Quantum Computing

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Module 21: Quantum computing and algorithms

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Objective: Quantum computing applies quantum mechanical phenomena. On a small scale, physical matter exhibits properties of both particles and waves, and quantum computing takes advantage of this behavior using specialized hardware. The basic unit of information in quantum computing is the qubit, similar to the bit in traditional digital electronics. Unlike a classical bit, a qubit can exist in a superposition of its two "basic" states, meaning that it is in both states simultaneously.

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• Future of quantum computing in insurance

• Is it necessary to know quantum mechanics?

• QIS Hardware and Apps

• quantum operations

• Qubit representation

• Measurement

• Overlap

• Matrix multiplication

• Qubit operations

• Multiple Quantum Circuits

• Entanglement

• Deutsch Algorithm

• Quantum Fourier transform and search algorithms

• Hybrid quantum-classical algorithms

• Quantum annealing, simulation and optimization of algorithms

• Quantum machine learning algorithms

• Exercise 33: Quantum operations multi-exercises

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Module 22: Introduction to quantum mechanics

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• Quantum mechanical theory

• Wave function

• Schrodinger's equation

• Statistical interpretation

• Probability

• Standardization

• Impulse

• The uncertainty principle

• Mathematical Tools of Quantum Mechanics

• Hilbert space and wave functions

• The linear vector space

• Hilbert's space

• Dimension and bases of a Vector Space

• Integrable square functions: wave functions

• Dirac notation

• Operators

• General definitions

• Hermitian adjunct

• Projection operators

• Commutator algebra

• Uncertainty relationship between two operators

• Operator Functions

• Inverse and Unitary Operators

• Eigenvalues and Eigenvectors of an operator

• Infinitesimal and finite unit transformations

• Matrices and Wave Mechanics

• Matrix mechanics

• Wave Mechanics

• Exercise 34: Quantum mechanics multi-exercises

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Module 23: Introduction to quantum error correction

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• Error correction

• From reversible classical error correction to simple quantum error correction

• The quantum error correction criterion

• The distance of a quantum error correction code

• Content of the quantum error correction criterion and the quantum Hamming bound criterion

• Digitization of quantum noise

• Classic linear codes

• Calderbank, Shor and Steane codes

• Stabilizer Quantum Error Correction Codes

• Exercise 35: Noise Model, Repetition Code and quantum circuit

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​Module 24: Quantum Computing II

• Quantum programming

• Solution Providers

  • IBM Quantum Qiskit

  • Amazon Braket

  • PennyLane

  • Cirq

  • Quantum Development Kit (QDK)

  • Quantum clouds

  • Microsoft Quantum

  • Qiskit

• Main Algorithms

  • Grover's algorithm

  • Deutsch–Jozsa algorithm

  • Fourier transform algorithm

  • Shor's algorithm

• Quantum annealers

• D-Wave implementation

• Qiskit Implementation

• Exercise 36: Quantum Circuits, Grover Algorithm Simulation, Fourier Transform and Shor​

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Module 25: Quantum Machine Learning

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• Quantum Machine Learning

• Hybrid models

• Quantum Principal Component Analysis

• Q means vs. K means

• Variational Quantum Classifiers

• Variational quantum classifiers

• Quantum Neural Network

  • Quantum Convolutional Neural Network

  • Quantum Long Short Memory LSTM

• Quantum Support Vector Machine (QSVC)

• Exercise 37: Quantum Support Vector Machine​

​Module 26: Tensor Networks for Machine Learning

• What are tensor networks?

• Quantum Entanglement

• Tensor networks in machine learning

• Tensor networks in unsupervised models

• Tensor networks in SVM

• Tensor networks in NN

• NN tensioning

• Application of tensor networks in credit scoring models

• Exercise 38: Neural Network using tensor networks

Upstream Oil and Gas Models

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Module 27: Geophysics and seismic data processing and interpretation using AI and Quantum AI

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One of the most crucial tasks in seismic reflection imaging is to identify salt bodies with

with great precision. Traditionally, this is achieved by visually selecting the boundaries between salt and sediment, which requires a great deal of manual work and can introduce systematic bias. With the recent progress of deep learning algorithms and increasing computational power, great efforts have been made to replace human effort with machine power in the interpretation of salt bodies.

