首页|Data on Machine Learning Reported by a Researcher at University of Houston (Petrophysical Property Prediction from Seismic Inversion Attributes Using Rock Physics and Machine Learning: Volve Field, North Sea)
Data on Machine Learning Reported by a Researcher at University of Houston (Petrophysical Property Prediction from Seismic Inversion Attributes Using Rock Physics and Machine Learning: Volve Field, North Sea)
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New study results on artificial intelligence have been published. According to news reporting from Houston, Texas, by NewsRx journalists, research stated, "An accurate petrophysical model of the subsurface is essential for resource development and CO2 sequestration. We present a new workflow that provides a high-resolution estimate of petrophysical reservoir properties using seismic data with rock physics modeling and machine-learning techniques (i.e., deep learning neural networks)." Funders for this research include Subsea Systems Institute; Allied Geophysics Laboratory (Agl) At The University of Houston. Our news reporters obtained a quote from the research from University of Houston: "First, we compare the sequential prediction of the following petrophysical attributes: mineralogy, porosity, and fluid saturation, with the simultaneous prediction of all of the properties using the Volve field in the Norwegian North Sea as an example. The workflow shows that the sequential prediction produces a more efficient and accurate classification of petrophysical properties (the RMS error between the predicted and the original seismic trace is 50% smaller for the sequential compared to the simultaneous procedure). Next, the seismic amplitude response of the reservoirs was studied using rock physics modeling and amplitude versus offset (AVO) analysis to distinguish the different lithologies and fluid types. To ascertain the optimal hydrocarbon production areas, we performed Bayesian seismic inversion and applied machine learning to estimate the petrophysical properties. We examined how porosity, Vclay, and fluid variations affect the elastic properties. In Poisson's ratio versus the P-wave impedance domain, a 10% porosity increase decreases the acoustic impedance (AI) by 30%, while a 20% Vclay decrease increases the AI by 12%. The Utsira Formation in the Volve field (5 km north of the Sleipner 0st field) was evaluated as a potential CO2 geological storage unit using Gassmann fluid substitution and seismic modeling."
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