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Generative AI and Quantum Computing in

Oil and Gas UpStream 

 

 

 

COURSE OBJECTIVE

The oil and gas industry is looking to adopt machine learning technology to solve its problems and improve business outcomes. However, many of these companies struggle to do so because they lack the necessary knowledge to effectively use the technology. As a result, they are unable to obtain reliable and efficient results from machine learning models, which impacts their investments and overall performance.

The petroleum industry (oil and gas) is divided into upstream, midstream and downstream. Upstream summarizes the subsurface (mining) part of the industry, including exploration followed by field development and crude oil/gas production. Midstream represents the transportation of oil and gas, and downstream is the refining, that is, the production of fuels, lubricants, plastics and other products. Artificial intelligence AI solutions are already applied and their results.

During the course, participants learn to apply machine learning to seismic processing and interpretation problems. The course covers automated salt interpretation and briefly introduces strategies for solving other seismic interpretation problems.

The course explains some important aspects of the application of deep learning algorithms to seismic images. And the choice of image size and image pre-processing is explained.

The course discusses issues related to geological modeling, including supervised machine learning for estimating petrophysical properties away from well locations.

We will be discussing a module called geomodeling which is used to understand the structural framework of a reservoir. This is done by analyzing the information obtained via seismic interpretation. To estimate petrophysical property values on a three-dimensional mesh, machine learning can be employed. Variograms serve as the foundation of almost all 3D spatial modeling techniques that are relevant to the industry. In this course, we will be using the Gaussian process regression model, which is a part of the probabilistic artificial intelligence, for the purpose of geomodeling.

The course aims to demonstrate various approaches for developing machine learning models to analyze decline curves and forecast oil production. It covers advanced machine learning forecasting models, including recurrent neural networks, long short-term memory, Bayesian machine learning, genetic algorithms, and advanced ensemble learning models.

 

Production modeling using machine learning methodologies, including production optimization, is discussed. A methodology for virtual measurement and the formulation of virtual sensors is explained.

The course enables participants to leverage generative AI algorithms and quantum computing techniques to enhance exploration and reservoir characterization in the oil and gas upstream sector. By utilizing advanced technologies, participants can generate more accurate subsurface models and gain valuable insights into reservoir properties, leading to better decision-making and maximizing hydrocarbon recovery.

QUANTUM COMPUTING

 

Quantum Machine Learning is the integration of quantum algorithms within Machine Learning programs. Machine learning algorithms are used to calculate large amounts of data, quantum machine learning uses qubits and quantum operations or specialized quantum systems to improve the speed of calculation and data storage performed by a program's algorithms. For example, some mathematical and numerical techniques from quantum physics are applicable to classical deep learning. A quantum neural network has computational capabilities to decrease the number of steps, the qubits used, and the computing time.

The important objective of the course is to show the use of quantum computing and tensor networks to improve the calculation of machine learning algorithms.

Additionally, the course explains quantum computing, quantum circuits, important quantum algorithms, quantum mechanics, quantum error and correction, and quantum machine learning.

The course explains applications of quantum computing, such as Quantum Machine Learning, in oil production forecasting models. The improvement of quantum models over traditional ML models is presented, for example LSTM neural networks versus LSTM quantum neural networks.

IMPORTANT

 

In the oil and gas upstream industry, there is a pressing need to accurately apply both traditional and quantum artificial intelligence. To address this, we have developed an advanced validation module along with powerful model risk techniques and probabilistic machine learning methodologies. These tools help us to understand the uncertainties that exist in the results we obtain. Additionally, we have included a module called XAI which ensures that the models we use are not "black boxes" and can be interpreted.

 

WHO SHOULD ATTEND?

The course is designed for professionals in the oil and gas industry who want to enhance their skills in building effective artificial intelligence and quantum computing models for upstream applications. Participants are expected to have knowledge of statistics to better comprehend the topics covered in the course.

