Turning an Offshore Analog Field into Digital Using Artificial Intelligence

R. Espinoza, J. Thatcher, M. Eldred
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引用次数: 1

Abstract

Replacing all analogue sensors in the oil field is very costly and normally only a fraction of them is done. Currently, there is no cost-effective method to efficiently, reliably and accurately capture analogue meter readings in a digital format. Operators are then left with only two options: either replace them with digital (high capex) or continue with manual gathering (high opex). This paper shows how computer vision and artificial intelligence was used for the first time to capture analogue field gauges data with dramatic reduction of cost and increase reliability. This unique solution was implemented in the Cheleken Oil field, Caspian Sea, Turkmenistan. In the offshore platforms, only low-cost cameras were necessary, and gauges were identified using QR codes. During the field trial, operators were only required to take pictures of the gauges at a given interval of time and upload the photos to the application. After an innovative process of calibration, the acquired images were processed using artificial intelligence and deep learning computer vision. Routine manually gathered data was compared with data collected using this solution with the following observations made: Date/time: Operators usually round time. The solution described records time on the captured pictures automatically.Value: Manually gathered data is subject to reading, typing and transcription errors. This solution has no error (provided a good calibration is done).Data Modification: Data gathered automatically with this solution has no human intervention. Therefore, is not subject to alteration, copying or duplication.Data collection with pictures was completed in 1/10th of the time that manual processes take.The business benefits from quicker operator rounds with improved accuracy in meter reading data, and time stamps. The administrative burden for operators of filling in extensive spreadsheets which are prone to error was reduced, this allowed them to collect more meter readings or be reassigned by management to more important scopes of work that bring greater value to the business. Once more it was proved that "a picture is worth a thousand words ". This solution offers an excellent opportunity for digitizing the marginal section of the field and provides a unique way to turn all analogue data into digital with a very low cost of implementation, on an infinitely scalable platform that is vendor agnostic and simple to manage.
利用人工智能将海上模拟油田数字化
更换油田中所有的模拟传感器是非常昂贵的,通常只完成了其中的一小部分。目前,还没有经济有效的方法来高效、可靠和准确地捕获数字格式的模拟仪表读数。然后,运营商只有两种选择:要么用数字化(高资本支出)取代它们,要么继续人工采集(高运营成本)。本文展示了如何首次使用计算机视觉和人工智能来捕获模拟现场仪表数据,从而大大降低了成本并提高了可靠性。这种独特的解决方案在土库曼斯坦里海的Cheleken油田得到了应用。在海上平台,只需要低成本的相机,并且使用QR码识别量具。在现场试验期间,作业人员只需要在给定的时间间隔内拍摄测量仪的照片,并将照片上传到应用程序。经过创新的校准过程后,使用人工智能和深度学习计算机视觉对获取的图像进行处理。将常规人工采集的数据与使用该解决方案收集的数据进行比较,并进行以下观察:日期/时间:操作人员通常在一周内完成作业。该解决方案描述了自动记录捕获图片的时间。数值:手动采集的数据容易出现读取、输入和转录错误。该解决方案没有误差(提供了良好的校准)。数据修改:使用此解决方案自动收集的数据无需人工干预。因此,不得更改、复制或复制。带图片的数据采集完成时间是手工采集的1/10。业务受益于更快的操作员轮询,提高了抄表数据的准确性和时间戳。减少了操作员填写大量容易出错的电子表格的管理负担,这使他们能够收集更多的仪表读数,或者被管理层重新分配到更重要的工作范围,为业务带来更大的价值。再一次证明了“一幅画胜过千言万语”。该解决方案为油田边缘部分的数字化提供了绝佳的机会,并提供了一种独特的方式,以极低的实施成本将所有模拟数据转换为数字数据,该解决方案具有无限可扩展的平台,与供应商无关且易于管理。
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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