Reservoir characterization

Machine Learning Unlocks Potential of Mud Logs, LWD in the Gulf of Thailand

This study aims to use machine-learning techniques to predict well logs by analyzing mud-log and logging-while-drilling data.

Flowchart illustrating the predictive workflow that starts with data collection and eds withmodel deployment. The flowchart emphasizes key stages of model development and deployment,including data preprocessing, feature creation, model training, model evaluation, and model deployment.
Flowchart illustrating the predictive workflow that starts with data collection and eds withmodel deployment. The flowchart emphasizes key stages of model development and deployment,including data preprocessing, feature creation, model training, model evaluation, and model deployment.
Source: SPE 222299.

The subject gas field in the Gulf of Thailand (GOT) stands as one of the largest natural gas reserves in Thailand, with over 30 years of development history and more than 1,000 penetrated wells. Use of machine learning (ML) for log synthesis can help reduce expenses and operational risks associated with traditional well-logging methods, including service fees, rig time, and potential retrieval challenges. This study aims to use ML techniques to predict well logs by analyzing mud-log and logging-while-drilling (LWD) data.

Introduction

Drilling in the GOT faces challenges involving high temperatures, pressures, and complex geological structures, demanding robust logging techniques and careful interpretation to ensure successful outcomes. Many wells feature incomplete well-log data, which complicates development planning.

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