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Schedules:

  • Europe: Mon-Fri, CEST 16-19 h

 

  • America: Mon-Fri, CDT 18-21 h

  • Asia: Mon-Fri, IST 18-21 h

 

 

 

 

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Price: 7.900 €

 

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Level: Advanced

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Duration: 36 h

 

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     Material: 

  • Presentations PDF

  • Exercises in Excel, R, Python, Jupyterlab y Tensorflow

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AGENDA
 AI and Quantum Computing in

Oil and Gas UpStream

 

Anchor 10

Machine Learning

Module 1: Machine Learning

 

  • Definition of Machine Learning

  • Machine Learning Methodology

    • Data Storage

    • Abstraction

    • Generalization

    • Assessment

  • Supervised Learning

  • Unsupervised Learning

  • Reinforcement Learning

  • Deep learning

  • Typology of Machine Learning algorithms

  • Steps to Implement an Algorithm

    • Information collection

    • Exploratory Analysis

    • Model Training

    • Model Evaluation

    • Model improvements

    • Machine Learning in consumer credit risk

  • Machine Learning in credit scoring models

  • Quantum Machine Learning

Module 2: EDA Exploratory Analysis

  • Data typology

  • Transactional data

  • Unstructured data embedded in text documents

  • Social Media Data

  • Data sources

  • Data review

  • Target definition

  • Time horizon of the target variable

  • Sampling

    • Random Sampling

    • Stratified Sampling

    • Rebalanced Sampling

  • Exploratory Analysis:

    • Histograms

    • Q Q Plot

    • Moment analysis

    • boxplot

  • Treatment of Missing values

    • Multivariate Imputation Model

  • Advanced Outlier detection and treatment techniques

    • Univariate technique: winsorized and trimming

    • Multivariate Technique: Mahalanobis Distance

  • ​Exercise 1: EDA Exploratory Analysis

Module 3: Feature Engineering

  • Data Standardization

  • Variable categorization

    • Equal Interval Binning

    • Equal Frequency Binning

    • Chi-Square Test

  • Binary coding

  • Binning

    • Kind of transformation

    • Univariate Analysis with Target variable

    • Variable Selection

    • Treatment of Continuous Variables

    • Treatment of Categorical Variables

    • Gini

    • Information Value

    • Optimization of continuous variables

    • Optimization of categorical variables

  • Exercise 2: Detection and treatment of Advanced Outliers

  • Exercise 3: Stratified and Random Sampling in R

  • Exercise 4: Multivariate imputation model

  • Exercise 5: Univariate analysis in percentiles in R

  • Exercise 6: Continuous variable optimal univariate analysis in Excel

  • Exercise 7: Estimation of the KS, Gini and IV of each variable in Excel

  • Exercise 8: Feature Engineering of variables

Unsupervised Learning

Module 4: Unsupervised models

  • Hierarchical Clusters

  • K Means

  • Standard algorithm

  • Euclidean distance

  • Principal Component Analysis (PCA)

  • Advanced PCA Visualization

  • Eigenvectors and Eigenvalues

  • Exercise 9: Segmentation of the data with K-Means R

 

Supervised Learning

Module 5: Logistic Regression and LASSO Regression

 

  • Econometric Models

    • Logit regression

    • probit regression

    • Piecewise Regression

    • survival models

  • Machine Learning Models

    • Lasso Regression

    • Ridge Regression

  • Model Risk in Logistic Regression

  • Exercise 10: Lasso Logistic Regression in R

  • Exercise 11: Ridge Regression in R

Module 6: Trees, KNN and Naive Bayes

 

