During this reporting period all preliminary tasks were completed (such as the creation of a flexible project database) and construction of the actual broadband transform function was begun. Analysis of intermediate results performed during the reporting period has proven that the neural networks being used can accurately predict data elements using surface seismic or crosswell seismic data and attributes as input
The project, "Calibration of Seismic Attributes for Reservoir Characterization," is now complete. Ou...
The first application is seismic inversion. Artificial neural networks were used to invert post-stac...
Machine learning has been used in the petroleum industry for a long time, but its usage was limited ...
Work during this reporting period consisted of completing the data processing tasks begun in previou...
Work during this reporting period focused primarily on data processing in support of creation of the...
During this reporting period work on Task 4: Develop Integrated Engineering Model was completed, inc...
This project has completed the initially scheduled third year of the contract, and is beginning a fo...
Due to the character of the original source materials and the nature of batch digitization, quality ...
Today, the major challenge in reservoir characterization is integrating data coming from different s...
Neural networks are powerful and elegant computational tools that can be used in the analysis of geo...
Accurate, high-resolution, three-dimensional (3D) reservoir characterization can provide substantial...
SIGLEAvailable from British Library Document Supply Centre-DSC:DXN051362 / BLDSC - British Library D...
Traditional joint inversion methods reqnire an a priori prescribed operator that links the reservoir...
This study presents an intelligent model based on probabilistic neural networks (PNN) to produce a q...
In this work, we applied enhanced geophysical techniques to detect new prospecting zones at the Puer...
The project, "Calibration of Seismic Attributes for Reservoir Characterization," is now complete. Ou...
The first application is seismic inversion. Artificial neural networks were used to invert post-stac...
Machine learning has been used in the petroleum industry for a long time, but its usage was limited ...
Work during this reporting period consisted of completing the data processing tasks begun in previou...
Work during this reporting period focused primarily on data processing in support of creation of the...
During this reporting period work on Task 4: Develop Integrated Engineering Model was completed, inc...
This project has completed the initially scheduled third year of the contract, and is beginning a fo...
Due to the character of the original source materials and the nature of batch digitization, quality ...
Today, the major challenge in reservoir characterization is integrating data coming from different s...
Neural networks are powerful and elegant computational tools that can be used in the analysis of geo...
Accurate, high-resolution, three-dimensional (3D) reservoir characterization can provide substantial...
SIGLEAvailable from British Library Document Supply Centre-DSC:DXN051362 / BLDSC - British Library D...
Traditional joint inversion methods reqnire an a priori prescribed operator that links the reservoir...
This study presents an intelligent model based on probabilistic neural networks (PNN) to produce a q...
In this work, we applied enhanced geophysical techniques to detect new prospecting zones at the Puer...
The project, "Calibration of Seismic Attributes for Reservoir Characterization," is now complete. Ou...
The first application is seismic inversion. Artificial neural networks were used to invert post-stac...
Machine learning has been used in the petroleum industry for a long time, but its usage was limited ...