Convolutional neural networks (CNN) is revolutionizing the field of computer vision and CNN-based classification is demonstrated using a state-of-the-art U-Net network structure, along with the ResNet residual learning framework, to delineate the body of salt with high precision.

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• Seismic reflection experiment

• Inverse problem: creating images and velocity models

• Acoustic wave equation

• The process of velocity estimation, and imaging of scatterers in the subsurface

• Saline interpretation with machine learning

• U-Net for semantic segmentation

• Loss function for U-Net

• Laminated K-Fold

• Interpretation results

• Classification of images in Real Estate

• • Problem Statement

  • Deep Learning Problem Formulation

  • Project and Data Source

  • Image Dataset

  • Evaluation Metric

  • Exploratory Data Analysis

  • Image Preprocessing

  • Model Training

  • Productionizing

• Image Classification: Data-driven Approach

• Convolutional Neural Networks

• Data and the Loss preprocessing

• Hyperparameters

• Train/val/test splits

• L1/L2 distances

• Hyperparameter search

  • Optimization: Stochastic Gradient Descent

  • Optimization landscapes

  • Black Box Landscapes

  • Local search

  • Learning rate

• Weight initialization

• Batch normalization

• Regularization (L2/dropout)

• Loss functions

• Gradient checks

• Sanity checks

• Hyperparameter optimization

•  Architectures

  • Convolution / Pooling Layers

  • Spatial arrangement

  • Layer patterns

  • Layer sizing patterns

  • AlexNet/ZFNet/Densenet/VGGNet/U-Net/Resnet

• Convolutional Neural Networks tSNE embeddings

• Deconvnets

• Data gradients

• Fooling ConvNets  

• Human comparisons Transfer Learning and

• Fine-tuning Convolutional Neural Networks

• Performance Metrics

  • Accuracy

  • F1-Score

  • AUC-ROC

  • Cohen Kappa Coefficient

• Exercise 29: Deep CNN-Based Architecture for Automatic Interpretation of Salt Bodies from Seismic Data

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Module 28: Production engineering using machine learning

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Production engineering encompasses various activities, such as measuring, analyzing, modeling, prioritizing and executing actions to improve productivity and profitability. Production engineers generally consider multiple options to improve the conditions of the production system from a technical, economic and environmental point of view. With the introduction of ML in the oil and gas industry, numerous applications have been published to assist in the research of flow state identification, performance measurement, and identification of production capacity problems and limitations.

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• Production engineering

• Potential Well Rate Prediction

• Increased well knowledge with virtual sensors

• Virtual rate measurement

• Predicting well rates from indirect measurements

• Predicting well rates for gas-lift wells

• Well failure prediction

• Data Feature Engineering

• Root Cause Analysis (RCA)

• Actions to improve performance

• Predicting poor well performance

• Water cut management based on speed

• Critical Oil Rate Prediction

• Optimal well spacing in naturally fractured reservoirs NFRs

• Reservoir threshold radius prediction

• Exercise 40: Critical Oil Rate Model using linear regression, random forest regression and quantum neural networks

• Exercise 41: Classical neural network model and quantum neural networks of Rate and Well Pressure

• Exercise 42: Well Failure Clustering Using K-Means

• Exercise 43: Well Rate Model of the random forest and Gradient Boosting Machines

AI and Quantum Computing for Reservoir Engineering

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Module 29: Machine Learning in Reservoir Engineering

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Reservoir engineering is an interdisciplinary field that integrates mechanics, geology, physics, mathematics and computer science as research tools to economically recover hydrocarbon resources in underground formations.

With the improvement of computing architecture, ML can also efficiently extract non-derived information from the explosion of real-world data collected in oil industry applications. As a powerful data-driven technique, ML is being widely accepted to help and improve our understanding of drilling, production and reservoir areas. In particular, ML has been widely used in reservoir engineering and has achieved excellent results, such as prediction of permeability, porosity and tortuosity, prediction of shale gas production, reservoir characterization, reconstruction 3D digital core analysis, well test interpretation, rapid shale gas production optimization, well log processing and historical matching.

