Péneloux et al. [16]. The use of our model for the prediction of live crude oil EACN [14] would gain in relevancy developing methods to predict crude oil EACNdo. A crude oil contains thousands of diverse chemicals and the exact composition is never known, as a consequence the combined use of above mentioned models and mixing rules to predict EACNdo is unrealistic.
During the past decade, we considered the use of data mining based approaches to extract information from databases and predict properties of complex fluids [17]. These approaches known by the acronym QSPR aim at identifying non-obvious correlations between property values of the matter and some features rendering information about the matter [18, 19]. In this work, we propose (i) the creation of a database containing experimental EACNdo values as well as results of experimental analysis for a series of crude oils, (ii) the application of machine learning methods to derive models for the prediction of crude oil EACN values, and (iii) the use of developed models for the prediction of live oil EACN for a set of crude oils. The article is organized as follows: the next section deals with materials and methods and gives all details regarding the database creation and methods used to generate models, a subsequent section presents the predictive performance of models and an application of generated models to predict live oil EACN, and the paper ends with concluding remarks.
2.1 Experimental data and database creation
Wan et al. proposed a review and comparisons of applicable approaches to experimentally determine the EACN of a dead crude oil [20]. As detailed in previous works [7, 8], all EACNdo values reported hereafter were obtained following the method referred to as the direct method by Wan et al. This method is mainly based on the use of equation (1) and consists in performing several phase diagrams for a {brine/surfactant/crude oil} system varying the salt concentration, i.e. a salinity scan. The S* for the {brine/surfactant/crude oil} system is reached when the phase diagram exhibits an equal repartition of the microemulsion between the oil and the aqueous phase. The so-obtained salinity scan and the S* value are then compared to those previously obtained for similar conditions (surfactant formulation, temperature, brine composition…) in the case of linear alkanes such as n-decane (C10), n-dodecane (C12) and n-tetradecane (C14). The EACNdo value for the crude oil is finally determined by solving equation (1) using K and Cc parameters obtained for C10, C12 and C14. This analytical methodology has been applied on 29 crude oils from around the world, and obtained EACNdo values are reported in Table 1. EACNdo values for crude oils of interest lie in between 1.2 and 18.0. The experimental determination of EACNdo for crude oils requires days to weeks to reach thermodynamic equilibrium depending on their nature, whether they are light or heavy. The interest of a fast and accurate theoretical method to predict EACNdo for a crude oil thus becomes evident but its parameters should be easily determined.
Experimental EACNdo, API gravity, fraction (%wt) and fractions (%wt) of saturates (Sat.), aromatics (Aro.), resins (Res.), and asphaltenes (Asp.) measured for the 29 crude oils considered in this study.
The American Petroleum Institute (API) gravity measures whether a crude oil is lighter or heavier than water. It is defined using the following expression:
with the Specific Gravity (SG) = ρ crude oil/ρ water, where ρ crude oil and ρ water denote the density of the crude oil and the density of water, respectively at 15.5 °C (60 °F). Densities for the 29 crude oils were measured using an Anton Paar density meter (model DMATM 4500 M) including an oscillating U-tube sensor, and the uncertainty associated to density measurements is 0.1%. API gravity values were then calculated using equation (2). Table 1 presents API gravity values for crude oils #01 to #29, it reveals that the set of crude oils covers a broad range of API gravity values from 11 to 50 denoting heavy and light crude oils, respectively. It is interesting to note that in the set of crude oils, crude oil #19 appears as an outlier in terms of API gravity.
The Saturates, Aromatics, Resins, Asphaltenes (SARA) analysis is a method based on fractionation to characterize the crude oil content in terms of saturates, aromatics, resins, and asphaltenes [21]. The basic idea is to divide the crude oil into smaller fractions playing with oil component solubilities in solvents such as linear alkanes. Different SARA methodologies have been described for instance varying the used n-alkane, i.e. n-pentane or n-heptane changing the amount of precipitated asphaltenes [22]. All considered crude oils were characterized using a SARA analysis similar to that used by Behar et al. [23], and the analytical procedure can be briefly described as follows: Each crude oil is dissolved in n-pentane at 43 °C, and the resulting solution is filtered (Durapore® membrane in polyvinylidene fluoride with 0.45 μm pore size) to separate by-products from other crude oil components. By-products are treated by adding dichloromethane at the same temperature to recover the precipitated asphaltenes, and fractions are weighted during the entire procedure. An aliquote of the n-pentane rich solution is analyzed by Gas Chromatography (GC) to quantify the fraction – the crude oil fraction containing compounds with a number of carbon atoms lower than 20. An another aliquote is evaporated and separated using Medium Pressure Liquid Chromatography (MPLC) to characterize weight amounts of saturates, aromatics, and resins. All fractions are then standardized according to measured masses, and the crude oil is assumed as the blend of , saturates, aromatics, resins, and asphaltenes. Table 1 presents results of the SARA analysis for the 29 considered crude oils. Fractions of saturates, aromatics, resins, and asphaltenes are determined with associated uncertainties of 2%wt, in agreement with conclusions drawn by Aske et al. [24]. Experiments necessary to determined , saturate, aromatic, resin, and asphaltene fractions demand approximatively 4 days whatever the nature of the oil. Note that fractions of saturates obtained with different SARA methodologies should be similar, and that the Aro., Res. or Asp. fraction determined with one SARA analysis should correlate with its corresponding fraction issued from a different SARA methodology. These assumptions result from comparisons of data reported in Table 1 with fractions determined using another SARA methodology (not shown here). From data reported in Table 1, it is interesting to note that in this set of crude oils, crude oil #02 appears as an outlier in terms of and SARA fractions.
Hereafter, each crude oil is characterized by descriptors presented in Table 1, i.e. API gravity, fraction and fractions of saturates (Sat.), aromatics (Aro.), resins (Res.), and asphaltenes (Asp.). The development of models will then consist in relating EACNdo to descriptors leading to relations between properties, i.e. Quantitative Property Property Relationships (QPPR). Several works in the literature report correlations of crude oil properties with SARA analysis outputs [25–30].
2.2 Modeling methods
2.2.1 Data sets
