Challenges and Solutions in Stochastic Reservoir Modelling (EET 12)

Geostatistics, Machine Learning, Uncertainty Prediction

image of Challenges and Solutions in Stochastic Reservoir Modelling (EET 12)
  • By Vasily Demyanov and Dan Arnold
  • Format: EPUB
  • Publication Year: 2018
  • Number of Pages: 264
  • Language: English
  • Ebook ISBN: 9789462822399

Many advances in stochastic reservoir modelling have been introduced in the past decade. Novel method of data integration and more accurate representation of geology have been developed with the advances in spatial statistics. However, integrated approach for predictive reservoir modelling still attracts continuous effort to manage reservoir decisions under uncertainty and make better use of the increasing amounts of data and domain knowledge accumulated in the field.
Many solutions to these challenges lie in the cross-disciplinary vision, where modern rigour of computer science and statistics brought together with core geological and engineering domain expertise and basic physical conceptual thinking.
This book aims to bridge across different fields — geostatistics, machine learning, and Bayesian statistics — to demonstrate the common grounds in solving challenging problems of uncertainty quantification, geological realism, and data integration in reservoir prediction. It presents an overview of key concepts and some of the basic and more advanced algorithms for reservoir modelling and uncertainty quantification. This book includes several practical examples to reinforce the learning outcomes. A tutorial on decision making under uncertainty provides a practical way to apply integrated thinking to a real field dataset.

Table of Contents

General disclaimer
1  Introduction

1.1 Introduction
1.2 Uncertainty and geostatistical modelling
1.3 The modelling workflow
1.4 Book structure

2  Basic assumptions of geostatistics
2.1 Spatial continuity
2.2 Modelling approaches
2.3 Data support
2.4 Building a spatial model from data
2.5 Stochastic modelling nature and statistical assumptions
2.6 Characteristics of a spatial random process

3  Discovery and modelling of spatial correlation using variography
3.1 Spatial continuity and correlation
3.2 Spatial correlation described by variography
3.3 Variogram modelling
3.4 Summary

4  Geostatistical estimation and stochastic simulation
4.1 Kriging
4.2 Stochastic simulations
4.3 Summary: geostatistical predictors and stochastic simulations

5  Learning-based models for reservoir description
5.1 Learning from data — a concept for modelling
5.2 Learning algorithms
5.3 Modelling approaches and a model choice
5.4 Model complexity in a reservoir description — a simple example
5.5 Support vector models
5.6 Semi-supervised models for reservoir description
5.7 Feature selection with multiple kernel learning
5.8 Summary

6  Uncertainty quantification of reservoir prediction
6.1 Introduction to uncertainty quantification
6.2 Bayes’ theorem and history matching
6.3 Building the uncertainty model
6.4 Geological parameterisation and history matching
6.5 Geological priors in history matching and uncertainty quantification
6.6 Interpretation, model setup, and parameterisation
6.7 Application of uncertainty quantification workflow to a fracture reservoir model example
6.8 Summary

7  Exercises
Answers and recommendations to the exercises


This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error