Predictive models have been widely used in different engineering fields, as well as in petroleum engineering. Due to the development of high-performance computer systems, the accuracy and complexity of predictive models have been increased significantly. One of the common methods for prediction is artificial neural network (ANN). ANN models in combination with optimization algorithms provide a powerful and fast tool for the prediction and optimization of processes which take a large amount of time if they are simulated using common simulation technics. In the present paper, to predict penetration rate during drilling process, several ANN models were developed based on the data obtained from drilling of a gas well located in south of Iran. Regarding the R 2 and RMSE values of the developed models, the best model was selected for prediction of penetration rate. In the next step, artificial bee colony algorithm was used for optimization of the parameters which are effective on rate of penetration (ROP). Results showed that the model is accurate enough for being used in the prediction and optimization of ROP in drilling operations.

中文翻译：

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一种在钻井作业中优化和预测钻速的新方法

预测模型已广泛应用于不同的工程领域以及石油工程。由于高性能计算机系统的发展，预测模型的准确性和复杂性已显着增加。一种常用的预测方法是人工神经网络（ANN）。ANN 模型与优化算法相结合，为过程的预测和优化提供了强大而快速的工具，如果使用通用模拟技术进行模拟，则需要大量时间。在本文中，为了预测钻井过程中的渗透率，基于伊朗南部气井钻井获得的数据开发了几种人工神经网络模型。关于开发模型的 R 2 和 RMSE 值，选择最好的模型来预测渗透率。在下一步中，人工蜂群算法被用于优化对渗透率（ROP）有效的参数。结果表明，该模型具有足够的精度，可用于钻井作业机械钻速的预测和优化。