Application of Artificial Neural Networks to geological classification: porphyry prospectivity in British Columbia and oil reservoir properties in Iran
Date
2022-11-03
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Degree Level
Masters
Abstract
Seismic facies analysis aims to classify oil and gas reservoirs into geologically and petrophysically meaningful rock groups, or classes. An artificial neural network (ANN) is a versatile and efficient tool for classifying data or estimating subsurface properties from large geophysical datasets. This tool can provide critical information for oilfield development and reservoir characterization.
This study includes application of artificial neural networks on two different datasets: 1) geophysical characterization of an oil reservoir in Iran and 2) geological prospectivity for porphyry in British Columbia, Canada. In the first case study, I utilize seismic attributes, well-log data, and core data analysis and use supervised machine learning techniques to efficiently estimate the acoustic impedance and porosity of the reservoir and to classify it into four lithological classes. Seismic attributes as inputs for our techniques capture the lithological patterns or structural characteristics in the seismic amplitude, phase, frequency, and other complex seismic properties that cannot be directly seen in the original seismic images. Selection of an optimal set of input features from the vast number of possible mathematical transformations of seismic data is a critical task for reservoir property prediction and classification. This selection is performed by standard as well as innovative procedures employing properties of the target classes.
Three different supervised approaches to non-linear classification are used: 1) the so-called probabilistic neural network (PNN), 2) conventional ANN, and 3) an ANN with the new approach of optimal attribute selection. For each of these approaches, images of classification confidence levels and confidence-filtered class images are produced. Assessments of the robustness and accuracy of seismic facies classification is performed for each of these algorithms. The ANN classifiers are validated using validation and test data subsets. The proposed algorithm shows a higher performance, particularly in comparison with the PNN algorithm. Several visualization techniques are used to examine and illustrate the power of the ANN-based approaches to classify the seismic facies with high accuracy. However, the three approaches still provide significantly different levels of lateral continuity, frequency content, and classification accuracy. Therefore, some level of expert assessment is still required when using machine learning for reservoir interpretation.
In the second case study, I use an ANN to explore the prospectivity for porphyry within the Quesnel Terrane, BC, Canada. A purely data-driven approach based on geophysical, structural, and volcanic-age data results in a predictive prospectivity map which correlates well with known mineral occurrences and suggests new areas for potential exploration.
Description
Keywords
Seismic facies analysis, Seismic attributes, Reservoir quality, Supervised neural network, Machine learning techniques, Predictive prospectivity map
Citation
Degree
Master of Science (M.Sc.)
Department
Geological Sciences
Program
Geology