Machine Learning and Seismic Attributes for Petroleum Prospect Generation and Evaluation: An Example from Offshore Australia

TL;DR

This paper demonstrates how machine learning algorithms, specifically neural networks, can effectively integrate seismic and elastic attributes to identify and evaluate hydrocarbon reservoirs, source rocks, and seal rocks in offshore Australia.

Contribution

It introduces a novel multi-physical attribute integration approach using ML to improve petroleum prospecting and reservoir characterization in complex geological settings.

Findings

Successful delineation of seal and source rocks using resistivity and shale volumes

Identification of reservoir intervals through porosity and resistivity analysis

Detection of subtle faults with CNN enhances hydrocarbon migration understanding

Abstract

The growing number of seismic and elastic attributes poses a challenge, making the full benefit from each attribute in characterizing geological formation very difficult, if not impossible. Various approaches are routinely employed to select the best attributes for specific purposes. Machine learning (ML) algorithms have demonstrated good capabilities in combining appropriate attributes to address reservoir characterization problems. This study aims to use and combine seismic and elastic attributes to detect hydrocarbon-saturated reservoirs, source rock, and seal rocks in the Poseidon field, Offshore Australia. A large number of attributes are extracted from seismic data and from impedance data. Artificial Neural Networks (ANN) are implemented to combine the extracted attributes and convert them into Resistivity volume, and Gamma Ray volume from which Shale probability volume, Sand volume probability volume, Effective Porosity volume, and Gas Chimney Probability Cubes. The cubes are deployed for a detailed analysis of the petroleum system in the area. The produced Shale volume and Resistivity cube helped delineate the seal rock and source rock in the area. Next, the reservoir intervals were identified using Porosity, Shale, and Resistivity volumes. A pre-trained Convolutional Neural Network (CNN) is trained using another carefully selected attribute set to detect subtle faults that hydrocarbons might migrated through from source rock to trap. The integration of all the extracted cubes contributed to find new prospects in the area and assess their geological probability of success. The proposed approach stands out for its multi-physical attribute integration, Machine Learning and Human expertise incorporation, possible applicability to other fields.