  • Decision Trees

    • Modeling

    • Advantages and disadvantages

    • Recursion and Partitioning Processes

    • Recursive partitioning tree

    • Pruning Decision tree

    • Conditional inference tree

    • Tree display

    • Measurement of decision tree prediction

    • CHAID model

    • Model C5.0

  • K-Nearest Neighbors KNN

    • Modeling

    • Advantages and disadvantages

    • Euclidean distance

    • Distance Manhattan

    • K value selection

  • Probabilistic Model: Naive Bayes

    • Naive bayes

    • Bayes' theorem

    • Laplace estimator

    • Classification with Naive Bayes

    • Advantages and disadvantages

  • Exercise 12: KNN and PCA

Module 7: Support Vector Machine SVM

  • Support Vector Classification

  • Support Vector Regression

  • Optimal hyperplane

  • Support Vectors

  • Add costs

  • Advantages and disadvantages

  • SVM visualization

  • Tuning SVM

  • Kernel trick

  • Exercise 14: Support Vector Machine in R

Module 8: Ensemble Learning

  • Classification and regression ensemble models

  • Bagging

  • Bagging trees

  • Random Forest

  • Boosting

  • Adaboost

  • Gradient Boosting Trees

  • Xgboost

  • Advantages and disadvantages

  • Exercise 15:  Boosting in R

  • Exercise 16: Bagging in R

  • Exercise 17: Random Forest, R and Python

  • Exercise 18:  Gradient Boosting Trees

Deep Learning

Module 9: Introduction to Deep Learning

  • Definition and concept of deep learning

  • Why now the use of deep learning?

  • Neural network architectures

  • Feedforward network

  • R deep learning

  • Python deep learning

  • Convolutional Neural Networks

  • Use of deep learning in image classification

  • cost function

  • Gradient descending optimization

  • Use of deep learning

    • How many hidden layers?

    • How many neurons, 100, 1000?

    • How many times and size of the batch size?

    • What is the best activation function?

  • Hardware, CPU, GPU and cloud environments

  • Advantages and disadvantages of deep learning

 

Module 10: Deep Learning Feed Forward Neural Networks

  • Single Layer Perceptron

  • Multiple Layer Perceptron

  • Neural network architectures

  • Activation function

    • Sigmoidal

    • Rectified linear unit (Relu)

    • The U

    • Selu

    • Hhyperbolic hypertangent

    • Softmax

    • Other

  • Back propagation

    • Directional derivatives

    • Gradients

    • Jacobians

    • Chain rule

    • Optimization and local and global minima

  • Exercise 19: Deep Learning Feed Forward

Module 11: Deep Learning Convolutional Neural Networks CNN

  • CNN for pictures

  • Design and architectures

  • Convolution operation

  • Descending gradient

  • Filters

  • Strider

  • Padding

  • Subsampling

  • Pooling

  • Fully connected

  • Exercise 20: deep learning CNN

Module 12: Deep Learning Recurrent Neural Networks RNN

  • Natural Language Processing

  • Natural Language Processing (NLP) text classification

  • Long Term Short Term Memory (LSTM)

  • Hopfield

  • Bidirectional associative memory

  • Descending gradient

  • Global optimization methods

  • One-way and two-way models

  • Deep Bidirectional Transformers for Language Understanding ​

  • Exercise 21: Deep Learning LSTM

Module 14: Generative Adversarial Networks (GANs)

 

  • Generative Adversarial Networks (GANs)

  • Fundamental components of the GANs

  • GAN architectures

  • Bidirectional GAN

  • Training generative models

  • Exercise 22: Deep Learning GANs

Module 15: Tuning Hyperparameters

  • Hyperparameterization

  • Grid search

  • Random search

  • Bayesian Optimization

  • Train test split ratio

  • Learning rate in optimization algorithms (e.g. gradient descent)

  • Selection of optimization algorithm (e.g., gradient descent, stochastic gradient descent, or Adam optimizer)

  • Activation function selection in a (nn) layer neural network (e.g. Sigmoid, ReLU, Tanh)

  • Selection of loss, cost and custom function

  • Number of hidden layers in an NN

  • Number of activation units in each layer

  • The drop-out rate in nn (dropout probability)

  • Number of iterations (epochs) in training a nn

  • Number of clusters in a clustering task

  • Kernel or filter size in convolutional layers

  • Pooling size

  • Batch size

  • Exercise 23: Optimization Xboosting, Random forest and SVM

  • Exercise 24: Optimized Deep Learning

Probabilistic Machine Learning 

 

