解决钻井挑战的最佳人工智能技术是什么?

S. Gharbi, Abdul Azeez Al Majed, A. Abdulraheem, S. Patil, S. Elkatatny
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引用次数: 0

摘要

钻井作业开始钻更远、更深、非常规条件下更具挑战性的井;这就要求钻井行业采用新技术来应对这些挑战。其中一个潜力巨大的支持技术是人工智能。人工智能可以帮助钻井工程师和作业人员处理大量钻井数据,将其转换为类似决策的格式,从而实现更安全、更快速、更经济的作业。挑战在于,人工智能项目由多维任务组成,从数据处理、基础设施建设开始,通过模型开发和与现有环境集成。这样的任务甚至会让IT团队感到困惑,因此开发针对钻井领域的人工智能项目将非常困难。经验表明,许多油气人工智能项目的失败是由于业务领域专家与人工智能之间的沟通不畅,没有共同的理解,数据中的问题或模型可计算性问题也可能是此类失败的其他原因。本文提出了一种提高人工智能钻井项目成功可能性的方法。这种方法就是CRISP-DM,即跨行业数据挖掘标准过程。该方法由以下阶段组成:业务理解、数据理解、数据准备、建模、评估和部署。本文对这些阶段进行了阐述,并将通过钻井案例研究来引导读者,该方法已成功应用于人工智能钻井项目。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
What is the Best Artificial Intelligent Technology to Solve Drilling Challenges?
Drilling operation started drilling more challenging wells that is farer, deeper and in unconventional conditions; this require the drilling industry to adopt new technologies supporting them in these challenges. One of the big potential supporting technologies is the artificial intelligent. Artificial intelligent could help drilling engineers and operation crew in crunching the massive amount of drilling data converting them to decision-like format, leading to safer, faster and more cost effective operations. The challenge is that artificial intelligent projects consists of multi dimension tasks, starting from data handling, infrastructure building, through model development and integrating with existing environment. Such tasks confuses even IT teams, so developing artificial intelligent projects targeting drilling domain will be very tough. Experience shows that a lot of oil & gas artificial intelligent projects fails due to miscommunication between the business domain experts and the artificial intelligent, not having common understanding, problem in the data, or model computability issues could be also other reason for such failure. This paper propose a methodology that will increase the possibility of having success artificial intelligent drilling project. This methodology is CRISP-DM which stand for Cross Industry Standard Process for Data Mining. This methodology consist of the following phases Business Understanding, Data Understanding, Data Preparation, Modeling, Evaluation and Deployment. This paper is shading lights on these phases, also it will derive the readers through a drilling case-study, where this methodology was applied leading to successful artificial intelligent drilling project.
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