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## Transparent Machine Learning Algorithm Offers Useful Prediction Method for Natural Gas Density | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Gas Processing Journal | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

مقاله 1، دوره 6، شماره 2، زمستان 2018، صفحه 1-14
اصل مقاله (834.18 K)
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نوع مقاله: Original Article | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

شناسه دیجیتال (DOI): 10.22108/gpj.2018.112015.1035 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

نویسندگان | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

David Wood ^{} ^{} ^{1}؛ Abouzar Choubineh^{2}
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^{1}Professor DWA Energy Limited Lincoln, United Kingdom | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

^{2}MSc Petroleum University of Technology, Ahwaz, Iran | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

چکیده | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Machine-learning algorithms aid predictions for complex systems with multiple influencing variables. However, many neural-network related algorithms behave as black boxes in terms of revealing how the prediction of each data record is performed. This drawback limits their ability to provide detailed insights concerning the workings of the underlying system, or to relate predictions to specific characteristics of the underlying variables. The recently proposed transparent open box (TOB) learning network algorithm successfully addresses these issues by revealing the exact calculation involved in the prediction of each data record. That algorithm, described in summary, can be applied in a spreadsheet or fully-coded configurations and offers significant benefits to analysis and prediction of many natural gas systems. The algorithm is applied to the prediction of natural gas density using a published dataset of 693 data records involving 14 variables (temperature and pressure plus the molecular fractions of the twelve components: methane, ethane, propane, 2-methylpropane, butane, 2-methylbutane, pentane, octane, toluene, methylcyclopentane, nitrogen and carbon dioxide). The TOB network demonstrates very high prediction accuracy (up to R ^{2} =0.997), achieving comparable accuracy to the predictions reported (R^{2} =0.995) for an artificial neuralnetwork (ANN) algorithm applied to the same data set. With its high levels of transparency, the TOB learning network offers a new approach to machine learning as applied to many natural gas systems. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

کلیدواژهها | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Predicting gas density؛ Learning networks؛ Multi-component natural gas؛ Auditable machine learning؛ Transparent predictions | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