The accuracy of predictive QPPR (similarly to QSPR) is in part related to the quality of data, hence the quality of the database is a keystone for the success of such modeling works. Possible correlations between descriptors have been investigated by generating a correlation matrix by means of the Materials Studio software [31]. No evidence of highly correlated descriptors has been found, and the highest values in the correlation matrix were obtained for couples: Res. with Aro. and Res. with . Fan and Buckley proposed on the basis of six medium-gravity dead crude oils (with API gravity values from 22.6 to 37.2) a relation between API gravity and SARA fractions [25]. No evidence of such a relation has been found in our data set. The best found correlation between API gravity and SARA fractions has a low coefficient of determination (R 2 ≃ 0.4). Data presented in the Table 1 have been used to constitute our database.
As a preprocessing of the data, we performed a Principal Component Analysis (PCA) applied on API gravity, and SARA fractions measured for the 29 crude oil samples. Figure 1 presents projections of the 29 crude oils in the space formed by the three main principal components resulting from the PCA. The diagram thus provides a representation of crude oil distributions in the chemical space of our database. This distribution reveals that one crude oil (crude oil #02) is isolated from all other samples that thus confirms crude oil #02 as an outlier. In Figure 1, each symbol is filled as a function of their EACNdo value, and there is no obvious relation between the location of a crude oil on the diagram and the value of its EACNdo.
Projections of crude oils on PC1, PC2 and PC3, the three first principal components resulting from the PCA. Symbols are filled using a colorbar and intensities are as follows: low and high EACNdo values are indicated in blue and red, respectively.
Application of external validation has been shown as necessary to validate model's robustness when predicting new compound property values, meaning candidates not used during the model development [32]. One of the popular methods is the n-fold Cross-Validation (n-CV) in which the data set is randomly divided in approximately equal n portions, the leave-one-out being its extreme version with n equals the number of samples in the database. An aggregate of (n−1) portions forms the Training set used to optimize predictive models, the remaining portion constituting the Test set. We emphasize that no data point belonging to external sets is used to derived models. This procedure is repeated n times choosing at each new fold another portion of data as a Test set. From conclusions drawn during the preprocessing of the database, we choose to impose crude oils #01 and #02 in all Training sets in order to keep EACNdo ranges constant. The 27 remaining crude oils were randomly distributed into nine portions, therefore the Training sets and Test sets represent 90% and 10% of the database, respectively.
Perfomances of models are evaluated on both Training and Test sets calculating values for some statistical indicators such as the Mean Absolute Error (MAE), Root Mean Square Error (RMSE), R 2, and the Concordance Correlation Coefficient (CCC) [33]. Chirico and Gramatica have shown that the use of this latter coefficient is advocated considering various scenarios such as location shifts, scale shifts, and location plus scale shifts [34, 35].
2.2.2 Machine learning methods
The application of machine learning methods for thermophysical property predictions has been the subject of our numerous past and ongoing research works [17]. From comparisons drawn in these previous studies, we have observed that Support Vector Machine (SVM) based models outperform other evaluated learning algorithms such as neural networks, partial least squares, Genetic Algorithm (GA). However, the number of data points is in this work quite small and we hereafter search for explicit multilinear equations that can be easely implemented in a spreadsheet. One possible manner to generate multilinear models is to use Evolutionary Algorithms (EA). EA are based on the Darwinian evolution theory of biological species in nature, and they represent promizing methods for optimization problems. When applied to regression problems, the resolution consists in the iterative evolution of a population of equations initially randomly generated and respecting the general following form:
where λ 0 is the inercept, λ i denotes a weight associated to the gene i (G i ), and N is the total number of genes in the model. To derive models, two approaches were tested varying from the level of information in genes.
In the first approach, a gene (see the proposed schematic representation in Fig. 2) consists of a tree built by combining descriptors (API gravity, fractions of , Sat., Aro., Res., and Asp.) and mathematical functions (see Tab. 2) allowing to catch non-linearity in property variation. Multi-Gene Genetic Programming (MGGP) based models were generated using the Genetic Programming Toolbox for the Identification of Physical Systems (GPTIPS) coded in the MATLAB environment [36–38]. We applied the tournament method to select individuals in the population of equations on the basis of their fitness and complexity. We fixed the tournament size to 25 corresponding to 10% of the population size. Generations are constructed by survival of fitter individuals, and reproduction of individuals consists in applying crossover as well as mutation operations to produce child equations. The iterative procedure ends when one of the fixed criteria such as maximum number of generations, best fitness values is reached. Note that during the iterative procedure, the structure of trees evolves through out crossover and mutation operations applied to sub-tree elements. Clearly, the maximum numbers of genes and nodes per tree must be limited to prevent overfitting problems. Additionally, the maximum numbers of generations and runs – repetition of the calculation – should be optimized to ensure convergence of calculations for reasonable computational ressources. Table 2 reports details about values and/or ranges of investigated GPTIPS settings in this work.
Schematic representation of a tree. Function stands for a mathematical operator belonging to the function set (see Tab. 2), for instance +, −, × or ÷. Descriptor denotes either API gravity, fractions of , Sat., Aro., Res., or Asp.
Investigated parameter settings for the MGGP based method.
In the second approach, a gene (see Eq. (3)) simply stands for one of the descriptors. A GA based variable selection method was followed, and the Genetic Function Approximation (GFA) as implemented in the Materials Studio software was used to build multilinear models [31]. The GFA procedure consists in iterations of selection, crossover, and mutation operations, coupled with objective criteria such as R 2 in order to extract the best fitting models. In this work, the adjusted R 2 was used as the objective criteria. The initial population (i.e., initial number of equations) was set to 6, and the maximum generation number to 50 000. This procedure was performed on each of the nine Training sets, noting that the same decompositions (folds, Training, and Test sets) are used during GFA and GPTIPS based procedures.
3.1 Development of QPPR models
In this section, we report various QPPR models to predict EACN of dead crude oil knowing a series of experimental data such as API gravity, fraction, and fractions of saturates, aromatics, resins, and asphaltenes, see Table 1. Two machine learning methods based on EA were used: GFA and GPTIPS.
During the development of GFA based models, the maximum number of variables allowed to form equations was set to four. This value meets the statistical criteria n ≥ 4k, where k and n are the number of variables in the model and the number of data points in the Training set, respectively [39]. GFA based models were optimized following a 9-fold cross-validation procedure. Samples #01 and #02 are fixed in Training sets, and therefore each of the nine folds contains three randomly selected crude oils. Equation (4) presents the so-obtained model. This model is composed of three descriptors weighted by λ i coefficients, and a constant λ 0 as follows:
