利用基于实时数据的机器学习算法预测等效循环密度

IF 1.8 Q4 ENERGY & FUELS
AIMS Energy Pub Date : 2023-01-01 DOI:10.3934/energy.2023023
Abdelrahman Kandil, S. Khaled, T. Elfakharany
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引用次数: 0

摘要

当量循环密度(ECD)是设计钻井方案时应考虑的最重要参数之一。随着深海油气开采井数量的增加、每天昂贵的井下测量费用、作业限制以及全球市场价格的波动,有必要减少因忽视和错误评估ECD而导致的井眼问题所带来的非生产时间和成本。因此,优化ECD和选择最佳钻井参数是此类作业的关键任务。这项工作的主要目标是使用三种机器学习算法来预测ECD:具有Levenberg-Marquardt反向传播算法的人工神经网络(ANN), K邻居回归器(knn)和被动攻击回归器(par)。这些模型基于钻井过程中井下传感器提供的14个关键操作参数,如环空压力、环空温度和钻速等。在本研究中,选取并纳入了4663个数据点,其中80% - 85%的数据集已经按照算法进行了训练和验证,剩余的数据点保留用于测试。此外,还采用了包括均方根误差(RMSE)、相关系数(R2)和均方误差(MSE)在内的多项统计检验来评价模型的准确性。所开发的模型的结果具有不同的一致性和准确性,而人工神经网络在训练、验证和测试以及整体上都具有较高的准确性,R2接近0.999。对于总体、训练、验证和测试,RMSE分别为0.000211、0.000253、0.00293和0.00315。这项工作扩大了人工智能在天然气和石油行业的应用。开发的人工神经网络模型在应对挑战方面更加灵活,减少了对人工的依赖,从而减少了人为遗漏的机会,提高了操作效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Prediction of the equivalent circulation density using machine learning algorithms based on real-time data
Equivalent circulation density (ECD) is one of the most important parameters that should be considered while designing drilling programs. With increasing the wells' deep, offshore hydrocarbon extraction, the costly daily rate of downhole measurements, operating restrictions, and the fluctuations in the global market prices, it is necessary to reduce the non-productive time and costs associated with hole problems resulting from ignoring and incorrect evaluation of ECD. Therefore, optimizing ECD and selecting the best drilling parameters are curial tasks in such operations. The main objective of this work is to predict ECD using three machine learning algorithms: an artificial neural network (ANN) with a Levenberg-Marquardt backpropagation algorithm, a K neighbors regressor (knn), and a passive aggressive regressor (par). These models are based on 14 critical operation parameters that have been provided by downhole sensors during drilling operations such as annular pressure, annular temperature, and rate of penetration, etc. In the study, 4663 data points were selected and included, where 80% to 85% of the data set has been used for training and validation according to the algorithm, and the remaining data points were reserved for testing. In addition, several statistical tests were used to evaluate the accuracy of the models, including root mean square error (RMSE), correlation coefficient (R2), and mean squared error (MSE). The results of the developed models show various consistencies and accuracy, while the ANN shows a high accuracy with an R2 of nearly 0.999 for the training, validation, and testing, as well as the overall of them. The RMSE is 0.000211, 0.000253, 0.00293, and 0.00315 for overall, training, validation, and testing, respectively. This work expands the use of artificial intelligence in the gas and oil industry. The developed ANN model is more flexible in response to challenges, reduces dependence on humans, and thus, reduces the chance of human omission, as well as increasing the efficiency of operations.
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来源期刊
AIMS Energy
AIMS Energy ENERGY & FUELS-
CiteScore
3.80
自引率
11.10%
发文量
34
审稿时长
12 weeks
期刊介绍: AIMS Energy is an international Open Access journal devoted to publishing peer-reviewed, high quality, original papers in the field of Energy technology and science. We publish the following article types: original research articles, reviews, editorials, letters, and conference reports. AIMS Energy welcomes, but not limited to, the papers from the following topics: · Alternative energy · Bioenergy · Biofuel · Energy conversion · Energy conservation · Energy transformation · Future energy development · Green energy · Power harvesting · Renewable energy
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