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The employment of machine learning algorithms to provide accurate predictions from complex systems governed by multiple variables with poorly defined non-linear relationships is growing. The use of system learning tools, such as artificial neural networks (ANN), adaptive neuro-fuzzy inference systems (ANFIS), support vector machines (SVM), least squares support vector machine (LSSVM), etc., are being ever more widely applied as systems learning tools (Schmidhuber, 2015). The learning potential of ANN was recognized in the 1950’s (Kleene, 1956) and has developed with a number of different algorithms now routinely exploited, such as the multilayer perceptron (MLP) (Hush & Horne, 1993; Haykin, 1995) and radial basis functions (RBF) (Broomhead & Lowe, 1988). Since its development in the 1990s ANFIS adapts ANN with a Takagi–Sugeno fuzzy inference system (Jang, 1993) and has successfully demonstrated its learning capabilities when applied to approximate and uncertain non-linear functions (Jang, Sun, & Mizutani, 1997). SVM and LSSVM algorithms, also developed in the 1990s, provide supervised learning that is successfully applied as nonlinear regression and correlation analysis (Cortes & Vapnik, 1995; Vapnik, 2000). These machine learning algorithms are now commonly applied to provide predictions to many non-linear systems, including those in the gas and oil industries. Moreover, they are also widely used in a hybrid form, coupled with various optimization algorithms, such as genetic algorithms to improve their performance (Ghorbani, Ziabasharhagh, & Amidpour, 2014; Ghorbani, Hamedi, Shirmohammadi, Mehrpooya, & Hamedi, 2016) and data handling capabilities (Ghorbani, Shirmohammadi, Mehrpooya, & Hamedi, 2018; Shirmohammadi, Ghorbani, Hamedi, Hamedi, & Romeo, 2015; Shirmohammadi, Soltanieh, & Romeo, 2018; Choubineh, Ghorbani, Wood, Moosavi, Khalafi, & Sadatshojaei, 2017). However, in academia and industry, the extensive exploitation of machine learning algorithms in their many hybrid forms has polarized scientists, particularly in the oil and gas industry. Perhaps the most-contentious issue is the lack of transparency provided by neural networks regarding their inner calculations, particularly the relative weightings and adjustments made to input variables in deriving specific predictions. This often leads to them being used and viewed as blackboxes (Heinert, 2008). It requires complex and sometimes cumbersome simulations to gain insight to the ways variables are treated in their calculations. At best this turns them into “white boxes” that provide insight to the relative influences of input variables on the calculations being made (Elkatatny, Tariq, & Mahmoud, 2016). This black-box condition frustrates and infuriates some scientists and industry practitioners. If it is not possible to see, quickly and in detail, how a prediction is derived from a machine learning tool, and no new fundamental insight is provided about the underlying system, then, for example, many experimental scientists see no value in such systems. Those in this camp when reviewing machine-learning studies simply dismiss them as correlation analysis of minor importance with no experimental justification. On the other hand, some oil and gas companies and their suppliers/ service companies are comfortable with a black-box approach as in some circumstances it can enhance their competitive advantage and keep their underlying data analysis confidential. Some researchers also embrace the black-box condition by their willingness to just enter the input-variable data into opaque coding (e.g., some MatLab machine-learning functions), derive accurate correlations and predictions for their objective function and make bold claims concerning the superiority of their newly developed algorithms. This leaves many practitioners blind to the inner workings of such systems. Nevertheless, the uptake of machine learning and the diversity of its applications continue to grow and have more impact on the way decisions are taken and real-time actions determined in the field. What is urgently required are more-transparent machine learning tools that raise awareness about their underlying systems rather than obscure them. A recently-proposed algorithm, the Transparent Open-Box (TOB) network (Wood, 2018) demonstrates that it is possible to do this in such way that sufficient prediction accuracy is provided at the same time as revealing the inner network calculations involved in deriving the prediction for each data record. In the natural gas industry density of natural gas (ρ) is dependent on several complex non-linear relationships relating to its physical and chemical characteristics. Its prediction from underlying physiochemical conditions makes it suitable for machine learning applications. Natural gas density is an important metric contributing to the calculations of other variables relevant to numerous systems involving natural gas (e.g. pipelines, storage facilities, and underground reservoirs). It is complex because it varies significantly with respect to pressure, temperature and gas composition (AlQuraishi & Shokir, 2011). However, measuring it experimentally is time-consuming and expensive, and estimating it from PVT data involves significant assumptions about related metrics, which are difficult to define with accuracy (e.g. z-factors). Although various equations of state (EOS) are proposed for calculating ρ they have proved to be too simple and inconsistent across the full