Drilling a high-pressure, high-temperature (HPHT) well involves many difficulties and challenges. One of the greatest difficulties is the loss of circulation. Almost 40% of the drilling cost is attributed to the drilling fluid, so the loss of the fluid considerably increases the total drilling cost. There are several approaches to avoid loss of return; one of these approaches is preventing the occurrence of the losses by identifying the lost circulation zones. Most of these approaches are difficult to apply due to some constraints in the field. The purpose of this work is to apply three artificial intelligence (AI) techniques, namely, functional networks (FN), artificial neural networks (ANN), and fuzzy logic (FL), to identify the lost circulation zones. Real-time surface drilling parameters of three wells were obtained using real-time drilling sensors. Well A was utilized for training and testing the three developed AI models, whereas Well B and Well C were utilized to validate them. High accuracy was achieved by the three AI models based on the root mean square error (), confusion matrix, and correlation coefficient (). All the AI models identified the lost circulation zones in Well A with high accuracy where the is more than 0.98 and is less than 0.09. ANN is the most accurate model with and . An ANN was able to predict the lost circulation zones in the unseen Well B and Well C with and and and , respectively.

中文翻译：

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人工智能技术在钻井传感器预测漏失带中的应用

钻高压高温（HPHT）井涉及许多困难和挑战。最大的困难之一是血液循环的丧失。钻井成本的近40％归因于钻井液，因此钻井液的损失大大增加了总钻井成本。有几种方法可以避免收益的损失。这些方法之一是通过识别丢失的循环区域来防止丢失的发生。由于该领域的某些限制，这些方法中的大多数很难应用。这项工作的目的是应用三种人工智能（AI）技术（即功能网络（FN），人工神经网络（ANN）和模糊逻辑（FL））来识别丢失的循环区域。使用实时钻井传感器获得了三口井的实时地面钻井参数。A井用于训练和测试三个开发的AI模型，而B井和C井用于验证它们。三种AI模型基于均方根误差（），混淆矩阵和相关系数（）。所有的AI模型都可以高精度地识别出A井中大于0.98而小于0.09的漏失带。ANN是最准确的模型 和 。人工神经网络可以预测看不见的B井和C井的漏失带 和 和 和 ，分别。