Prediction of natural gas hydrates formation using a combination of thermodynamic and neural network modeling

Rebai, Noura; Hadjadj, Ahmed; Benmounah, Abdelbaki; Berrouk, Abdallah S.; Boualleg, Salim M.

doi:10.1016/j.petrol.2019.106270

Cited by 32 publications

(11 citation statements)

References 28 publications

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“…All the hyperparameters connected with the deep neural network (DNN), i.e., number of hidden layers, neurons in each layer, activation function, and optimization function), were finalized to obtain a minimum value of the root mean square (RSM) error. Further details on the model can be found in the author's previous work [33][34][35]. The current DNN model is finalized with two hidden layers consisting of four and three neurons in the first and second layers, respectively, with Levenberg-Marquardt (LM), as an optimizing algorithm; Rectifier Linear Unit (ReLU) as an activation function for the input and the two hidden layers, and sigmoid as an activation function for the output layer as displayed in Figure 7.…”

Section: The Deep Neural Networkmentioning

confidence: 99%

Turbine Design and Optimization for a Supercritical CO2 Cycle Using a Multifaceted Approach Based on Deep Neural Network

et al. 2021

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Turbine as a key power unit is vital to the novel supercritical carbon dioxide cycle (sCO2-BC). At the same time, the turbine design and optimization process for the sCO2-BC is complicated, and its relevant investigations are still absent in the literature due to the behavior of supercritical fluid in the vicinity of the critical point. In this regard, the current study entails a multifaceted approach for designing and optimizing a radial turbine system for an 8 MW sCO2 power cycle. Initially, a base design of the turbine is calculated utilizing an in-house radial turbine design and analysis code (RTDC), where sharp variations in the properties of CO2 are implemented by coupling the code with NIST’s Refprop. Later, 600 variants of the base geometry of the turbine are constructed by changing the selected turbine design geometric parameters, i.e., shroud ratio (rs4r3), hub ratio (rs4r3), speed ratio (νs) and inlet flow angle (α3) and are investigated numerically through 3D-RANS simulations. The generated CFD data is then used to train a deep neural network (DNN). Finally, the trained DNN model is employed as a fitting function in the multi-objective genetic algorithm (MOGA) to explore the optimized design parameters for the turbine’s rotor geometry. Moreover, the off-design performance of the optimized turbine geometry is computed and reported in the current study. Results suggest that the employed multifaceted approach reduces computational time and resources significantly and is required to completely understand the effects of various turbine design parameters on its performance and sizing. It is found that sCO2-turbine performance parameters are most sensitive to the design parameter speed ratio (νs), followed by inlet flow angle (α3), and are least receptive to shroud ratio (rs4r3). The proposed turbine design methodology based on the machine learning algorithm is effective and substantially reduces the computational cost of the design and optimization phase and can be beneficial to achieve realistic and efficient design to the turbine for sCO2-BC.

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Section: The Deep Neural Networkmentioning

confidence: 99%

Turbine Design and Optimization for a Supercritical CO2 Cycle Using a Multifaceted Approach Based on Deep Neural Network

et al. 2021

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“…Restriction in the diameter available to the production flow, caused by the fluid composition, solid deposition, or related to pressure and temperature fluctuations, leads to flow assurance issues. Hydrate formation, scaling, and slugging are some examples [22] [23] [24]. Also, changes in the reservoir static pressure can increase BSW (Basic Sediments and Water) rate, modifying the viscosity of the production fluid.…”

