Reservoir characterization

Machine-Learning Method Determines Salt Structures From Gravity Data

The authors develop an innovative machine-learning method to determine salt structures directly from gravity data.

gravity field overlaid on seismic
Fig. 1—Continued gravity field overlaid on seismic. Correlation between the base of the body with observed short wavelengths in the gravity is expected because of the relationship between the potential field and the source. The vertical axis represents depth, and the horizontal axis represents one of the spatial coordinates.

In the complete paper, the authors develop a machine-learning (ML) method to determine salt structures directly from gravity data. Based on a U-net deep neural network, the method maps the gravity downward continuation volume directly to a salt body mask volume, which is easily interpretable for an exploration geophysicist. The authors conclude that the ML-based method from gravity data complements seismic data processing and interpretation for subsurface exploration.

Introduction

In subsurface exploration, seismic is the dominant method used to reconstruct the underground image for geophysicists and geologists to locate possible hydrocarbon reservoirs. Seismic acquisition is carried out by human-induced sound waves (by airgun or vibrators) that are recorded, once reflected, on the surface.

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