Wireline sonic

Quality downhole sonic measurements

SLB wireline logging truck

Acquiring critical downhole data

Wireline sonic solutions measure porosity by analyzing the speed of sound waves through the formation. Various formation and borehole conditions—such as natural fractures, compaction, fluid-filled porosity, mud filtrate, and temperature—can impact the quality of interval transit time (DT) measurements, which are used to provide quantitative formation evaluation. Wireline sonic from SLB leverages the industry’s most advanced sensors and workflows to deliver quality downhole information faster—for the most accurate picture of your reservoir.

High-resolution wireline sonic data drive higher performance through better reservoir understanding.

Advancing acoustic acquisition

Our portfolio of advanced dipole sonic solutions accurately measure elastic properties axially, radially, and azimuthally to support geomechanical, geophysical, fracture, and petrophysical modeling in a wide range of borehole environments. So you get a deeper, clearer picture of your reservoir. Find all the answers you need to characterize your reservoir, optimize drilling and completion designs, maximize production, and evaluate cement bond quality—in even the most challenging formations.

All-new automated acoustic interpretation capabilities harness the power of Wellbore Insights on Delfi™ and edge computing to deliver insights faster than ever before. Just one click delivers immediate compressional and shear slowness, dipole anisotropy, vertical transverse isotropy (VTI) elastic property, and sonic classification data to support rapid decision making.

3D Far-Field Sonic Service

The 3D far-field sonic service leverages acoustic data to extend sonic imaging far beyond the reach of standard sonic logging.

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Our services

Automated subsurface feature extraction

Acoustic scanning platform for downhole acoustic measurements

Through-the-bit logging services

Sonic measurement of the bond between casing and cement

Dual-string barrier evaluation