Multi-scale, Multi-dimensional Reservoir Characterization Using Advanced Analytics and Machine Learning
Date
2023
Authors
Koochak, Roozbeh
Editors
Advisors
Haghighi, Manouchehr
Sayyafzadeh, Mohammad
Bunch, Mark
Sayyafzadeh, Mohammad
Bunch, Mark
Journal Title
Journal ISSN
Volume Title
Type:
Thesis
Citation
Statement of Responsibility
Conference Name
Abstract
This thesis aims to investigate novel approaches in the field of Machine learning and advanced
data analytics that can handle large data volumes and open new doors in the field of reservoir
characterization.
To begin, a new approach for rock typing is introduced using fractal theory where conventional
resistivity logs are the only required data. Fractal analysis of resistivity logs showed that the
fractal dimension of these logs which is a measure of the variability of the signal, is related to
the complexity of the rock fabric. the fractal dimension of multiple deep resistivity logs in the
Cooper Basin, Australia was measured and compared with the fabric structure of cores from
same intervals. The results showed that the fractal dimension of resistivity logs increases from
1.14 to 1.29 Ohm-meter for clean to shaly sands respectively, indicating that the fractal
dimension increases with complexity of rock texture.
The thesis continues with a machine learning application to augment/automate facies
classification using resistivity image logs. Given the complexity of the application, a supervised
learning strategy in combination with transfer learning was used to train a deep convolutional
neural network on available data. The results show that in the absence of other
information/logs, the trained network can detect image facies with a testing accuracy of 82%
form electric image logs and a proposed post-processing method increases the final
categorization accuracy even further.
An important step in reservoir characterization is understanding and quantification of
uncertainty in reservoir models. In the next section a novel Generative Adversarial Network
(GAN) architecture is introduced which can generate realistic geological models while maintaining the variability of the generated dataset. The concept of mode collapse and its
adverse effect on variability is addressed in detail. The new architecture is applied to a binary
channelized permeability distribution and the results compared with those generated by Deep
Convolutional GAN (DCGAN) and Wasserstein GAN with gradient penalty (WGAN-GP). The
results show that the proposed architecture significantly enhances variability and reduces the
spatial bias induced by mode collapse, outperforming both DCGAN and WGAN-GP in the
application of generating subsurface property distributions.
Finally, an advanced analytics technique for efficient history matching is proposed in the
appendix. In this part of the thesis, an ensemble of surrogates (proxies) with generation-based
model-management embedded in CMA-ES is proposed to reduce the number of simulation
calls efficiently, while maintaining the history marching accuracy. History matching for a real
field problem with 59 variables and PUNQ-S3 with eight variables was conducted via a standard
CMA-ES and the proposed surrogate-assisted CMA-ES. The results showed that up to 65% and
50% less simulation calls for case#1 and case#2 were required.
School/Discipline
School of Mining and Petroleum Engineering
Dissertation Note
Thesis (Ph.D.) -- University of Adelaide, School of Mining and Petroleum Engineering, 2023
Provenance
This electronic version is made publicly available by the University of Adelaide in accordance with its open access policy for student theses. Copyright in this thesis remains with the author. This thesis may incorporate third party material which has been used by the author pursuant to Fair Dealing exceptions. If you are the owner of any included third party copyright material you wish to be removed from this electronic version, please complete the take down form located at: http://www.adelaide.edu.au/legals