Prediction of Sediment Undrained Shear Strength from Geophysical Logs Using Neural Networks

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 ?