λ i values obtained considering successively each fold as external data (not used during the optimization procedure) are reported in Table 3. Equation (4) is in line with some physical intuitions. For instance, the negative value of λ 1 agrees with the fact that EACNdo should decrease with increasing fractions (the light fraction of the crude oil). The opposite effect is observed through out the positive values for λ 2 and λ 3, i.e. EACNdo values should increase with Sat. and Asp. fractions (heavy fractions of the crude oil). Note that the value obtained for λ 0 roughly corresponds to the average of EACNdo values in the database (12.9 points of EACN). Table 4 presents performance characteristics for equation (4), calculated as follows: (i) For all crude oils belonging to Test sets and corresponding λ i coefficients (see Tab. 3). For instance, for crude oils belonging to Fold-01 we used λ 0 = 13.761, λ 1 = −0.120, λ 2 = 0.141, and λ 3 = 0.112. Performance characteristics reported in Table 4 so reflect the predictive capabilities of GFA based models. (ii) For all crude oils and average parameters (see Tab. 3). In this case, no conclusion can be drawn regarding the predictive capabilities of the GFA based model as all crude oils have been indirectly involved in the learning procedure.
Parameter values for the GFA model presented in equation (4), determined using each fold as external data (Test set). <> stands for the average of parameter values taken over the nine folds.
Performance characteristics for the GFA based model. Values taken by some statistical indicators when applied to predictions of equation (4) for crude oils in Test sets (Pred.). <> stands for the average of parameter values taken over the nine folds.
Figure 3 presents scatterplots of experimental EACNdo versus predicted EANCdo values using equation (4) with either parameters associated to each fold in Test set, or average parameters. The diagram exhibits no huge difference between the two sets of parameters in terms of predicted EACNdo values. Values taken by some statistical indicators as reported in Table 4 quantitatively confirm this observation. Noting that EACNdo values for samples fixed in Training sets (i.e., 18.0 and 1.2 for crude oils #01 and #02, respectively) can only be predicted using average parameters, and GFA based model fails in predicting the EACNdo value for crude oil #01.
Scatterplots of experimental EACNdo values versus predicted EACNdo values using equation (4). Circles stand for pure predictions using GFA based models while plus symbols denote the use of average parameters.
GP is a generalization of GA. The main difference between GP and GA is that models obtained by the latter are strings of weighted descriptors, while the former returns tree expressions in which a node is either a descriptor, a mathematical function or a coefficient. MGGP can be seen as a combination of GA and GP as MGGP returns strings of genes, each gene having a tree structure [40]. We have used MGGP to develop correlations able to catch possible non-linearity effects in EACNdo modelling. In MGGP as implemented in the GPTIPS, important settings should be parameterized, for instance numbers of runs, generations, genes, nodes. To our knowledge there is no methodology to parameterize GPTIPS and it often results from trials and errors. Mohamadi-Baghmolaei et al. indicated that some of MGGP parameters are commonly randomly set [41], and Garg et al. proposed to adjust MGGP parameters according to the nature of the regression problem [40, 42]. For our problem, the maximum number of genes has been chosen according to the statistical criteria n ≥ 4k, where k and n are the number of variables (i.e. genes) in the model and the number of data points in the Training set, respectively. Considering both the database content and the Training/Test splitting, a reasonable value for the maximum number of genes is 6. Regarding settings dedicated to tree's structure, the maximum number of nodes per tree and the maximum depth of tree have been set to 12 and 4, respectively. For settings related to convergence of calculations, the maximum number of generations and the maximum number of runs have been set to 2000 and 40, respectively. In order to allow a great possibility in nonlinear models, we have chosen a large number of mathematical operators such as addition, subtraction, multiplication, division, square root, exponential, logarithm. We propose an approach to optimize the numbers of runs, generations, genes, and nodes that will further be used to develop a model. The convergence matter was first addressed, and the trade-off between convergence, computational time, and accuracy was treated as follows:
The numbers of genes and nodes were set to their respective maximum possible value in order to generate models with the highest complexity. We then compared model's performances exploring the space formed by the number of generations discretized as follows: 100, 500, 1000, and 2000, and by the number of runs discretized as follows: 1, 5, 10, 15, 20, 25, 30, and 40, as indicated in Table 2. Performances were evaluated using the RMSE statistical indicator calculated over all samples in the dataset. No Training and Test sets division was considered in this step as the idea is to roughly select appropriate numbers of runs and generations, leading to reasonable accuracy and computational time. Figure 4 presents the power law evolution of model's performance as a function of numbers of runs and generations. Clearly, models poorly perform when optimized with only 100 generations. Considering 500, 1000 or 2000 generations leads roughly to similar performances after 20 runs. However, the deviation between raw data and the corresponding power law is in the case of 2000 generations twice that obtained for 500 or 1000 generations (not shown here). Contour plots on Figure 4 indicate that for a similar computational cost, equivalent performances are obtained with 500 or 1000 generations. The consideration of 30 runs seemed relevant to ensure the convergence of calculations, and in order to limit the computational time we selected 500 generations.
Power law evolution of model's performance (on the basis of RMSE values) as a function of the number of runs, for number of generations in between 100 and 2000. Contour plots (thin grey lines) show lines that share the same computational cost.
Numbers of generations and runs were respectively set to 500 and 30, and models were developed all along the space formed by the number of genes discretized as follows: 1, 2, 3, 4, 5, and 6, and by the number of nodes discretized as follows: 2, 4, 6, 8, 10, and 12, as indicated in Table 2. Contrary to step (i), Training and Test sets were considered here using the nine folds previously generated for the GFA based model development. For each point of the space, sum of squared errors were calculated for Test sets, and the optimum numbers of genes and nodes were determined minimizing the total sum of squared errors on Test sets. The optimization procedure led to a number of nodes of 8 and a number of genes of 4.
The proposed optimization procedure applied to our regression problem led to numbers of runs, generations, genes, and nodes of 30, 500, 4, and 8, respectively. Nine GPTIPS based models were optimized following a 9-fold cross-validation procedure, noting that the nine folds generated for the GFA based model development have been reused. Performances of models are presented in Table 5. All models outperform GFA based models, and exhibit RMSE values in between 0.97 and 1.40. The model that best generalizes the database has been developed using Fold-08 as Test set. Details about this latter model such as the four weighted genes and the intercept are presented in equation (5).
