1 / 13

Predicting Sediment Undrained Shear Strength from Geophysical Logs Using Neural Networks

This study explores a non-destructive approach to predict the undrained shear strength of sediments using neural networks and geophysical logs. Traditional methods like drilling and coring are expensive and may not capture the full range of sediment properties. By employing Multi-Sensor Core Logger (MSCL) and downhole wireline logging, the research leverages data on compressional wave velocity and other geophysical properties to develop a predictive model. Results indicate that the predictions, while not perfect, provide valuable insights, significantly reducing the need for actual measurements.

ivy-langley
Télécharger la présentation

Predicting Sediment Undrained Shear Strength from Geophysical Logs Using Neural Networks

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Prediction of Sediment Undrained Shear Strength from Geophysical Logs Using Neural Networks M. Paulson, J. Ressler; and K. Moran and C. Baxter OCE 582 Professor: Dr. Moran Presented By: Sean-Philip Bolduc Date: Oct. 16, 2008

  2. Motive • Current Practice for investigation of Physical Properties of sediments: • Drilling and Coring  Very Expensive! • Samples of each core section is very valuable

  3. Objective • Use Non-Destructive Testing of Sediment Cores • Give more information then sampling alone • Two Non-Destructive Testing Techniques: • Multi-Sensor Core Logger (MSCL) • Downhole Wireline Logging

  4. Goal • Data collected from “Non-Destructive” Processes to obtain Sediment Properties Typically found Using Destructive Processes • I.E. Undrained Shear Strength • Using a Vane Shear Device • Using a Neural Network to predict the Undrained Shear Strength

  5. Backround • Multi-Sensor Core Logger (MSCL) • Laboratory Testing • Uses Compressional Wave Velocity and Magnetic Susceptibility at high Resolution

  6. Backround • In-Situ Downhole Wireline Logging • Provides continuous record of several geophysical properties • Natural Gamma Ray • Neutron Porosity • Bulk Density • Resistivity • Photoelectric Effect • P-wave Velocity • Spectral Gamma Ray (Thorium, Uranium, Potassium) • Caliper

  7. Backround • Neural Network • Pattern Recognition Computer • Future responses are dictated by outcome of previous experiences • Operates as Two Phase System • Training or “Learning” Phase • Predictive Phase • Applies learned relationship to new input data, predicting output response

  8. Data Sites • Two Sites used for this Study • Jumbo Piston Cores (Gulf of Mexico) • Samples Cored and Logged using MSCL • Marine Clays • ODP Leg 162 (North Atlantic Gateways II) • Downhole wireline geophysical logs • Marine Clays

  9. Procedure • Run the Neural Network Program to Predict the Undrained Shear Strength • Train the Neural Network with the first half of the data from each site • Use the second half of the data to compare the prediction of the Neural Network Program

  10. Results • Gulf of Mexico • Prediction not perfect but typically within 10 kPa • Accurately anticipated Increase in Su at about 8.3 mbsf

  11. Results • ODP Leg 162 • Based off Downhole Wireline Logs • Good Predictions by Neural Network

  12. CONCLUSION • Successful method of Prediction using data obtained from MSCL core Logger or Downhole Wireline Logs • Future in use of Neural Network Prediction  reducing number of actual measurements required

  13. Questions ?

More Related