Artificial Lift Selection Using Machine Learning

Thanawit Ounsakul, Thum Sirirattanachatchawan, Wiwat Pattarachupong, Yaovanart Yokrat, P. Ekkawong
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引用次数: 5

Abstract

The artificial lift selection process performed by human involves iterating of several design parameters. Moreover, the human's curated selection required the decision making with unbiased, repeatable and reliable. Capturing the lesson learned from the previous mistake into the new design and lack of look back in the past performances are the limits of human. The supervised machine learning method can apply to improve selection process. This approach can minimize the life-cycle cost of artificial lift wells by using machine learning which incorporate the past performances and lesson learnt from installations. The data is prepared into a structured dataset. The dataset is pre-processed to determine the "Good" and "Bad" wells based on their life-cycle cost, then used for training and validating the classification models. The most simple and accurate model is adopted for future artificial lift selection and current wells’ performance assessment. Finally, the performance of new wells is continuously added for further model's training. The artificial lift suggested by the machine learning expects reducing life-cycle cost in the ongoing trial in the fields. In term of assessing tool, the selection model reveals some discrepancy in the current installed artificial lift. This alerts the operator to look inside the potential problems. However, the subject matter experts still need to give an adequate interaction in case of false alarm. Therefore, the discovered pattern for good artificial lift selection will help improve the fields’ production. In addition, the endless learning capability of machine learning allows the new data feeds into the existing dataset and further incorporates the model in order to response to the dynamic change of the fields’ conditions. In conclusion, machine learning process is more comprehensive comparing to the selection made by conventional process where only few tables used for the artificial lift selection and overlook the value of data captured. The Artificial Intelligence is one of the emerging technologies which provides the breakthrough results. This paper presents the artificial intelligence trend in oil and gas industry. It is a promising tool which help solving human's complex problems. Ultimately, adding the durable competitive advantage to the oil and gas industry.
利用机器学习进行人工举升选择
人工举升的选择过程涉及多个设计参数的迭代。此外,人类的精心选择要求决策具有无偏、可重复和可靠的特点。把以前的错误吸取的教训融入到新的设计中,对过去的表演缺乏回顾,这是人类的极限。有监督的机器学习方法可以应用于改进选择过程。这种方法通过使用机器学习,结合过去的性能和从安装中吸取的经验教训,可以最大限度地降低人工举升井的生命周期成本。将数据准备成结构化数据集。对数据集进行预处理,以根据其生命周期成本确定“好”和“坏”井,然后用于训练和验证分类模型。采用最简单、最准确的模型进行未来的人工举升选择和当前井的动态评价。最后,不断添加新井的性能,以进一步训练模型。机器学习提出的人工举升技术有望在油田的持续试验中降低生命周期成本。从评价工具的选择模型来看,目前已安装的人工举升存在一定的差异。这提醒操作员查看潜在问题的内部。然而,在发生误报的情况下,主题专家仍然需要给予足够的互动。因此,所发现的人工举升模式有利于提高油田的产量。此外,机器学习的无限学习能力允许将新数据馈送到现有数据集中,并进一步合并模型,以响应领域条件的动态变化。总之,机器学习过程比传统过程的选择更全面,传统过程只使用少量表格进行人工举升选择,忽略了所捕获数据的价值。人工智能是提供突破性成果的新兴技术之一。本文介绍了人工智能在油气行业的发展趋势。它是一个很有前途的工具,可以帮助解决人类的复杂问题。最终,为石油和天然气行业增加持久的竞争优势。
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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