Performance characteristics (statistical indicators) GPTIPS based models applied to crude oils in the database. Fold-0i means that the fold is external to the learning procedure.
Clearly, each gene non-linearly contributes to the predicted EACNdo value, and the model involves all descriptors excepted the fraction. Figure 5 presents the scatterplot of experimental EACNdo versus predicted EANCdo values using equation (5). All data points are less scattered from both sides of the bisector (predicted EACNdo equals experimental EACNdo) as compared to observations performed on Figure 3. Contrary to the GFA based model, equation (5) well predicts crude oil #01 with a value of 18.2. Although crude oil #02 has been sensed as an outlier in terms of EACN value and composition, its EACN is well estimated using both GFA and GPTIPS based models. The largest deviation between experimental and predicted EACNdo values (2.5 EACN points) is measured for crude oil #21, noting that none of GPTIPS based models developed on each fold succeeds in predicting this value and that the GFA based model also failed in predicting this EACNdo value.
Scatterplots of experimental EACNdo values versus predicted EACNdo values using equation (5).
We investigated the sensitivity of equation (5) regarding uncertainties associated to input data. Indeed, due to filtration operations, evaporation losses and/or incomplete solvent removal during SARA analysis, fractions of saturates, aromatics, resins, and asphaltenes are determined with associated uncertainties of about 2%wt [24]. API is calculated from the density of the crude oil and using equation (2). The uncertainty associated to density measurements is 0.1%. Although the fraction of aromatics is involved in genes G1, G2, and G4, a 2%wt deviation applied on Aro. fractions in Table 1 only slightly deteriorates predictions with MAE and RMSE of 0.87 and 1.00, respectively. Performances of equation (5) only falls to MAE = 0.96 and RMSE = 1.12, considering a 0.1% deviation on density values and a 2%wt deviation on each SARA fraction (Tab. 1).
3.2 Application to the prediction of live crude oil EACN
Live oils are oils containing dissolved gases at specific temperature and pressure conditions. Creton and Mougin proposed a model based on thermodynamics to predict the EACN of a live oil (EACNlo) knowing the EACN of the dead oil (stock tank oil), reservoir pressure (P) and temperature (T) conditions, as well as the gas to oil ratio (R si) [14]. This model is based on a volumetric mixing law applied to EACNdo and EACN of gas (EACNg), as follows:
where, EACNg equals the sum of n-alkane carbon atom numbers (ACN, alkane carbon number) weighted by their respective volumetric fraction, i.e. when solely methane is used as representative gas: EACNg equals 1. Molar volumes were calculated using the SRK [15] Equation of State (EoS) applied with the volume correction proposed by Péneloux et al. [16]. Creton and Mougin validated their model for several crude oils covering broad ranges of reservoir characteristics, and studied impact of pressure, temperature, and gas composition on predicted live oil EACNs [14]. These latter crude oils are part of our database (Tab. 1), and we propose hereafter to feed the model proposed by Creton and Mougin with EACNdo values obtained using equation (5). Table 6 presents for crude oils in common between reference [14] and this work, some oil properties and their original reservoir characteristics. Figure 6 presents scatterplots of experimental EACNlo values versus predicted EACNlo values. Using the full predictive approach – the model proposed by Creton and Mougin together with equation (5) – a MAE of 1.5 EACN point is observed on EACNlo predictions as compared to a MAE of 1.1 EACN point when experimental EACNdo are used to feed the model by Creton and Mougin [14]. Therefore, the proposed combining of the two models appears as a relevant tool to estimate EACN of live crude oils.
Scatterplots of experimental EACNlo values versus predicted EACNlo values. Circles stand for the full predictive approach (the model proposed by Creton and Mougin together with Eq. (5)) while plus symbols denote the use of the model proposed by Creton and Mougin together with experimental EACNdo.
Summary of live crude oil properties. For each case study, the predicted EACNdo using equation (5), reservoir temperature and pressure, the gas to oil ratio, the solution gas composition, and experimental and predicted EACNlo values are indicated [14].
3.3 Conclusions and perspectives
In the context of EOR, some chemical EOR techniques involve surfactant formulations to mobilize oil trapped by capillary forces. In order to assist and speed up experiments necessary for the formulation design, we recently proposed a model based on thermodynamics to predict EACN of live crude oil. This model consists in a linear mixing rule based on volumetric fractions of the EACN of the dead crude oil and the EACN of the representative gas. The objective of the present work was to use data mining based approaches to investigate and develop relations between the EACN and the composition of dead crude oils.
We collected 29 crude oil samples originating from around the world and performed analysis to obtained compositional information. Each sample has been described in terms of EACNdo, API gravity, and fractions of , saturates, aromatics, resins, and asphaltenes. The database covers a broad range of API gravity with values ranging from 11 to 50 denoting heavy and light crude oils, respectively. Machine learning methods based on EA have been applied to our database in order to generate QPPR to predict EACNdo. In the case of MGGP, we proposed an approach to parameterize GPTIPS. Obtained QPPR models were compared to each other in terms of capacity to generalizing the database. Note that this work could be done using any SARA analysis but fractions of , saturates, aromatics, resins, and asphaltenes within the database must be consistent. The best QPPR model was then used to feed a thermodynamics based model to predict EACNlo for crude oils. Comparisons carried out demonstrate that the proposed combining of the two models appears as a relevant tool for fast and accurate estimates of live crude oil EACNs.
To the best of our knowledge, this work represents the first attempt to predict EACN of crude oils using data mining. When new samples of crude oils will be available, API gravity, and fractions of , saturates, aromatics, resins, and asphaltenes will be experimentally determined. The new samples will be used to supplement our database. The QPPR model (Eq. (5)) developed in this work will be applied to predict EACNdo values for the new crude oils. According to the accuracy of the property predictions and as the QPPR model is more statistical than a physical law, the QPPR model may necessitate an update using MGGP.
Authors wish to thank the EOR Alliance team for their technical assistance and all relevant discussions.
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text:
Q: Google App Engine: Upload data syntax? I'm trying to use appcfg.py to upload to localhost/Google App Engine. I've basically been following these instructions word for word, but now I'm having trouble.
Here is the command I'm trying:
appcfg.py upload_data --app-id=appname --config_file="path\to\ItemLoader.py" --filename="path\to\data.csv" --kind=Item --url=http://localhost:8080/remote_api <path\to\app>

