利用人工神经网络预测钻机表面泵压的新模型

IF 1.3 4区 工程技术 Q3 CHEMISTRY, ORGANIC
Sahmee Eddwan Mohammed, Duraid Al-Bayati, Yahya Jirjees Tawfeeq
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引用次数: 0

摘要

机器学习和人工智能最近被广泛应用于许多工程领域。人工神经网络(ANN)已广泛应用于石油和天然气领域,用于预测许多重要参数。考虑到不同钻井参数的影响,这项工作使用人工神经网络预测所需的地表泵压力。这些参数包括:深度、穿透率 (ROP)、钻头重量 (WOB)、每分钟转速 (RPM)、每分钟冲程 (SPM)、泥浆重量和泥浆流速。使用两层建立了 ANN 模型,并使用双曲 Tanh 和对数 sigmoid 传递函数来预测模型的有效性。约 2020 个数据值用于测试、训练和验证模型预测。灵敏度分析对每个传递函数(对数 sigmoid 和双曲 Tanh)分别使用了 2、4、8 和 10 个神经元。结果表明,八节点 Tanh 模型的预测结果与测试可用的整体数据最为匹配。例如,训练数据集的 R 值为 99.67%,测试数据集的 R 值为 99.45%,验证数据集的 R 值为 98.57%,总体数据集的 R 值为 99.47%。另一方面,对于上述测试的相同数据,使用具有 10 个节点的 Log 模型提供了最佳的数据集匹配。结果显示,测试数据与模型预测方法的收敛率为 99.58,而训练和验证的收敛率分别为 99.52 和 98.95。因此,我们建议使用基于 Log 模型的新模型来预测表层泵压力。该模型将有助于预测任何钻井现场所需泵的数量和大小。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

A New Model for Predicting Surface Pump Pressure of Drilling Rig Using Artificial Neural Network

A New Model for Predicting Surface Pump Pressure of Drilling Rig Using Artificial Neural Network

Machine learning and artificial intelligence are recently used in many engineering sectors. Artificial neural network (ANN) has been widely used in oil and gas to predict many important parameters. This work uses ANN to predict the required surface pump pressure at the surface, considering the impact of different drilling parameters. These parameters are: depth, rate of penetration (ROP), weight on bit (WOB), rotation per minute (RPM), stroke per minute (SPM), mud weight, and mud flow rate. ANN models were built using two layers, and both hyperbolic Tanh and Log sigmoid transfer functions were used to predict the model’s validity. Around 2020 data values were used to test, train and validate model prediction. Sensitivity analysis used 2, 4, 8, and 10 neurons for each transfer function (Log sigmoid and hyperbolic Tanh). Results indicated that the prediction for the eight nodes Tanh model best matches the overall data available for the test. For instance, a 99.67% R for training, 99.45% test, 98.57% validation, and 99.47% overall data set were obtained. On the other hand, using a Log model with ten nodes offered the best data set matching for the same data tested above. Results show that test data converged 99.58 with the model prediction method, while 99.52 and 98.95 were obtained for training and validation, respectively. Therefore, we suggest a new model based on the Log model to predict surface pump pressure. This model would be beneficial for predicting the required number and size of pumps at any drilling site.

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来源期刊
Petroleum Chemistry
Petroleum Chemistry 工程技术-工程:化工
CiteScore
2.50
自引率
21.40%
发文量
102
审稿时长
6-12 weeks
期刊介绍: Petroleum Chemistry (Neftekhimiya), founded in 1961, offers original papers on and reviews of theoretical and experimental studies concerned with current problems of petroleum chemistry and processing such as chemical composition of crude oils and natural gas liquids; petroleum refining (cracking, hydrocracking, and catalytic reforming); catalysts for petrochemical processes (hydrogenation, isomerization, oxidation, hydroformylation, etc.); activation and catalytic transformation of hydrocarbons and other components of petroleum, natural gas, and other complex organic mixtures; new petrochemicals including lubricants and additives; environmental problems; and information on scientific meetings relevant to these areas. Petroleum Chemistry publishes articles on these topics from members of the scientific community of the former Soviet Union.
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