Module 16: Probabilistic Machine Learning

​​

  • Introduction to probabilistic machine learning

  • Gaussian models

  • Bayesian Statistics

  • Bayesian logistic regression

  • Kernel family

  • Gaussian processes

    • Gaussian processes for regression

  • Hidden Markov Model

  • Markov chain Monte Carlo (MCMC)

    • Metropolis Hastings algorithm

  • Machine Learning Probabilistic Model

  • Bayesian Boosting

  • Bayesian Neural Networks

  • Exercise 25: Gaussian process for regression

  • Exercise 26: Bayesian Neural Networks

Model Validation

Module 17: Validation of traditional and Machine Learning models

  • Model validation

  • Validation of machine learning models

  • Regulatory validation of machine learning models in Europe

  • Out of Sample and Out of time validation

  • Checking p-values in regressions

  • R squared, MSE, MAD

  • Waste diagnosis

  • Goodness of Fit Test

  • Multicollinearity

  • Binary case confusion matrix

  • K-Fold Cross Validation

  • Diagnostico del modelo

  • Exercise 27: Validación avanzada de la regression

  • Exercise 28: Diagnostico de la regresión

  • Exercise 29: K-Fold Cross Validation in R

Module 18: Advanced Validation of AI Models

  • Integration of state-of-the-art methods in interpretable machine learning and model diagnosis.

  • Data Pipeline

  • Feature Selection

  • Black-box Models

  • Post-hoc Explainability

  • Global Explainability

  • Local Explainability

  • Model Interpretability

  • Diagnosis: Accuracy, WeakSpot, Overfit, Reliability, Robustness, Resilience, Fairness

  • Model comparison

    • Comparative for Regression and Classification

    • Fairness Comparison

  • Exercise 30: Validation and diagnosis of advanced credit scoring models

Auto Machine Learning and XAI

Module 19: Automation of ML

 

  • What is modeling automation?

  • That is automated

  • Automation of machine learning processes

  • Optimizers and Evaluators

  • Modeling Automation Workflow Components

  • Hyperparameter optimization

  • Global evaluation of modeling automation

  • Implementation of modeling automation in banking

  • Technological requirements

  • Available tools

  • Benefits and possible ROI estimation

  • Main Issues

  • Genetic algorithms

  • Exercise 31: Automation of the modeling, optimization and validation of pricing models

Explainable Artificial Intelligence

Module 20: Explainable Artificial Intelligence XAI

 

  • Interpretability problem

  • Machine learning models

    • 1. The challenge of interpreting the results,

    • 2. The challenge of ensuring that management functions adequately understand the models, and

    • 3. The challenge of justifying the results to supervisors

  • ​Black Box Models vs. Transparent and Interpretable Algorithms

  • Interpretability tools

  • Shap, Shapley Additive explanations

    • Global Explanations

    • Dependency Plot

    • Decision Plot

    • Local Explanations Waterfall Plot

  • Lime, agnostic explanations of the local interpretable model

  • Explainer Dashboard

  • Other advanced tools

  • Exercise 32: XAI interpretability of pricing

Quantum Computing

Module 21: Quantum computing and algorithms

  ​

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.

  • 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

Module 22: Introduction to quantum mechanics

  • 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

Module 23: Introduction to quantum error correction

  • 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

​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

Module 25: Quantum Machine Learning

  • 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

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

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.

  • 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

Module 28: Production engineering using machine learning

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.

  • 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

Module 29: Machine Learning in Reservoir Engineering

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

Module 30: Advanced Oil Production Forecasting Models with Multivariate Data

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.

  • 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

Probabilistic Machine Learning for Oil and Gas

Module 31: Geomodeling used Gaussian process regression

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.

  • Variogram

  • Data Description

  • Basic Well Log Data Analysis

  • Gaussian process regression

  • Formulation

  • Radial Basis Function (RBF) Kernel

  • Exercise 53: Geomodeling using Gaussian process regression

GENERATIVE AI IN OIL & GAS UPSTREAM

 

Module 41: Generative AI​

  • 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

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