P-T and compositional ranges encountered (Elsharkawy, 2003; Farzaneh-Gord, Khamforoush, Hashemi, & Pourkhadem, 2010). Shokir (2008) proposed a fuzzy logic method and AlQuraishi and Shokir (2011) developed the probabilistic alternating conditional expectations model to predict ρ. Since then several machine learning algorithms have been applied to accurately predictρ with various machine learning algorithms applied to various medium-sized and large databases. These include: ANN (AlQuraishi & Shokir, 2009); LSSVM (Esfahani, Baselizadeh, & Hemmati-Sarapardeh, 2015); ANN-TLBO (Choubineh, Khalafi, Kharrat, Bahreini,& Hosseini, 2017); and, ANFIS (Dehaghani & Badizad, 2017). These studies have typically achieved accuracies of predicted versus measured data with coefficients of determination of about 0.99 and very low values of statistical error measures (e.g., root mean squared error). Here, we describe the methodology and mathematical basis of the TOB algorithm and demonstrate its application benefits using, as an example, a complex non-linear natural gas system for predicting natural gas density from mole fractions of gas composition, together with its temperature and pressure. We have selected a comprehensive published dataset (Atilhan, Aparicio, Karadas, Hall, & Alcade, 2012) of experimental measurements performed on Qatar North Field natural gas samples (693 data records), because it has been previously used for published ANN study (Choubineh, Khalafi, Kharrat, Bahreini, & Hosseini, 2017) (to predict ρ). Our objective is to show that the TOB algorithm is capable of producing comparable accuracy for predicting from this dataset as the ANN study with the additional benefit of providing transparency to each individual prediction it calculates. We make no claims that the TOB algorithm can outperform other more mathematically-complex machine-learning algorithms (ANN, ANFIS, LSSVM etc.) in terms of prediction accuracy, but rather that it can achieve acceptable levels of accuracy with the additional benefit of greater transparency.
The TOB network approach was proposed and outlined by Wood (2018). There are 14 steps, divided into two stages (stage 1 and stage 2), involved in applying the TOB learning network algorithm (2018). These sequences of steps are explained here with the sequence of steps summarized in a flow diagram (Figure 1).
Where:
X for the i data record^{th}
X for the i data record^{th}
Where: is the value of variable is the value of variable is the squared error of variable k data record of the training subset.^{th}Then sum the
Where: is the squared error for variable k data record of the training subset.^{th}is the sum of the squared errors for all k data record of the training subset.^{th}
Use values as the basis for ranking the matches in the training subset in ascending order of (∑VSE) for each tuning subset record.
j data record. That fractional contribution is calculated by Eq. (4) to Eq. (6) and is proportionalto their relative ∑^{th}VSE scores for the j data record.^{th}
Where:
j record in the tuning subset calculated such that Eq.(5) applies.^{th}
In order to ensure that the matching record with the lowest value contributes most to the dependent variable prediction for the
Where: is the dependent variable for the j data record in the tuning subset.^{th}is the initially predicted value for the dependent variable for the Applying Eq. (6) ensures that the rank#1 in the training subset top-matching records contributes most to the predicted values. On the other hand, the rank #Q match in the training subset contributes least to the dependent-variable prediction for the
Where:
j data record in the tuning subset^{th}is the actual value of the dependent variable for the is the predicted value of the dependent variable for the is the average actual value of the dependent variable for all This step represents the end of TOB stage one of the prediction process. Step 10 provides a provisionally-tuned TOB network that provides predictions based upon uniform weighting (as described in Step 7) applied to all the variables and by matching data records with those in the training subset. The TOB stage 2 involves applying optimization to improve the accuracy of the predictions for the tuning set as a whole. TOB stage 2 also tests the optimized prediction metrics with the yet-to-be- used independent testing subset.
1. The weights ( N input variables in Eq. (3) are allowed to vary independently between values 0 and 1. This contrasts with Step 7 of stage one of the algorithm, where all the weights were initially set to the same constant number between 0 and 1. Also, in Step 11 the dependent variable (identified as variable N+1) is not involved in the optimization as it is considered as an unknown, so a zero weight is applied to it. Sometimes, very-low weights (e.g. 1.0 E-10 or less) may be selected as optimum weights for certain input variables. This very-low-weight value does not mean that such a variable is insignificant in the optimum solution. Their non-zero values, albeit small, will contribute to selecting the relative contributions of each of the top-matching records in the predictions made. This point is illustrated for an example data record from the dataset evaluated.2. The integer values of Q (how many of the top-matching records to include in the predictions) is allowed to vary in Eqs. (4), (5) and (6). Typically, 2<=Q<=10 is the range within which Q is allowed to vary and the optimizer selects the best value of Q from that range. Q values of higher than 10 could be