Section: Problems During Oil and Gas Productionmentioning

confidence: 99%

Unsupervised Methods to Classify Real Data from Offshore Wells

Castro¹,

Santos²,

Leta³

et al. 2021

AJOR

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In the petroleum industry, sensor data and information are valuable. It can detect, predict and help to understand processes during oil production. Offshore wells require more attention. Once workovers, maintenance, and intervention are more costly than onshore wells. Coupling data-driven methods for well-monitoring applications, two unsupervised classification methods, one statistical and one machine learning-based, are proposed to detect anomalies in well data. The novelty is presented by applying a Control Chart using a 3 standard deviations window for the Permanent Downhole Gauge Pressure sensor (P-PDG), and a Fuzzy C-means algorithm to classify data from pressure and temperature sensors in an offshore field. The main goal in structuring a classified data set is using it to train machine learning models to monitor and manage petroleum production. Modeling applications for early fault detection systems in offshore production, based on real-time data from production sensors, require classified data sets. Then, labeling two target classes: "normal" and "fault" is a key step to be implemented in order to train the machine learning models. Therefore, this paper applies two methodologies to classify a real-time data set to create a training data set divided into "normal" and "fault" classes. Thus, it is possible to visualize the abnormal events pointed out by the methodologies and compare how sensible is each method. In addition, it is proposed a random forest application to test the performance of the classified data sets from both methods. The results have shown that the control chart method presents higher sensibility than fuzzy c-means, however, the differences between are insignificant. The random forest performance displayed sensitivity and specificity values of 99.91% and 100% for the data set classified by the control chart method and 94.01% and 99.98% for the data set classified by fuzzy c-means algorithm.

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“…It is an ice-like, non-stochiometric crystalline mixture made up of a water cage frame dominated by gas molecules like methane, CH4, ethane, C2H5, and carbon dioxide, CO2, that forms solid particles that takes place under high pressure and low temperature conditions [3][4][5][6]. Gas hydrates are produced in the petroleum and natural gas industries' output, refining and transmission facilities [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

“…The use of Machine Learning (ML) is an innovative risk control technique that is currently being applied. ML is basically a numerical representation of a phenomenon, given with a certain significance and based on a certain environment, aimed at performing a job [8]. Deployment of ML models would allow us to do predictive analysis of gas hydrate growth because the value of determining hydrate formation is also that the solid crystalline structure which forms like ice will plug the oil and gas pipes as hydrate forms, either during gas production or transmission.…”

Section: Introductionmentioning

confidence: 99%

Application of Gaussian Process Regression (GPR) in Gas Hydrate Mitigation

Suresh

Qasim

Lal

et al. 2021

ARFMTS

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The production of oil and natural gas contributes to a significant amount of revenue generation in Malaysia thereby strengthening the country’s economy. The flow assurance industry is faced with impediments during smooth operation of the transmission pipeline in which gas hydrate formation is the most important. It affects the normal operation of the pipeline by plugging it. Under high pressure and low temperature conditions, gas hydrate is a crystalline structure consisting of a network of hydrogen bonds between host molecules of water and guest molecules of the incoming gases. Industry uses different types of chemical inhibitors in pipeline to suppress hydrate formation. To overcome this problem, machine learning algorithm has been introduced as part of risk management strategies. The objective of this paper is to utilize Machine Learning (ML) model which is Gaussian Process Regression (GPR). GPR is a new approach being applied to mitigate the growth of gas hydrate. The input parameters used are concentration and pressure of Carbon Dioxide (CO2) and Methane (CH4) gas hydrates whereas the output parameter is the Average Depression Temperature (ADT). The values for the parameter are taken from available data sets that enable GPR to predict the results accurately in terms of Coefficient of Determination, R2 and Mean Squared Error, MSE. The outcome from the research showed that GPR model provided with highest R2 value for training and testing data of 97.25% and 96.71%, respectively. MSE value for GPR was also found to be lowest for training and testing data of 0.019 and 0.023, respectively.

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Prediction of natural gas hydrates formation using a combination of thermodynamic and neural network modeling

Cited by 32 publications

References 28 publications

Turbine Design and Optimization for a Supercritical CO2 Cycle Using a Multifaceted Approach Based on Deep Neural Network

Turbine Design and Optimization for a Supercritical CO2 Cycle Using a Multifaceted Approach Based on Deep Neural Network

Unsupervised Methods to Classify Real Data from Offshore Wells

Application of Gaussian Process Regression (GPR) in Gas Hydrate Mitigation

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