18551216. AUTOMATIC SALT GEOMETRY DETECTION IN A SUBSURFACE VOLUME simplified abstract (Schlumberger Technology Corporation)
Contents
- 1 AUTOMATIC SALT GEOMETRY DETECTION IN A SUBSURFACE VOLUME
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 AUTOMATIC SALT GEOMETRY DETECTION IN A SUBSURFACE VOLUME - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
AUTOMATIC SALT GEOMETRY DETECTION IN A SUBSURFACE VOLUME
Organization Name
Schlumberger Technology Corporation
Inventor(s)
Chunpeng Zhao of Houston TX (US)
Anisha Kaul of Houston TX (US)
Aria Abubakar of Houston TX (US)
AUTOMATIC SALT GEOMETRY DETECTION IN A SUBSURFACE VOLUME - A simplified explanation of the abstract
This abstract first appeared for US patent application 18551216 titled 'AUTOMATIC SALT GEOMETRY DETECTION IN A SUBSURFACE VOLUME
Simplified Explanation
The method described in the abstract involves using seismic data and machine learning to improve velocity models for salt detection in subsurface imaging.
- Receiving seismic data and an initial velocity model
- Generating a first seismic image based on the seismic data and initial velocity model
- Training a machine learning model to predict salt masks from seismic images
- Merging the initial velocity model and the first salt mask to generate a modified velocity model
- Generating an updated velocity model based on the modified velocity model
- Generating a second seismic image based on the updated velocity model
- Predicting a second salt mask based on the second seismic image and updated velocity model
- Merging the updated velocity model and second salt mask to generate a second modified velocity model
Potential Applications
This technology can be applied in oil and gas exploration, geophysical surveys, and subsurface imaging for various industries.
Problems Solved
This technology helps improve the accuracy and efficiency of subsurface imaging by enhancing velocity models for salt detection.
Benefits
The benefits of this technology include more precise imaging of subsurface structures, better identification of salt bodies, and increased productivity in exploration activities.
Potential Commercial Applications
The potential commercial applications of this technology include oil and gas exploration companies, geophysical survey firms, and companies involved in subsurface imaging for infrastructure projects.
Possible Prior Art
Prior art in this field may include traditional seismic imaging techniques, manual interpretation of seismic data, and basic velocity model building methods.
What are the limitations of this technology in real-world applications?
The limitations of this technology in real-world applications may include the need for high-quality seismic data, computational resources for training machine learning models, and potential challenges in integrating the updated velocity models into existing workflows.
How does this technology compare to existing methods for salt detection in subsurface imaging?
This technology offers a more automated and data-driven approach to salt detection compared to traditional methods, potentially leading to faster and more accurate results in subsurface imaging projects.
Original Abstract Submitted
A method includes receiving seismic data and an initial velocity model, generating a first seismic image based at least in part on the seismic data and the initial velocity model, training a machine learning model to predict salt masks based at least in part on seismic images, merging the initial velocity model and the first salt mask to generate a first modified velocity model, generating an updated velocity model based at least in part on the first modified velocity model, generating a second seismic image based at least in part on the updated velocity model, predicting a second salt mask based at least in part on the second seismic image and the updated velocity model, using the trained machine learning model, and merging the updated velocity model and the second salt mask to generate a second modified velocity model.
