Permeability Prediction Using Machine Learning Methods for the CO2 Injectivity of the Precipice Sandstone in Surat Basin, Australia
MetadataShow full item record
This paper presents the results of a research project which investigated permeability prediction for the Precipice Sandstone of the Surat Basin. Machine learning techniques were used for permeability estimation based on multiple wireline logs. This information improves the prediction of CO2 injectivity in this formation. Well logs and core data were collected from 5 boreholes in the Surat Basin, where extensive core data and complete sets of conventional well logs exist for the Precipice Sandstone. Four different machine learning (ML) techniques, including Random Forest (RF), Artificial neural network (ANN), Gradient Boosting Regressor (GBR), and Support Vector Regressor (SVR), were independently trained with a wide range of hyper-parameters to ensure that not only is the best model selected, but also the right combination of model parameters is selected. Cross-validation for 20 different combinations of the seven available input logs was used for this study. Based on the performances in the validation and blind testing phases, the ANN with all seven logs used as input was found to give the best performance in predicting permeability for the Precipice Sandstone with the coefficient of determination (R2) of about 0.93 and 0.87 for the training and blind data sets respectively. Multi-regression analysis also appears to be a successful approach to calculate reservoir permeability for the Precipice Sandstone. Models with a complete set of well logs can generate reservoir permeability with R2 of more than 90%.
Showing items related by title, author, creator and subject.
Ravestein, J.; Griffiths, Cedric; Dyt, C.; Michael, K. (2015)Underground geological storage of CO2 (GSC) requires a high level of subsurface understanding that is often hindered by a lack of data. This study demonstrates the use of stratigraphic forward modelling (SFM) in generating ...
Thermal History and Deep Overpressure Modelling in the Northern Carnarvon Basin, North West Shelf, AustraliaHe, Sheng (2002)The Northern Carnarvon Basin is the richest petroleum province in Australia. About 50 gas/condensate and oil fields, associated mainly with Jurassic source rocks, have been discovered in the sub-basins and on the Rankin ...
Identification of sedimentary-diagenetic facies and reservoir porosity and permeability prediction: An example from the Eocene beach-bar sandstone in the Dongying Depression, ChinaWang, J.; Cao, Y.; Liu, Keyu; Liu, J.; Kashif, M. (2017)Accurate prediction of reservoir porosity and permeability is essential for prospecting hydrocarbon reserves and petroleum production capacity. We propose an innovative reservoir porosity and permeability prediction method ...