Seismic well tie is a critical process to verify the time-depth relationship of a well. This process requires density and sonic transit time data. However, sonic logs are usually not acquired due to cost saving, unfavorable well path, or other operational issues. Attempts to generate synthetic logs by Gardner equation, porosity correlation, or depth correlation did not provide the required accuracy. Therefore, the goal of our project was to generate synthetic sonic logs using machine learning technique for seismic well ties. This paper will compare the different methods tested, compare the results and lists the advantages of using Machine Learning.

This approach uses machine learning technique to create synthetic sonic logs. The machine learning model is trained to predict sonic log from other relevant logs. The model representativeness is confirmed by blind tests, which consists of two steps. The first step compares the synthetic sonic logs to the actual sonic logs. In the second step, four synthetic seismograms are generated from actual sonic, machine learning synthetic sonic, Gardner predicted sonic, and averaged constant sonic. The seismic well ties are compared between those four synthetic seismograms. Once the machine learning synthetic and actual logs show similar results, the model is deemed good and can be applied on wells that do not have sonic logs. The synthetic seismograms are then generated using synthetic sonic logs for all the wells that do not have actual sonic logs.

The use of synthetic sonic logs gives us the ability to

  1. Generate synthetic seismogram to tie wells that do not have sonic data

  2. Reduce the number sonic data acquisition, saving time and money

  3. Reduce the risk of long logging string getting stuck in the hole that would requires fishing operations and its associated cost.

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