High resolution seismic data derived from pre-stack inversion and machine learning
US10802171B2 · kind B2 · utility
Assignee
Inventor
Key dates
| Filing date | Apr 27, 2018 |
| Grant date | Oct 13, 2020 |
| Priority date | — |
| Expiry date | Apr 12, 2039 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG01V2210/6161
- WIPO fieldMeasurement
- WIPO sectorInstruments
Abstract
A system and method combines model-based inversion and supervised neural networks to develop high resolution rock property volumes from surface seismic data. These volumes have higher frequency and are calibrated to fit well log data. In addition to rock volumes, a Reflection Coefficient (RC) volume is derived from the acoustic impedance volume. The RC volume has much higher frequency, better lateral continuity, and ties to the well logs better than conventional seismic or frequency enhanced data. By interpreting and mapping with this RC volume, a much more accurate depth model can be built, which allows for a horizontal well to be accurately drilled.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.