储层描述

机器学习释放泰国湾泥浆测井和随钻测井的潜力

本研究旨在利用机器学习技术通过分析泥浆记录和随钻测井数据来预测测井曲线。

流程图展示了从数据收集到模型部署的预测工作流程。该流程图强调了模型开发和部署的关键阶段，包括数据预处理、特征创建、模型训练、模型评估和模型部署。
流程图展示了从数据收集到模型部署的预测工作流程。该流程图强调了模型开发和部署的关键阶段，包括数据预处理、特征创建、模型训练、模型评估和模型部署。
来源：SPE 222299

泰国湾（GOT）的该气田是泰国最大的天然气储量之一，拥有超过30年的开发历史，已钻井超过1000口。使用机器学习（ML）进行测井曲线合成有助于降低传统测井方法相关的成本和运营风险，包括服务费、钻机时间和潜在的检索挑战。本研究旨在利用机器学习技术，通过分析泥浆录井和随钻测井（LWD）数据来预测测井曲线。

介绍

在GOT钻井面临着高温、高压和复杂地质构造的挑战，需要强大的测井技术和细致的解释才能确保钻井成功。许多井的测井数据不完整，这给开发规划带来了挑战。

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原文链接/JPT
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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