1887
Volume 40, Issue 9
  • ISSN: 0263-5046
  • E-ISSN: 1365-2397

Abstract

Abstract

3D seismic MegaMerges provide valuable information over large areas in mature petroleum provinces. While these surveys in combination with borehole data are useful for regional geological and geophysical analyses, they also offer an enormous opportunity for big-data analysis leading to new understandings. In this project, we combine seismic 3D-merges and well data in a geological context. The target is to predict petrophysical properties such as P-wave velocity (V), Bulk Density (RHOB), Porosity (), and Clay Volume (V) from well and seismic data using machine learning (ML) techniques.

Our workflow comprises five steps. In the first step, seismic 3D surveys are conditioned, reprocessed, and finally merged. In the second step, a low-frequency Acoustic Impedance (Z) model is generated using the well-log data over the whole MegaMerge before carrying out a model-based inversion to obtain a Z cube. In the third step, Multi-linear and different neural network algorithms are used for the ML training and validation analysis. In step 4, the well-log trained ML model is applied to predict V and RHOB from seismic. And in the final step 5, petrophysical relations and well data are used to predict V and ∅ from the ML-derived properties.

In the current example of the Northern North Sea (NNS) MegaMerge, this new approach shows excellent results, providing new insights into the basin geology, petroleum system, reservoir architecture and reservoir quality..

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2022-09-01
2022-10-03
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