I get back:
The syntax of this command is incorrect.

What am I doing wrong?
UPDATE: Looks like I'm not actually supposed to include the < > chars around the path to the app. Now I'm getting an error:
appcfg.py: error: no such option: --app-id

UPDATE 2: I took out the --app-id parameter, and now another error:
appcfg.py: error: Expected <directory> argument

So... I do need the brackets?
UPDATE 3: Ok, it accepts the commands with quotes instead of brackets. But the development console at http://localhost:8080/_ah/admin/datastore shows that the Datastore is still empty. The following error was reported:
IOError: [Errno 13] Permission denied: `path\\to\\google\\SDK\\bulkloader-log-20100301.200921'

UPDATE 4: It appears that it was a permissions error with my file system. I granted a bunch of permissions, and now it is complaining about the referenced scripts instead.

A: Replace:

appcfg.py upload_data --app-id=appname
  --config_file="path\to\ItemLoader.py" --filename="path\to\data.csv" --kind=Item --url=http://localhost:8080/remote_api

by :

appcfg.py upload_data
  --application=appname --config_file="path\to\ItemLoader.py" --filename="path\to\data.csv" --kind=Item --url=http://localhost:8080/remote_api

redpajama_set_name: RedPajamaStackExchange


text:
Eritrea faces societal collapse
By Jean Shaoul
A United Nations report has exposed the dreadful conditions in Eritrea, located in the Horn of Africa, that have prompted hundreds of thousands to flee the country. By mid-2014, there were nearly 360,000 Eritreans registered as refugees worldwide by the UN, out of a population of just 6.3 million.
Eritreans are victims, alongside migrants from other North African and Middle Eastern countries, of brutal wars stoked by US imperialism and its European allies in their bid to control the region's energy resources. They are second in number only to Syrians seeking a safe refuge in Europe. According to the UN High Commissioner for Refugees, nearly one in five of the 200,000 migrants who made the crossing to Europe last year were Eritreans.
The 484-page report by the UN's Office of the High Commission for Human Rights (OHCHR) is the result of a year-long investigation. It was based on 550 interviews in third countries and 160 written submissions because the Eritrean government refused to cooperate. Many people declined to provide evidence to the OHCHR, even anonymously, fearing reprisal. It details "systematic, widespread and gross human rights violations, forcing hundreds of thousands of refugees to flee the country." These abuses were carried out by President Isaias Afwerki's "totalitarian" government, which rules without accountability or the rule of law.
Afwerki has been in power since independence in 1993. Since then, there have been no elections, opposition parties have been banned and the media suppressed.
The report argued that, collectively, Afwerki's ruling People's Front for Democracy and Justice Party, the military, the national security office, the police, and government departments have operated a system that leads to arbitrary arrests and detention. Torture and even enforced disappearances are the norm. Even those who have committed no crime could end up in arduous and indefinite national service that amounts to forced labour.
Eritrea's foreign ministry denounced the UN report as a "vile slander" and a "cynical political travesty." It said that the allegations were "totally unfounded and devoid of all merit." The report was, in addition, an attack "not so much on the government, but on a civilized people and society who cherish human values and dignity."
The study found that the government's policy of conscription can leave young people trapped indefinitely in the army. Some interviewees said they had served long terms as conscripts, including one who had served 17 years. Those who try to flee the country are treated as "traitors." There is a shoot-to-kill policy on the border. The OHCHR said people were still being shot in 2014, including children, although the government says it has ended the policy.
Conditions at the military training camps are dire, and sexual violence against women and girls is rampant. Many underage students are subject to forced labour under the threat of death, leading the OHCHR to conclude that, "forced labour in this context is a practice similar to slavery."
"The commission also finds that the violations in the areas of extrajudicial executions, torture (including sexual torture), national service and forced labour may constitute crimes against humanity," it adds.
The UN report contradicted the controversial Danish report Eritrea—Drivers and Root Causes of Emigration that concluded that many Eritreans were fleeing for economic reasons. The report caused outrage and was widely discredited as politically-motivated, designed to deny asylum status to Eritreans, with the UK using it to argue that Eritreans are no longer in danger if they return home.
The OHCHR said that it was these conditions that caused hundreds of thousands to flee the country: "In desperation, they resort to deadly escape routes through deserts and neighbouring war-torn countries and across dangerous seas in search of safety... To ascribe their decision to leave solely to economic reasons is to ignore the dire situation of human rights in Eritrea and the very real suffering of its people."
The desperate situation in Eritrea is part of the ongoing fragmentation and disintegration of the countries in the Horn of Africa, which includes Ethiopia, Sudan, South Sudan, Somalia and Djibouti. The Horn is an arena of intense great power rivalry for the control of oil reserves in neighbouring countries and the sea route through the narrow Bab al-Mandeb straights through which much of Europe's oil passes. The United States and its European allies are engaged in a ferocious struggle with China for control of the region's oil resources.
Eritrea became independent from Ethiopia in 1993 after a bitter 30-year long civil war, but without a properly drawn up settlement or agreement about long sections of the borders. Both countries became client states of the US, with Ethiopia, home to 90 million people, becoming the biggest sub-Saharan recipient of US aid. America was one of only a handful of states to establish an embassy in Eritrea, in large part because its sea port Massawa occupies a strategic position immediately opposite Saudi Arabia on the Red Sea, commanding the route to and from the Suez Canal.
The economic paths of Ethiopia and Eritrea soon began to diverge, leading to disputes and a full-scale war—just five years after independence—over Badme, a remote but fertile area said to contain gold and industrial minerals. The war brought economic ruin to two of the poorest countries on the planet, as they spent millions re-arming. This had a devastating impact on living standards, with a widening gap between rich and poor and the displacement of hundreds of thousands of people.
The war spilled over into neighbouring Somalia, a country that all but disintegrated thanks to the political machinations of competing imperialist powers during the colonial and Cold War eras, and the IMF interventions in the 1980s that wrecked Somalia's pastoral economy and led to the civil wars and famine that have bedevilled the country ever since. In their search for allies, Ethiopia and Eritrea supported opposing factions in Somalia, leading to a proxy war that was to continue until 2009, involving sponsoring rebel groups in each other's countries.
Eritrea fell foul of the US, which backed Ethiopia as its key strategic ally in the region.
The Ethiopia-Eritrea war ended in 2000 after the loss of more than 100,000 lives, following an Algerian-brokered mediation and arbitration that largely ruled in Eritrea's favour. Ethiopia never honoured the agreement, leading to a stalemate, occasional military flare-ups, and heavy militarisation on both sides of the border and the cessation of trade between the two countries, depriving landlocked Ethiopia of access to Eritrea's Red Sea ports.
The "Ethiopian threat" served as a much-needed justification for Afwerki and his clique's dictatorship, as they profited from the country's mineral resources.
In 2008, US President George W Bush declared Eritrea a "state sponsor of terrorism" for its backing of al-Shabab in Somalia, which Ethiopia was then fighting on Washington's behalf. This triggered US economic sanctions. The following year, at US behest, the UN passed Resolution 1907 imposing sanctions against Eritrea, leading Eritrea to become an international pariah.
This has had a devastating impact on the impoverished country, where the average per capita income is about $550 a year, according to the most recent World Bank figures.
Workers Struggles: Europe, Middle East & Africa
South Africa: COVID-19 cases soar with little prospect of widespread vaccination
US recognizes Morocco's annexation of Western Sahara

redpajama_set_name: RedPajamaCommonCrawl


A federal appeals court has denied the Archdiocese of Washington's bid to force the city's public transportation agency to display its Christmas ads.
The U.S. Court of Appeals for the D.C. Circuit issued an order Wednesday suggesting the agency could lawfully refuse to run the archdiocese's explicitly religious holiday advertisement. The Washington Metro Area Transit Authority (WMATA), the agency which administers bus and subway service in the D.C. metro area, does not run ads that promote religious observance. The archdiocese claims the policy is unconstitutional viewpoint discrimination.
The proposed ad promotes a Catholic holiday campaign called "Find the Perfect Gift," which encourages participation in various forms of Christmas observance.
The order, issued by a three-judge panel, explains that the archdiocese failed to show WMATA's policy favors secular holiday views over religious ones. Though WMATA excludes religious groups promoting Christian Christmas practice, the agency has not run ads expressing secular views about the holiday, meaning no viewpoint discrimination has taken place.
"[The archdiocese] has not come forward with a single example of a retail, commercial, or other non-religious advertisement on a WMATA bus that expresses the view that the holiday season should be celebrated in a secular or non-religious manner," the order reads.
They further say the archdiocese failed to show that WMATA's policy substantially burdens Catholic religious practice.
Supreme Court precedent allows the government to impose reasonable limitations on speech and expression in certain public spaces like transportation hubs.
The archdiocese argues WMATA's policy is unreasonable, arbitrary, and discriminatory under the First Amendment. They claim, for example, that a commercial ad promoting Christmas shopping would satisfy agency advertising guidelines, but a religious ad would not.
The Wednesday order was not a ruling on the merits of the lawsuit; it simply left in place a lower court ruling denying the archdiocese an injunction. The D.C. Circuit directed the parties to prepare for expedited briefing and argument in the case. Full briefing on the substance of the lawsuit must be filed by mid-February. Oral arguments will be scheduled for the first available date once each side has filed its briefs.
Editor's note: The writer's fiancee is employed by a law firm involved in this litigation.
I wonder if the transportation authority as a matter of policy refuses to run atheist, anti-religion Christmas ads similar to the ones I sometimes see on billboards. If the answer is "yes," then the standard is fairly applied. If "no," then there's a problem. And, for the record, I'm Jewish.