used. However, the experience of applying the algorithm to multiple datasets suggests that all the top-ten matching records are not used in the optimum solutions found by the optimizer. In this study, the standard “Solver” optimizer in Microsoft Excel is used to conduct the optimization process. Specifically, it is the GRG (Generalized Reduced Gradient) algorithm option within the Solver function that is used. GRG applies a robust non-linear-programming algorithm (Farzaneh-Gord, Khamforoush, Hashemi, & Pourkhadem, 2010). GRG is setup to “multistart” (i.e., run multiple cases each with a population of 150) and to converge to a solution value of 0.0001, if possible, for the RMSE objective function. GRG can be run directly from an Excel worksheet or as part of a visual basic for applications (VBA) code in Excel. It is possible to use other fully-coded optimizers to achieve this, but the advantages of doing it in Excel for mid-sized dataset is explained. The optimization process accepts the top-Q matches in the training subset for each data record in the tuning subset established by step 8 of TOB stage 1. However, in TOB stage two it re-evaluates the scores using Eq. (3) by varying Q in each iteration of the optimizer.
In summary, TOB Stage 1 involves constructing a network of initial record matches from a large training subset to the individual records of a much smaller tuning subset of data records. That first stage yields a provisional prediction for the dependent variable which can usually be significantly improved upon by the optimization applied in TOB stage 2. TOB stage 1involves standard matching and ranking algorithms between an unknown record and the multiple records in the larger training subset. That training subset should typically be comprised of more than about 70% of all the data records available. In order to obtain reliable predictions across the entire PODV-value range covered by the dataset, the records included in the tuning subset and the testing subset should be distributed across the full range displayed by the dataset. It is also appropriate for the data records with the minimum and maximum PODV values to be placed in the training subset. These requirements mean that the division of the data records between the data subsets is not conducted randomly, as that might lead to sparse data coverage in certain PODV-value ranges in the training and tuning subsets. The simple steps of TOB Stage 1 (steps 1 to 10) often generate predictions for the dependent variable of credible but sub-optimal accuracy. This highlights which data records in the training subset should be the focus of more detailed analysis for each data record in the tuning subset. Stage 1 can often achieve impressive levels of accuracy from highly non-linear input data distributions. TOB stage 2: (steps 11 to 12) applies optimization to refine and tune the predictions derived from TOB stage 1. A comparison of the prediction results from stage 1 and stage 2 can typically reveal the respective contributions of each stage to the accuracy of the final predictions derived. Once the optimized-tuning process is completed, and the optimum tuned values of The TOB learning network can be applied using spreadsheets (e.g. Excel workbooks), which is a suitable approach for small to mid-sized data sets. It can also be set up in fully-coded formats or as a hybrid code plus spreadsheet configurations. The spreadsheet and hybrid alternatives have the attraction that the standard built-in spreadsheet optimizers can be exploited (e.g. the generalized reduced gradient, GRG, and evolutionary optimizers of Excel’s Solver optimization function). That approach enables the final steps of the TOB Stage 1 and the Stage 2 prediction calculations to be displayed as simple and easily-audited formulas for each data record in the spreadsheet cells. For large datasets it is more efficient to code the TOB algorithm with suitable mathematical coding languages (i.e., Octave, R, Python, MatLab etc.). To predict ρ from the compiled natural gas dataset (693 data records) evaluated in this study, a hybrid VBA-Excel spreadsheet configuration is used. The TOB subsets (training, tuning and testing) are initially displayed in Excel with some calculations conducted using spreadsheet formula (e.g. statistical metrics for all variables). Visual Basic for Applications (VBA) coding is then used to normalize, rank, and match the data records of the tuning and training subsets (TOB stage 1). The VBA code places the top-ten ranked matches for each tuning subset data record into an Excel sheet cell. This enables the final TOB Stage 2 optimization calculations to be conducted with Excel cell formula, enabling the Solver optimizer(s) (Frontline Solvers. Standard Excel Solver, 2018) to be applied. This approach enhances the transparency and insight to the dataset compared to the fully-coded method.
A TOB network is used to predict ρ from the dataset of experimental measurements performed on Qatar North Field natural gas samples (693 data records) published by Atilhan et al. (2012). The data records cover a temperature range of 250 to 450 K and a pressure range of 15 to 65 MPa. They also include compositional data for each data record in the form of mole fractions for the 12 components: methane, ethane, propane, 2-methylpropane, butane, 2-methylbutane, pentane, octane, toluene, methylcyclopentane, nitrogen and carbon dioxide. A value range and mean for each variable in the full 693-record data set is provided in Table 1.
