基于机器学习的东苏里格油田水力压裂井生产动态评价

DEStech Transactions on Environment, Energy and Earth Science Pub Date : 2021-03-20 DOI:10.12783/DTEEES/PEEES2020/35499

Xianlin Ma, De-sheng Zhou, Wenbing Cai

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摘要

本文提出了一个综合的工作流程，将机器学习算法与蒙特卡罗模拟相结合，并提供了一个现场实例来证明所提出的工作流程可以合理地捕获油井生产数据的行为。该工作流程可以帮助工程师从以往的作业中吸取宝贵的经验教训，以优化苏里格气田未来的水力压裂处理方法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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The Production Performance Evaluation of Hydraulically Fractured Wells in the East Sulige Field Using Machine Learning

The paper presents a comprehensive workflow to integrate the machine learning algorithm with the Monte Carlo simulation, and a field example is provided to demonstrate that the proposed workflow could reasonably capture the behaviour of well production data. The workflow helps engineers in learning valuable lessons from their historical operations to optimize the future hydraulic fracturing treatments in the Sulige gas field.

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DEStech Transactions on Environment, Energy and Earth Science

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