Today's messy monday activity while very messy and needed to be done outside was actually quite clean :-) We gave her little people a bath. I got the idea from the crafty crow which got it here. I have to say I was pleasantly surprised at Amber's enjoyment of this activity.... it provided hours of fun, and when i say that i am not exaggerating because the fun did last into the second hour, a record I think! We started out with a tub of water and 5 little people that Amber's Grammy gave her for easter, and a little frog joined us in the middle. Her favorite part of the whole thing (aside from washing their hair with a washcloth) was the soap... imagine our scene along the lines of the Mad Hatter's tea party in Alice in Wonderland... they keep pouring tea but no time to drink it before it needs to be poured again.... that was Amber with the soap. Squirt out the soap, set it down, opps! NEED MORE SOAP! go pick it up again. As you can see in this picture, she even squirted it right on their little heads. (I think we finally finished this bottle of soap that we have had since before she was born)..(she is also this way with Ketchup, but she calls it dip. She always needs more, she never seems to have enough on her plate and she refuses to eat until she has enough) I learned from this activity that she might one day become a chess player. She would move all the people around with such purpose and thought... from a distance it looked like she was playing chess, the way she would ponder, grap and move the person, then move one back a bit, pause, move again... soo cute. Here they are all in a line, ready to play again.
I even got a foot wash/massage not once but twice!! Talk about a relaxing activity for a sunny day.
Here she is again with the soap, this time the tub had migrated to the ground and it was the simple joy of squirting on the table and rubbing around with the cloth.
Finally she finished the bath with a bath for herself. She just could not get enough of washing her face... i think b/c she got soap in her nose and had to keep rubbing it b/c it was annoying :-) I think we could have gone even longer, but it was nap time.


text:
Q: Get/generate shared link for a uploaded file in Google Drive API v3 I am uploading files to a Gdrive follow this instructions. In the File objtect I just set the name, like this:
{
 "name": "myObjectName"
}

The files are uploading without problem. Now I need to generate a shared link for each upload file, Do you know who is the request that I have to do?
Thanks,

A: With @Jacques-Guzel Heron´s help, I completed the upload file process. I created a wrapper for Google Drive API v3. That is my code if any need to do something like that: (I am using C#):
public interface IGDriveApiV3Wrapper
{
    string UploadFile(string filePath, string gDriveUploadDestinationFolderId = null);

    bool SetFilePermissions(string fileId, GDriveFileRole gDriveRole, GDriveFileType gDriveType);

    GDriveFile GetFileInfo(string fileId);
}

public class GDriveApiV3NativeWrapper : IGDriveApiV3Wrapper
{
    private const string GDriveFilesApiResumablePath = "https://www.googleapis.com/upload/drive/v3/files?uploadType=resumable";

    private const string GDriveTokenApiPath = "https://oauth2.googleapis.com/token";

    private static readonly HttpClient GDriveClient = new HttpClient { Timeout = Timeout.InfiniteTimeSpan };

    private readonly List<KeyValuePair<string, string>> _getTokenRequestContent;

    private static GDriveTokenInfo _gDriveTokenInfo;

    private static readonly object UpdateGDriveTokenInfoLocker = new object();

    public GDriveApiV3NativeWrapper(string gDriveApiClientId, string gDriveApiClientSecret, string gDriveApiRefreshToken)
    {
        _getTokenRequestContent = new List<KeyValuePair<string, string>>
        {
            new KeyValuePair<string, string>("client_id", gDriveApiClientId),
            new KeyValuePair<string, string>("client_secret", gDriveApiClientSecret),
            new KeyValuePair<string, string>("refresh_token", gDriveApiRefreshToken),
            new KeyValuePair<string, string>("grant_type", "refresh_token")
        };
    }

    public string UploadFile(string filePath, string gDriveUploadDestinationFolderId = null)
    {
        if (string.IsNullOrEmpty(filePath))
            throw new ArgumentException("Value cannot be null or empty.", nameof(filePath));

        FileInfo fileInfo;
        try
        {
            fileInfo = new FileInfo(filePath);
        }
        catch (Exception ex)
        {
            throw new ArgumentException("File not valid.", nameof(filePath), ex);
        }

        if (!fileInfo.Exists)
            throw new ArgumentException("File not exists.", nameof(filePath));

        using (var initiateResumableUploadSessionRequest = new HttpRequestMessage(HttpMethod.Post, GDriveFilesApiResumablePath))
        {
            UpdateGDriveTokenInfo();

            initiateResumableUploadSessionRequest.Headers.Authorization = new AuthenticationHeaderValue(_gDriveTokenInfo.TokenType, _gDriveTokenInfo.AccessToken);

            var jsonContent = new JObject(
                new JProperty("name", fileInfo.Name));

            if (!string.IsNullOrEmpty(gDriveUploadDestinationFolderId))
            {
                jsonContent.Add(new JProperty("parents", new JArray { gDriveUploadDestinationFolderId }));
            }

            initiateResumableUploadSessionRequest.Content = new StringContent(jsonContent.ToString(), Encoding.UTF8, "application/json");

            var initiateResumableUploadSessionResponse = GDriveClient.SendAsync(initiateResumableUploadSessionRequest).Result;

            if (initiateResumableUploadSessionResponse.StatusCode != HttpStatusCode.OK)
                throw new ExternalException(initiateResumableUploadSessionResponse.ToString());

            using (var uploadFileRequest = new HttpRequestMessage(HttpMethod.Put, initiateResumableUploadSessionResponse.Headers.Location))
            {
                uploadFileRequest.Content = new ByteArrayContent(File.ReadAllBytes(filePath));

                HttpResponseMessage uploadFileResponse;

                uploadFileResponse = GDriveClient.SendAsync(uploadFileRequest).Result;

                if (uploadFileResponse.StatusCode != HttpStatusCode.OK && uploadFileResponse.StatusCode != HttpStatusCode.Created)
                    throw new ExternalException(uploadFileResponse.ReasonPhrase);

                var uploadFileResponseBody = uploadFileResponse.Content.ReadAsStringAsync().Result;

                JObject uploadFileResponseJson = JObject.Parse(uploadFileResponseBody);

                return uploadFileResponseJson["id"].ToString();
            }
        }
    }

    public bool SetFilePermissions(string fileId, GDriveFileRole gDriveFileRole, GDriveFileType gDriveFileType)
    {
        if (string.IsNullOrEmpty(fileId))
            throw new ArgumentException("Value cannot be null or empty.", nameof(fileId));

        using (var setFilePermissionsRequest = new HttpRequestMessage(HttpMethod.Post, $"https://www.googleapis.com/drive/v3/files/{fileId}/permissions"))
        {
            UpdateGDriveTokenInfo();

            setFilePermissionsRequest.Headers.Authorization = new AuthenticationHeaderValue(_gDriveTokenInfo.TokenType, _gDriveTokenInfo.AccessToken);

            var jsonContent2 = new JObject(
                new JProperty("role", gDriveFileRole.ToString().ToLower()),
                new JProperty("type", gDriveFileType.ToString().ToLower()));

            setFilePermissionsRequest.Content = new StringContent(jsonContent2.ToString(), Encoding.UTF8, "application/json");

