Application of Artificial Neural Network to the Realm of Oil & Gas

The HOT Energy Group (HOT)is a technology driven, multidisciplinary and independent organisation serving the energy industry in its pursuit of recovery and field operations optimisation.We are amulti-award winning training providerof upstream oil and gas training solutions, and we pride ourselves in delivering excellence with each and every course session: Quality is always at the core of what we do.Our training portfolio covers all stages of the E&P value chain. Our top-notch oil and gas courses, developed and taught exclusively by true experts, have been valued by the industry for over 30 years. The training sessions equip our clients withpowerful skillsto remain competitive in an increasingly complex petroleum industry.We accelerate careers and empower people, striving to achieve the best possible results.

HOT Engineering - A Member of the HOT Energy Group

This course introduces the applications of Artificial Neural Network in the oil & gas industry.

Vienna

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DESCRIPTION: This course introduces the applications of Artificial Neural Network in the oil &amp; gas industry. The basic principles covered can be applied to any oil and gas field or unit data.Course Level: Foundation

THIS COURSE IS DESIGNED FOR YOU, IF YOU ARE:<ul><li> An engineer working in oil &amp; gas fields/units</li></ul>Prerequisite: Participants having basic knowledge of calculus will derive maximum benefit from the course.

HOW WE BUILD YOUR CONFIDENCE:<ul><li> Highly interactive course</li><li> Practical oil &amp; gas case examples will be presented and adequate exposure will be provided to the participants through exercises</li><li> The instructor is experienced in the application of neural networks as well as training engineers on the basics of Artificial Neural Networks</li></ul>

THE BENEFITS FROM ATTENDING:By the end of the course you will feel confident in your understanding of:<ul><li> Basic structures of the Artificial Neural Network model</li><li> Managing data appropriately for the neural network model</li><li> Applications of the ANN technique to unknown input data</li><li> Developing ANN models to predict output data</li></ul>

TOPICS:<ul><li> Applications of the neural model</li><li> Similarities to biological neurons</li><li> Concept and structure of Artificial Neural Network</li><li> Managing the data for a neural network</li><li> Linear and non-linear separability</li><li> Commonly used activation functions</li><li> Effect of parameters on the activation functions</li><li> Utilising activation functions for mapping outputs - forward pass for a small data set</li><li> Developing the network with a different set of random weights</li><li> Determining the cost function on a small data set based on activation functions</li><li> Basic concept for calculation of derivates of training dataset</li><li> The local &amp; global Minima - avoiding the local minima using momentum term</li><li> Procedure for backpropagation to develop a new set of weights</li><li> Minimisation of cost function using excel solver</li><li> Checking cost function in the validation data set with activation functions</li><li> Finalising the weights for the model</li><li> Testing the finalised weights on validation &amp; test data using different activation functions</li><li> The concept of Multiple Input Multiple Output (MIMO) and Multiple Input Single Output (MISO) models</li><li> Obtain final weights using Leaky ReLu method</li><li> Testing the finalised weights on validation and test data in MIMO &amp; MISO models</li><li> Calculating the cost function on validation and test data</li><li> Computation of cost function based on the parametric ReLu (as per the specified negative gradient) function on pre-defined set of data</li><li> Trial on the test data with model obtained from training dataset</li><li> Applications of neural network to above system using the Swish function</li><li> Gas Compression example with Swish Function</li><li> Mean squared Error with the weights obtained (using Leaky ReLu and Swish activation functions on training data set) on validation and test model</li><li> Review the graphical correlation between the true and model predicted values</li></ul>

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Application of Artificial Neural Network to the Realm of Oil & Gas

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Registrations are closed

Application of Artificial Neural Network to the Realm of Oil & Gas

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