Modeling and Experimental Investigation of Parabolic Trough Solar Collector

In this thesis, a mathematical model is applied to study the performance of a parabolic trough solar collector (PTSC). The proposed model consists of three parts. The first part is a solar radiation model. In this section, the amount of solar radiation incident upon Earth is estimated using equations and relationships between the sun and the Earth. The second part is the optical model. This part has the ability to determine the optical efficiency of PTSC throughout the daytime. The last part is the thermal model. The aim of this section is to estimate the amount of energy collected by the working fluid, heat loss, thermal efficiency, and outlet temperature. All heat balance equations and heat transfer mechanisms: conduction, convection, and radiation, have been incorporated. The proposed model is implemented in MATLAB software. In addition, an experimental investigation was conducted on parabolic trough solar collector. The test was carried out at Embry-Riddle Aeronautical University, Daytona Beach, FL on February 19, 2014. A test rack was used during the experimentation. It contains a circulating pump, a storage tank, a heat exchanger (radiator), and flow meter. Thermocouples were used to measure the inlet and outlet temperature of PTSC (the second trough). Water is used as a working fluid. After data were recorded, thermal performance analysis was performed. The results indicate that a maximum temperature of 48 was achieved and a maximum efficiency of 30 % was obtained. Lastly, comparisons between the experimental and modeled results have been carried out for validation purpose. The results show acceptable agreement even though there are some variances. This deviation is accounted for in the heat loss from the connectors, supporting brackets, location of thermocouples, accuracy of thermocouples and thermocouple reader, and accuracy of heat transfer equations

Photoelectric Factor Prediction Using Automated Learning and Uncertainty Quantification

The photoelectric factor (PEF) is an important well logging tool to distinguish between different types of reservoir rocks because PEF measurement is sensitive to elements with high atomic number. Furthermore, the ratio of rock minerals could be determined by combining PEF log with other well logs. However, PEF log could be missing in some cases such as in old well logs and wells drilled with barite-based mud. Therefore, developing models for estimating missing PEF log is essential in those circumstances. In this work, we developed various machine learning models to predict PEF values using the following well logs as inputs: bulk density (RHOB), neutron porosity (NPHI), gamma ray (GR), compressional and shear velocity. The predictions of PEF values using adaptive-network-fuzzy inference system (ANFIS) and artificial neural network (ANN) models have errors of about 16% and 14% average absolute percentage error (AAPE) in the testing dataset, respectively. Thus, a different approach was proposed that is based on the concept of automated machine learning. It works by automatically searching for the optimal model type and optimizes its hyperparameters for the dataset under investigation. This approach selected a Gaussian process regression (GPR) model for accurate estimation of PEF values. The developed GPR model decreases the AAPE of the predicted PEF values in the testing dataset to about 10% AAPE. This error could be further decreased to about 2% by modeling the potential noise in the measurements using the GPR model

Ensemble learning model for petroleum reservoir characterization: A case of feed-forward back-propagation neural networks

Conventional machine learning methods are incapable of handling several hypotheses. This is the main strength of the ensemble learning paradigm. The petroleum industry is in great need of this new learning methodology due to the persistent quest for better prediction accuracies of reservoir properties for improved exploration and production activities. This paper proposes an ensemble model of Artificial Neural Networks (ANN) that incorporates various expert opinions on the optimal number of hidden neurons in the prediction of petroleum reservoir properties. The performance of the ensemble model was evaluated using standard decision rules and compared with those of ANN-Ensemble with the conventional Bootstrap Aggregation method and Random Forest. The results showed that the proposed method outperformed the others with the highest correlation coefficient and the least errors. The study also confirmed that ensemble models perform better than the average performance of individual base learners. This study demonstrated the great potential for the application of ensemble learning paradigm in petroleum reservoir characterizatio

Ensemble model of non-linear feature selection-based Extreme Learning Machine for improved natural gas reservoir characterization