            HttpResponseMessage setFilePermissionsResponse = GDriveClient.SendAsync(setFilePermissionsRequest).Result;

            if (setFilePermissionsResponse.StatusCode != HttpStatusCode.OK)
                throw new ExternalException(setFilePermissionsResponse.ToString());
        }

        return true;
    }

    public GDriveFile GetFileInfo(string fileId)
    {
        using (var getFileInfoRequest = new HttpRequestMessage(HttpMethod.Get, $"https://www.googleapis.com/drive/v3/files/{fileId}?fields=name,webViewLink"))
        {
            UpdateGDriveTokenInfo();

            getFileInfoRequest.Headers.Authorization = new AuthenticationHeaderValue(_gDriveTokenInfo.TokenType, _gDriveTokenInfo.AccessToken);

            HttpResponseMessage getFileInfoResponse = GDriveClient.SendAsync(getFileInfoRequest).Result;

            if (getFileInfoResponse.StatusCode != HttpStatusCode.OK)
                throw new ExternalException(getFileInfoResponse.ToString());

            var getFileInfoResponseBody = getFileInfoResponse.Content.ReadAsStringAsync().Result;

            JObject getFileInfoResponseJson = JObject.Parse(getFileInfoResponseBody);

            return new GDriveFile
            {
                Id = fileId,
                Name = getFileInfoResponseJson["name"].ToString(),
                WebViewLink = getFileInfoResponseJson["webViewLink"].ToString()
            };
        }
    }

    private void UpdateGDriveTokenInfo()
    {
        lock (UpdateGDriveTokenInfoLocker)
        {
            if (_gDriveTokenInfo != null && !_gDriveTokenInfo.IsExpired())
            {
                return;
            }

            using (var refreshTokenRequest = new HttpRequestMessage(HttpMethod.Post, GDriveTokenApiPath))
            {
                refreshTokenRequest.Content = new FormUrlEncodedContent(_getTokenRequestContent);

                var getTokenRequestResponse = GDriveClient.SendAsync(refreshTokenRequest).Result;

                var jsonResponse = JObject.Parse(getTokenRequestResponse.Content.ReadAsStringAsync().Result);

                _gDriveTokenInfo = new GDriveTokenInfo((string)jsonResponse["access_token"], (int)jsonResponse["expires_in"], (string)jsonResponse["token_type"]);
            }
        }
    }

public class GDriveFile
{
    public string Id { get; set; }
    public string Name { get; set; }
    public string WebViewLink { get; set; }
}

public enum GDriveFileRole
{
    Owner,
    Organizer,
    FileOrganizer,
    Writer,
    Commenter,
    Reader
}

public enum GDriveFileType
{
    User,
    Group,
    Domain,
    Anyone
}

public class Program
{
    private static IGDriveApiV3Wrapper _gDriveApiV3Wrapper;

    private readonly string _gDriveUploadDestinationFolderId;

    public Program(IGDriveApiV3Wrapper gDriveApiV3Wrapper, string gDriveUploadDestinationFolderId = null)
    {
        _gDriveApiV3Wrapper = gDriveApiV3Wrapper;
        _gDriveUploadDestinationFolderId = gDriveUploadDestinationFolderId;
    }

    public string Upload(string filePath)
    {
            string fileId = _gDriveApiV3Wrapper.UploadFile(filePath, _gDriveUploadDestinationFolderId);

            _gDriveApiV3Wrapper.SetFilePermissions(fileId, GDriveFileRole.Reader, GDriveFileType.Anyone);

            GDriveFile gDriveFile = _gDriveApiV3Wrapper.GetFileInfo(fileId);

            return gDriveFile.WebViewLink;
    }
}

A: I'll assume that you are using Drive API v3. To set up permissions after uploading the file you have to use the method create. You can check the sharing documentation to know more about the different ways of creation of permissions and its different levels of access. After sharing the file, you can retrieve the link with the property webViewLink of the file. If you still have any doubts, please ask me for further clarification.

A: The Class GDrivenTokenInfo i don`t see its code


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Teen Challenge for Teenagers in Torrance, California
Home » Teen Challenge for Teenagers in Torrance, California
Teen Challenge in Torrance, California Enrolls Teens Who are Struggling with School, Lack of Care and Bad Behavior
Counter to traditional boarding schools near Torrance, California, Teen Challenge is uniquely equipped to work with struggling adolescents who are self-destructing or showing a lack of motivation, anger, dropping grades, or discord with family and others. As a Christian therapeutic boarding school, Teen Challenge believes enduring change takes place from the inside out.
Tuition at Teen Challenge programs is lower than most boarding schools for troubled teens near Torrance, California.
Mainly designed to be a therapeutic and life-transforming program, Teen Challenge boarding schools also excel in their accredited school with fully equipped classrooms and certified teachers on campus every day. Course work is transferable back to the child's home school and diplomas are awarded to teens who graduate while in a Teen Challenge school. Their dedication to helping teens grow behaviorally, spiritually, and in character is matched by their desire to provide boys and girls with quality education and quality care.
Teens Come to Teen Challenge Schools from Torrance, California
Adolescent boys and girls attend Teen Challenge from around the country, including Torrance, California. Most Teen Challenge students are from cities and towns located over 100 miles away from the campus. We find it valuable for teens to grow in a new environment well away from their old peer groups who may be near California.
Teen Challenge boarding schools near Torrance, California provide a dynamic and professional staff who wrap teens in love when they are at their worst. Teens who are in our care receive individual and group counseling and spiritual guidance. Teens progress through five phases of growth until they gain new attitudes and are ready to return to ordinary life at home. Outside of structured sessions, therapy takes place as teens work with our team of Christian mentors and staff during class time, recreation, meals, and even chores. Our staff stay with the teens in the dorms and humbly impart their lives out of a desire to positively affect the teens.
Teen Challenge Program's Students from Torrance, California Learn to Help Others As Well
An added major sign of growth is a desire to serve and share with others. Teen Challenge heavily encourages community participation and giving through serving. Teen Challenge teens serve one another, serve the community and learn to serve the world. Service takes place on campus; during field trips to shelters, churches, and parks; and in missions experiences such as building homes for the disadvantaged in 3rd world countries. Helping teens grow means teaching them how to give to others, and we work hard to lead by example. The volunteer work is completely charitable and unpaid.
Though You are Seeking a Teen Challenge Program in Torrance, California, Please Consider a Teen Challenge Boarding School in Another State, Since Every Teen Challenge Program Has its Own Strengths
Please think about looking outside of Torrance, California, to see how Teen Challenge's unique program can bring restoration to your family. Of the families we've served over the past 30 years, more than 80% say that they have experienced healing. To learn more, we invite you to explore each Teen Challenge program and school for boys and girls in Torrance, California, listed on the home page of this site.
If Teen Challenge boarding schools listed here are full or too far away for your teen, we'll help you find other Teen Challenge boarding schools near Torrance, California. We're here to help you either way.
More about Teen Challenge for teens in Torrance, California:
Torrance is a city incorporated in 1921 and located in the South Bay region of Los Angeles County, California, United States. Torrance has 1.5 miles of shore-front beaches on the Pacific Ocean, quieter and less well-known by tourists than others on the Santa Monica Bay, such as those of neighboring Redondo Beach and Manhattan Beach. Torrance enjoys a moderate year-round climate with warm temperatures, sea breezes, low humidity and an average rainfall of 12.55Â inches per year. The Torrance population was 145,438 at the 2010 census. This residential and light-high-tech industries city has 90,000 street trees and 30 city parks.
Excerpt about Teen Challenge in Torrance, California, used with permission from Wikipedia.
Teen Challenge is a Christian Boarding School Program for Troubled Teens in Torrance, California.
Teen Challenge for Teenagers in Compton, California Teen Challenge in Compton, California Exclusively Enrolls Troubled Teens Struggling with School, Poor Motivations and Bad Attitudes In addition to traditional boarding schools near Compton,…
Teen Challenge for Teenagers in Delano, California Teen Challenge in Delano, California Enrolls Teens Who are Struggling with Grades, Lack of Care and Bad Attitudes Counter to traditional boarding schools near Delano,…
Teen Challenge for Teenagers in Bakersfield, California Teen Challenge in Bakersfield, California Exclusively Enrolls Teens Who are Struggling with Grades, Lack of Care and Bad Behavior Unlike traditional boarding schools near Bakersfield,…
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Should you need help finding residential schools, Christian boarding schools, ranches for boys, boarding schools, faith-based boarding schools, or boarding schools, please let us know. Teen Challenge schools are for troubled teens. Teen Challenge offers Christian schools for troubled teens. Teen Challenge is much less costly than therapeutic boarding schools. Teen Challenge programs and boarding schools are nonprofit and their standards are set by Teen Challenge USA and Teen Challenge International.


