[7]P6 Sonic/Density Well Log Data Editing With Pseudo Curve Generation – Indonesian Examples Using a Multiple Crossproduct Non-Linear Method

Sonic and density well log data are vital for geophysicists, petrophysicists, engineers and geologists in the search and evaluation of economic oil and gas reserves. In many cases, however, the data are missing or need editing due to environmental effects such as sonic cycle skipping or density rugose hole and washout effects. In the past, most methods relied on manual well log editing or pseudo curve generation using single logs.

A new method has been developed that automatically edits sonic and density logs and generates pseudo curves from combinations of other logs. By incorporating the crossproducts of multiple curves as an addition to the conventional linear multiple regression techniques, more accurate non-linear data correlation can be obtained which yield better results.

Preliminary applications of this new approach include improved synthetic seismograms, pseudo sonic/density log predictions, replacement of bad or missing well log data, calibration of logs to core data, and better well log quality control. For example, gamma ray, density and neutron data may be calibrated to generate a pseudo sonic curve that can be used in places where the sonic is missing or bad.

This method has been tested in Indonesia over the last few years and several examples demonstrate the benefits and advantages of using this approach. Additional future applications such as permeability estimations and production prediction may also be possible.

PROGRAM APPLICATIONS curve to determine coefficients that can be

1. Well log data quality control

2. Edit sonic logs to remove cycle skips

3. Edit density logs for borehole washout effects

4. Edit other logs for environmental effects

5. Verify questionable log responses

6. Generate curves to replace bad or missing data

7. Sonic log prediction

8. Calibrate logs to core data and generate data in non-cored zones

9. Other applications-possible DST, production prediction P ROG RAM D ES C R I PTIO N The automatic curve editing and generating program is divided into two separate parts: Step 1: (ACECAL-Automatic Curve Editing and Calibration) The ACECAL program calibrates three input curves to a fourth input reference applied to the three input curves for generating a new reference curve using Step 2.

Step 2: (ACEGEN-Automatic Curve Editing and Generation) The ACEGEN program applies the coefficients previously determined from ACECAL to the three input curves to generate a new reference curve that can be used to replace the original reference curve.

PROGRAM METHOD A reference curve such as a sonic, density, etc. is selected for editing or calibration. Three other curves that are anticipated to have some reasonable correlation to the