The deluge of multi-dimensional data acquired from advanced data acquisition tools requires sophisticated algorithms to extract useful knowledge from such data. Traditionally, petroleum and natural gas engineers rely on “rules-of-thumb” in the selection of optimal features with much disregard to the hidden patterns in operational data. The traditional multivariate method of feature selection has become grossly inadequate as it is incapable of handling the non-linearity embedded in such natural phenomena. With the application of computational intelligence and its hybrid techniques in the petroleum industry, much improvement has been made. However, they are still incapable of handling more than one hypothesis at a time. Ensemble learning offers robust methodologies to handle the uncertainties in most complex industrial problems. This learning paradigm has not been well embraced in petroleum reservoir characterization despite the persistent quest for increased prediction accuracy. This paper proposes a novel ensemble model of Extreme Learning Machine (ELM) in the prediction of reservoir properties while utilizing the non-linear approximation capability of Functional Networks to select the optimal input features. Different instances of ELM were fed with features selected from different bootstrap samplings of the real-life field datasets. When benchmarked against existing techniques, our proposed ensemble model outperformed the multivariate regression-based feature selection, the conventional bagging and the Random Forest methods with higher correlation coefficient and lower prediction errors. This work confirms the huge potential in the capability of the new ensemble modeling paradigm to improve the prediction of reservoir properties

Improving the prediction of petroleum reservoir characterization with a stacked generalization ensemble model of support vector machines

The ensemble learning paradigm has proved to be relevant to solving most challenging industrial problems. Despite its successful application especially in the Bioinformatics, the petroleum industry has not benefited enough from the promises of this machine learning technology. The petroleum industry, with its persistent quest for high-performance predictive models, is in great need of this new learning methodology. A marginal improvement in the prediction indices of petroleum reservoir properties could have huge positive impact on the success of exploration, drilling and the overall reservoir management portfolio. Support vector machines (SVM) is one of the promising machine learning tools that have performed excellently well in most prediction problems. However, its performance is a function of the prudent choice of its tuning parameters most especially the regularization parameter, C. Reports have shown that this parameter has significant impact on the performance of SVM. Understandably, no specific value has been recommended for it. This paper proposes a stacked generalization ensemble model of SVM that incorporates different expert opinions on the optimal values of this parameter in the prediction of porosity and permeability of petroleum reservoirs using datasets from diverse geological formations. The performance of the proposed SVM ensemble was compared to that of conventional SVM technique, another SVM implemented with the bagging method, and Random Forest technique. The results showed that the proposed ensemble model, in most cases, outperformed the others with the highest correlation coefficient, and the lowest mean and absolute errors. The study indicated that there is a great potential for ensemble learning in petroleum reservoir characterization to improve the accuracy of reservoir properties predictions for more successful explorations and increased production of petroleum resources. The results also confirmed that ensemble models perform better than the conventional SVM implementation

A least-square-driven functional networks type-2 fuzzy logic hybrid model for efficient petroleum reservoir properties prediction

Various computational intelligence techniques have been used in the prediction of petroleum reservoir properties. However, each of them has its limitations depending on different conditions such as data size and dimensionality. Hybrid computational intelligence has been introduced as a new paradigm to complement the weaknesses of one technique with the strengths of another or others. This paper presents a computational intelligence hybrid model to overcome some of the limitations of the standalone type-2 fuzzy logic system (T2FLS) model by using a least-square-fitting-based model selection algorithm to reduce the dimensionality of the input data while selecting the best variables. This novel feature selection procedure resulted in the improvement of the performance of T2FLS whose complexity is usually increased and performance degraded with increased dimensionality of input data. The iterative least-square-fitting algorithm part of functional networks (FN) and T2FLS techniques were combined in a hybrid manner to predict the porosity and permeability of North American and Middle Eastern oil and gas reservoirs. Training and testing the T2FLS block of the hybrid model with the best and dimensionally reduced input variables caused the hybrid model to perform better with higher correlation coefficients, lower root mean square errors, and less execution times than the standalone T2FLS model. This work has demonstrated the promising capability of hybrid modelling and has given more insight into the possibility of more robust hybrid models with better functionality and capability indices

Evaluation of Barium Sulfate (Barite) Solubility Using Different Chelating Agents at a High Temperature

Barium sulfate (barite) is one of the widely used weighting materials in the preparation of drilling fluid for deep oil and gas wells. Barite is not soluble in the regular solvents; such as, hydrochloric acid (HCl) and other acids. Therefore, in this study, we focused on evaluating the dissolution of the industrial barite particles in different chelating agents. Chelating agents; such as, diethylene triamine penta acetic (DTPA), ethylene diamine tetra acetic (EDTA), and hydroxyethyl ethylene diamine tri acetic (HEDTA) acids with high and low pH values were used in this study. The effect of the base of chelating agents, namely alkali and alkali earth hydroxides, on the dissolution of barite was also investigated. For the first time, the optimum pH, concentration, and base (sodium or potassium) of chelating agents which yielded the maximum dissolution were investigated in this study at a high temperature. Previous studies did not consider the wellbore constraints during their experiments and they used chelating agent volume to barite weight ratio, which could not be implemented in the real wells. In this study and for the first time, we considered the wellbore volume (chelating agent volume that can be used) and the barite weight (filter cake) during the dissolution experiments. Based on the results obtained from this study, potassium base DTPA-K5 and EDTA-K4 of a concentration of 20 wt.% were found to be the most effective chelating agents to dissolve barite. The solubility of barite was found to be 26.8 g/L in a solution containing 20 wt.% of DTPA-K5 and 25.6 g/L in a solution containing 20 wt.% of EDTA-K4 during a soaking time of 24 hrs and a pH value above 11 at 200°F

