Sand management is one of the key component of Bongkot production processes. Current sand production prediction is based on a model which requires sonic and density logs for all the wells. However, a combination of complex well architecture and focus on reducing well cost resulted in many wells not having acquired these important logs. This project has implemented new technique of "Artificial Neural Network" to solve this problem. Using this method, synthetic logs are generated to obtain the values of missing sonic and density data. These data are then used in the existing sand models to predict sand production potential.

This project was evaluated with three field cases. The sand failure predictions based on synthetic rock properties matched with actual sand production. Therefore, the sand prediction workflow has been updated to include log synthetic if acroustic or density log are missing.

Keywords:
artificial neural network, actual sonic transit time, artificial intelligence, reservoir, bulk density, sand model, application, prediction, accuracy, predict sand production
Subjects:
Flow Assurance, Formation Evaluation & Management, Information Management and Systems, Solids (scale, sand, etc.), Open hole/cased hole log analysis, Artificial intelligence, Neural networks
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2018. Offshore Technology Conference
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