首页|Researchers from University of Technology Detail Findings in Machine Learning (Reservoir Porosity Assessment and Anomaly Identification From Seismic Attributes Using Gaussian Process Machine Learning)
Researchers from University of Technology Detail Findings in Machine Learning (Reservoir Porosity Assessment and Anomaly Identification From Seismic Attributes Using Gaussian Process Machine Learning)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Machine Learning is the subject of a report. According to news reporting out of Perak, Malaysia, by NewsRx editors, research stated, "Porosity, as one of the reservoir properties, is an important parameter to numerous studies, i.e., the reservoir's oil/gas volume estimation or even the storage capacity measurement in the Carbon Capture Storage (CCS) project. However, an approach to estimate porosity using elastic property from the inversion propagates its error, affecting the result's accuracy." Funders for this research include UTP fundamental research grant, PETRONAS Malaysia, Centre for Subsurface Imaging and Geoscience department Universiti Teknologi PETRONAS. Our news journalists obtained a quote from the research from the University of Technology, "On the other hand, direct estimation from seismic data is another approach to estimating porosity, but it poses a high non-linear problem. Thus, we propose the non-parametric machine learning approach, Gaussian Process (GP), which draws distribution over the function to solve the high non-linear problem between seismic data with porosity and quantify the prediction uncertainty simultaneously. With the help of Random Forest (RF) as the feature selection method, the GP predictions show excellent results in the blind test, a well that is completely removed from the training data, and comparison with other machine learning models. The uncertainty, standard deviation from GP prediction, can act as a quantitative evaluation of the prediction result. Moreover, we generate a new attribute based on the quartile of the standard deviation to delineate the anomaly zones. High anomaly zones are highlighted and associated with high porosity from GP and low inverted P-impedance from inversion results."
PerakMalaysiaAsiaCyborgsEmerging TechnologiesGaussian ProcessesMachine LearningUniversity of Technology
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