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• XGBoost for cumulative production forecasting

• Random Forest for the identification of water invasion patterns; property estimation

• SVM for surface oil rate prediction; permeability estimation

• Gaussian Regression Process for early oil and gas production

• ARIMA for production dynamics

• LSTM for production dynamics; identification of reservoir models

• CNN for well test interpretation and production dynamics

• Exercise 44: Forecasting Future Production Rates of an Oil Well Using Decline Curve Analysis

• Exercise 45: Forecasting future production rates of an oil well using ARIMA

• Exercise 46: Production dynamics of an oil well using LSTM

• Exercise 47: Production dynamics of an oil well using Bayesian LSTM

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Module 30: Advanced Oil Production Forecasting Models with Multivariate Data​

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Accurate forecasting of well production is crucial for petroleum engineers to identify rapid declines in oil production and select appropriate recovery mechanisms. It also helps estimate the ultimate recovery and useful life of the well, leading to sustainable development and balancing economic inputs and outputs. However, predicting oil production accurately can be challenging due to the complexity of subsurface conditions. Heterogeneous formation properties, multiphase fluid flow, uncertain subsurface conditions, and frequent in situ operations can significantly impact production, making forecasting models more complex. Therefore, advanced forecasting models are presented.

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• The oil production forecasting model

• Multivariate data analysis

• raw data import

• oil volume forecast

• evaluating the predictive performance of the model

• comparison with reference models.

• Advanced Models

• TCN-Temporal Convolutional Networks

• LSTM-Long Short Term Memory

• RNN-Recurrent Neural Network

• GRU-Gated recurring units

• QLSTM-Quantum Long Short Memory

• Advanced model with genetic algorithms

• Exercise 48: LSTM-Long Short Term Memory for oil production forecasting

• Exercise 49: GRU-Gated recurring oil production forecasting units

• Exercise 50: Quantum Long Short Term Memory for oil production forecasting

• Exercise 51: TCN-Temporal Convolutional Networks for oil production forecasting

• Exercise 52: Genetic algorithm model for oil production forecasting

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Probabilistic Machine Learning for Oil and Gas

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Module 31: Geomodeling used Gaussian process regression

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Geomodeling is an important step in the exploration and production planning process. Geomodeling is carried out after understanding the structural framework of the reservoir based on the information extracted during the seismic interpretation. Machine learning can help us estimate petrophysical property values on a three-dimensional mesh. Variograms are the basis of almost all 3D spatial modeling techniques of industrial relevance.

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• Variogram

• Data Description

• Basic Well Log Data Analysis

• Gaussian process regression

• Formulation

• Radial Basis Function (RBF) Kernel

• Exercise 53: Geomodeling using Gaussian process regression

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GENERATIVE AI IN OIL & GAS UPSTREAM

Module 41: Generative AI​

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• Introducing generative AI

• What is Generative AI?

• Generative AI Models

  • Generative Pre- trained Transformer (GPT)

  • Llama 2

  • PaLM2

  • DALL-E

• Text generation, Image generation, Music generation, Video generation

• Generating text

• Generating Code

• Ability to solve logic problems

• Generating Music

• Enterprise Use Cases for Generative AI

• Overview of Large Language Models (LLMs)

  • Transformer Architecture

  • Types of LLMs

  • Open-Source vs. Commercial LLMs

  • Key Concepts of LLMs

• Prompts

• Tokens

• Embeddings

• Model configuration

• Prompt Engineering

• Model adaptation

• Emergent Behavior

• Specifying multiple Dataframes to ChatGPT

• Debugging ChatGPT’s code
  Human errors

• Exercise 54: Embeddings for words, sentences, question answers

• Exercise 55: Embedding Visualization

• Exercise 56: PCA (Principal Component Analysis)

• Exercise 57: Embeddings on Large Dataset

• Exercise 58: Prompt engineering

• Exercise 59: Advanced Prompting Techniques

• Exercise 60: Large Language Models (LLMs)

• Exercise 61: Retrieval Augmented Generation

• Exercise 62: Traditional KMeans to LLM powered KMeans

• Exercise 63:  generative AI in Reservoir Engineering

• Exercise 64: Transformers Oil Production Forecasting  with generative AI