text:
Ray Charles Had a Total of 12 Children With 10 Different Women
Michelle Obama Reveals She Has 'Low Grade Depression'
Silke Jasso, August 6, 2020 1:05 pm
AP Photo/Evan Vucci
Former first lady Michelle Obama revealed in the second episode of her podcast, The Michelle Obama Podcast, that she is "dealing with some form of low-grade depression." According to Obama, her depression is not only due to the quarantine but because of "the racial strife, and just seeing this administration, watching the hypocrisy of it, day in and day out, is dispiriting."
The former first lady discussed mental health amid the COVID-19 pandemic and ongoing protests over racial injustice after the death of George Floyd. Featured in the episode was journalist Michele Norris, a former anchor of NPR's All Things Considered. Obama blames, in part, the Trump administration's response to current events going on nationwide for her depression. She stated,
"I don't think I'm unusual in that. But I'd be remiss to say that part of this depression is also a result of what we're seeing in terms of the protests, the continued racial unrest, that has plagued this country since its birth. I have to say that waking up to the news, waking up to how this administration has or has not responded, waking up to yet another story of a Black man or a Black person somehow being dehumanized, or hurt or killed, or falsely accused of something, it is exhausting. And it has led to a weight that I haven't felt in my life, in a while."
Obama stated she deals with her depression by keeping a packed routine, something she learned to do during her years in the White House. But, she noted she struggled at times with her exercise and sleep schedule. The former first lady reportedly wakes up in the middle of the night worrying about something or feeling "a heaviness." She noted, "You know, I've gone through those emotional highs and lows that I think everybody feels, where you just don't feel yourself, and sometimes there's been a week or so where I had to surrender to that, and not be so hard on myself. And say, 'You know what? You're just not feeling that treadmill right now.'"
She went on to say that managing one's emotional lows and highs require one to know themselves and doing things that bring you joy. For her, her spirit is lifted when she is feeling healthy, and when she is surrounded by good people. She also noted the Obamas have all been gathered as a family and usually do puzzles most nights during the Coronavirus pandemic quarantine. Obama's first episode of her new podcast, which you can listen to on Spotify, was a conversation about community with former President Barack Obama.
Watch: Michelle Obama Named 2019?s ?Most Admired Woman? in the World
About the author: Silke Jasso, Editor
Silke Jasso is a bilingual editor, writer, producer, and journalist specialized in online media. Born in Laredo Texas, her previous work includes LareDOS Newspaper where she was an editor and writer and Entravision Communications where she was a Co-Anchor and Multi-Media Journalist for Fox39 News and Univision 27.


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text:
36-year-old midfielder considering move to PL rivals amid talk of Spurs reunion
To The Lane And Back
According to SuperDeporte (h/t Daily Star), Real Madrid star and former Tottenham Hotspur player Luka Modric is contemplating a move to Manchester City after his current contract with Los Blancos ends. It is believed that the Croatian international would also snub the chance to join Newcastle United and a chance to reunite with Spurs.
The 36-year-old midfielder has established himself as one of the best footballers in recent times. He has been a great servant to Real Madrid. He played 404 matches across all competitions for the Spanish club during which time he scored 28 goals and provided 65 assists.
The former Dinamo Zagreb player won the Ballon d'Or in 2018 which has only cemented his position amongst the best. He grew in stature during his time with the Lilywhites. Modric played 160 games for Spurs across all competitions during which time he scored 17 goals and provided 27 assists.
Luka Modric is supposedly open to joining Manchester City
Modric's current contract with Madrid will expire at the end of the season. It is believed that Florentino Perez has been working on a new deal but talks have hit a wall due to Real's policy of offering only a one-year extension to players above the age of 30.
While the Croatian international was an integral part of Spurs during his time in England, one could argue that he would be way past his prime at 36. Any move to the Premier League would now see a different Modric as compared to the one we were accustomed to seeing at White Hart Lane.
Luka Modric is a Tottenham Hotspur icon
That being said he would bring a great level of experience to any team after playing at an elite level for such a long time. A chance to bring him back at Tottenham should be weighed in its pros and cons as he would be another significant name added to the wage bill.
More Tottenham Hotspur News
While it may be too early to rule out any possibility, it would be interesting to see him return to the PL in whatever capacity it may be. Certainly an adored icon at Spurs, one could assume that the fanbase would not be best pleased if he decides to go to the Etihad.
As for us, it's better to invest any funds that we have on players that can offer us a number of years at the top. It is not wrong to say that footballers, around the age that Modric is right now, start to decline on a physical level. We must look for long-term solutions under Conte if we are serious about building something special at N17.


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Seahawks Players Can't Listen to Future at Practice Because of Ciara
Josh Dhani September 14, 2015 0
Freebandz is running the streets right now, but Seattle Seahawks players will only hear him on their MP3 players or car stereo's.
The Seahawks understandably want Future to be played before games and practices, but the resident DJ says he can't do it.
Here is his reasoning, per Billboard:
This year, I've been getting a lot of requests for Future. But that dynamic is weird too, because I can barely play Future with Ciara running around and that's her baby's dad. It's just that he has a dope-ass album out right now, players like to hear it, it definitely gets you pumped up — even though it's low-tempo, it's still stuff that you want to hear — and then you've got Ciara running up with little baby Future and she's dating Russell so it's like, "Oh f—."
Only the Seahawks know if this will end up being a messy situation, or a DJ making a big deal out of nothing.
By Glenn Erby
Celebrities Ciara Culture Future Glenn Erby Latest Music NFL Russell Wilson Seattle Seahawks Trash Talk
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