Full Automation, High Performance and Novel Insights: A New Sonic Processing Framework
Westside Houston
Speaker:
Seminar Date: Jun 25 2026
Registration Opens: Jun 04 2026 - Jun 25 2026
Time: 11:30 AM - 01:00 PM (US CDT)
Admission/Registration Link: None
Donation Link: None
Meeting/Webinar Link: None
Contact: Tarek Sabry-Mohamed (VP Westside, SPWLA Houston Chapter)
Corresponding: vpwestside@spwla-houston.org
Fees: FREENOTES:
Speaker : Chongbing Liu
Date : Thursday, June 25, 2026
Time : 11:30 am – 1:00 pm (US CDT)
Venue : SLB, 6350 West Sam Houston Parkway North, Houston, TX 77041
Admission : This activity will include a boxed lunch.
The seminar is sponsored by SLB so there is no charge for registration,
However, you still need to register using the applicable links below.
Parking Info : Guest parking is available free of charge. Upon arrival, please proceed to the front desk to check in
Please register by one day before the event to reserve lunch using the above provided link.
Contact : Tarek Sabry-Mohamed and QinShan (Shan) Yang (SPWLA Houston VP Westside)
Corresponding : vpwestside@spwla-houston.org
ABSTRACT:
Sonic measurements play a crucial role in well construction and reservoir evaluation, and sonic processing software is essential for transforming the raw data to operational insights with accuracy in a timely manner. This presentation introduces a new sonic processing framework that is fully automated, high-performance, and capable of delivering novel insights. The structure of the framework includes common modules such as unpacking raw sonic waveforms, preparing input data, (D)STC processing, Alford rotation, and computing compressional and shear slownesses. The framework also incorporates sonic data classification to quickly characterize azimuthal anisotropy and borehole condition. The key techniques that enable full automation and high performance include the followings. 1. Multi-Resolution Tracking (MRT), which is an analysis of the monopole data for the compressional and shear slowness that uses automatic peak detection on multiple receiver levels, removing any subjective manual labeling after semblance processing. 2. Machine Learning Aided Dipole Inversion (MLADI), which computes shear slowness from dipole waveforms using a physics-based machine learning without the need of processing parameter tuning unlike the traditional (D)STC processing. 3. Sonic data classification, which uses machine learning and MLADI outputs to classify azimuthal anisotropy, borehole ovality and VTI feature in vertical wells. 4. Integration of complex work steps to a single workflow without user interaction. 5. High-Performance Computing (HPC) methods, including parallel processing and MPI, to parallelize independent computation steps and process multiple depths simultaneously whenever feasible. This sonic processing framework accelerates the transformation of raw sonic waveforms to interpretation-ready outputs, completing in hours rather than days.
BIOGRAPHY:
Chongbing Liu is a Senior Software Engineer in the Interpretation Engineering group at SLB-Houston Formation Evaluation center. He joined Schlumberger in 2018. His expertise includes software development, well logging interpretation, cloud-native development, high-performance computing, and machine learning. Chongbing received his master’s degree in geophysics from China University of Geosciences in 1994 and his Ph.D. in computer science from New Mexico State University in 2008. He is a member of SEG.