Modeling the permeability of carbonate reservoir using type-2 fuzzy logic systems

In this work, the use of type-2 fuzzy logic systems as a novel approach for predicting permeability from well logs has been investigated and implemented. Type-2fuzzylogicsystem is good in handling uncertainties, including uncertainties in measurements and data used to calibrate the parameters. In the formulation used, the value of a membership function corresponding to a particular permeability value is no longer a crisp value; rather, it is associated with a range of values that can be characterized by a function that reflects the level of uncertainty. In this way, the model will be able to adequately account for all forms of uncertainties associated with predicting permeability from well log data, where uncertainties are very high and the need for stable results are highly desirable. Comparative studies have been carried out to compare the performance of the proposed type-2fuzzy logic system framework with those earlier used methods, using five different industrial reservoir data. Empirical results from simulation show that type-2fuzzylogic approach outperformed others in general and particularly in the area of stability and ability to handle data in uncertain situations, which are common characteristics of well logs data. Another unique advantage of the newly proposed model is its ability to generate, in addition to the normal target forecast, prediction intervals as its by-products without extra computational cost

Intelligent Prediction of Minimum Miscibility Pressure (MMP) During CO2 Flooding Using Artificial Intelligence Techniques

Carbon dioxide (CO2) injection is one of the most effective methods for improving hydrocarbon recovery. The minimum miscibility pressure (MMP) has a great effect on the performance of CO2 flooding. Several methods are used to determine the MMP, including slim tube tests, analytical models and empirical correlations. However, the experimental measurements are costly and time-consuming, and the mathematical models might lead to significant estimation errors. This paper presents a new approach for determining the MMP during CO2 flooding using artificial intelligent (AI) methods. In this work, reliable models are developed for calculating the minimum miscibility pressure of carbon dioxide (CO2-MMP). Actual field data were collected; 105 case studies of CO2 flooding in anisotropic and heterogeneous reservoirs were used to build and evaluate the developed models. The CO2-MMP is determined based on the hydrocarbon compositions, reservoir conditions and the volume of injected CO2. An artificial neural network, radial basis function, generalized neural network and fuzzy logic system were used to predict the CO2-MMP. The models’ reliability was compared with common determination methods; the developed models outperform the current CO2-MMP methods. The presented models showed a very acceptable performance: the absolute error was 6.6% and the correlation coefficient was 0.98. The developed models can minimize the time and cost of determining the CO2-MMP. Ultimately, this work will improve the design of CO2 flooding operations by providing a reliable value for the CO2-MMP

Predicting Petroleum Reservoir Properties from Downhole Sensor Data using an Ensemble Model of Neural Networks

The acquisition of huge sensor data has led to the advent of the smart field phenomenon in the petroleum industry. A lot of data is acquired during drilling and production processes through logging tools equipped with sub-surface/down-hole sensors. Reservoir modeling has advanced from the use of empirical equations through statistical regression tools to the present embrace of Artificial Intelligence (AI) and its hybrid techniques. Due to the high dimensionality and heterogeneity of the sensor data, the capability of conventional AI techniques has become limited as they could not handle more than one hypothesis at a time. Ensemble learning method has the capability to combine several hypotheses to evolve a single ensemble solution to a problem. Despite its popular use, especially in petroleum engineering, Artificial Neural Networks (ANN) has posed a number of challenges. One of such is the difficulty in determining the most suitable learning algorithm for optimal model performance. To save the cost, effort and time involved in the use of trial-and-error and evolutionary methods, this paper presents an ensemble model of ANN that combines the diverse performances of seven "weak" learning algorithms to evolve an ensemble solution in the prediction of porosity and permeability of petroleum reservoirs. When compared to the individual ANN, ANN-bagging and RandomForest, the proposed model performed best. This further confirms the great opportunities for ensemble modeling in petroleum reservoir characterization and other petroleum engineering problems