The dataset is divided into training (532 data records; 77% of the complete dataset), tuning (90 data records; 13% of the complete dataset) and testing subsets (71 data records; 10% of the complete dataset) for detailed TOB network analysis. The relationships between the key variables, P and T and ρ for the training subset are illustrated in Figures 2 A to D demonstrating the significant non-linearity and irregularity in the relationships among these variables across the entire dataset. Table 2 and Figures 3 and 4 show the results obtained from applying the TOB to this dataset: 1) up to step 10 (evenly-weighted-variable contributions to POV prediction) for the configured tuning set; 2) up to step 12 for the optimized tuning set; 3) up to step 12 applying the optimized TOB settings to the testing subset.
The algorithm was applied to this dataset using the combination of an Excel spreadsheet for steps 10 to 14 (enabling the use of Solver’s GLG and evolutionary optimization functions) and VBA code to handle the ranking sorting normalization, record matching, and selection (steps 1 to 8).
The results reveal that the TOB algorithm can achieve very high levels of prediction accuracy (RMSE= 5.6; R= 0.995. Both TOB and ANN provide superior ρ predictions than the published correlations applied to the same dataset [Azizi, Behbahani, & Isazadeh, 2010;Sanjari & Lay, 2012). RMSE and^{2 } R achieved by Azizi et al.’s (2010) correlation were 59.18 and 0.7, respectively. Gas density predicted using that model (Azizi, Behbahani, & Isazadeh, 2010) for the low-density range areis reasonable. However, that correlation significantly overestimates ρ in the higher-density range. Sanjari and Lay’s (2012) correlation model, achieved a better gas density prediction performance (RMSE=12.6;^{2} R0.97than the Azizi et al.’s (2010) correlation. Although Sanjari and Lay’s (2012) model estimates gas density values lower than 340 kg/m^{2}=^{3} with reasonable precision, the values diverge greatly from the unit slope line (y = x) for values in the range of 340–450 kg/m^{3}, indicating the limitations of that model in that density range.The proposed TOB model achieves a high level of prediction accuracy while also being able to display the exact prediction calculation for each record in each subset (i.e., identify which of the top-ranking matching records are involved and their fractional contributions to the prediction value). The algorithm achieves most of this in steps 1 to 10 (i.e., TOB stage 1) for this data set (achieving Table 2 highlights the results of optimization and sensitivity analysis by varying the Q value from 10to 2. The optimum Q value for this data set is 3 (yielding the minimum RMSE value). For values of Q below 3, the accuracy of the model is impaired slightly, suggestive of under fitting. The impact of varying Q on the predictions is caused by subtle changes in the weightings applied to variables T and P. As it happens for this data set the optimum weightings for these two variables in gas density prediction are close in relative magnitude (e.g. 0.5:0.5). That is why the TOB stage 1 provisional prediction managed to achieve such high prediction accuracy because it applied 0.5 weightings to all 14 input variables. As shown in Table 2, the ratio of the weightings for T and P (w On the positive side, the TOB methodology provides transparency to the specific calculations involved in each prediction it makes and it achieves credible levels of prediction accuracy. On the negative side, TOB cannot extrapolate its predictions beyond the minimum and maximum values of the dependent variable covered by data records in the training subset, which many other machine-learning algorithms can do. It also cannot achieve highly accurate predictions in sparsely populated regions of a training sunset. This is not necessarily a bad limitation as it inhibits the algorithm from over fitting sparse datasets; a criticism often leveled against other machine learning algorithms. We believe that these attributes make the TOB algorithm a complementary addition to the suite of existing machine-learning algorithms and justify its use in conjunction with other machine learning algorithms to provide greater transparency to the prediction process.
The Transparent Open-Box (TOB) learning network provides a valuable tool for evaluating and deriving predictions from complex, non-linear natural-gas systems. It offers advantages and complementary capabilities to the more-traditional machine-learning networks in that: - · all its intermediate calculations and relationships are fully auditable and accessible;
- · relative weightings applied to variables in optimized solutions are clearly revealed;
- · it performs well with standard optimizers (e.g., Excel’s Solver options) and can be also be linked easily to customized optimization algorithms;
- · varying its Q-factor values readily identifies under-fitted versus over-fitted solutions
We recommend that what can be achieved in terms of prediction-performance accuracy by the transparent open-box network algorithmshould be useful as a performance benchmark when applying less-transparent machine-learning algorithms to specific datasets. There are many natural gas datasets to which the TOB (e.g. PVT, drilling, well-log data, reservoir and source rock analysis) learning network could be readily applied and provide more enlightening analysis and predictions than the black boxes currently applied to them. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

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