Predicting the rate of penetration (ROP) plays a key role in the success of the drilling operation. It is not an easy task to predict the ROP with high accuracy as it depends on several factors such as; drilling parameters, drilling fluid properties, and drilled formation characteristics. The objective of this paper is to develop a new empirical equation for predicting the ROP in real-time using different artificial intelligence (AI) techniques such as artificial neural networks (ANN), adaptive neuro-fuzzy inference system (ANFIS), and support vector machine (SVM). For the first time, poly diamond crystalline (PDC) bit design parameters, total flow area, in addition to mud density (MWin), gamma ray (GR), and drilling parameters were used to build the AI models. Actual field data was used to build the AI models (1000 data points from Well A) and another 972 data points from Well B were used for validating the developed AI models.

The obtained results confirmed that the three AI techniques could be used to predict the ROP for complex lithologies with high accuracy. The ANN outperformed the SVM and ANFIS for predicting the ROP for the unseen data (972 data points of validation). The developed ROP-ANN model could be used to predict the ROP with high accuracy (the root mean square error (RMSE) was less than 0.659 for the available two wells). The developed empirical correlation was able to predict the ROP with high accuracy, RMSE was 0.66. The new ROP equation can be used without the need for the ANN Matlab code or special software.

预测钻速（ROP）在钻探作业成功与否中起着关键作用。要高精度地预测ROP并非易事，因为它取决于多个因素，例如：钻井参数，钻井液特性和钻井地层特征。本文的目的是开发一个新的经验方程，使用不同的人工智能（AI）技术（例如人工神经网络（ANN），自适应神经模糊推理系统（ANFIS）和支持向量）实时预测ROP。机器（SVM）。首次使用聚金刚石晶体（PDC）钻头设计参数，总流量面积，泥浆密度（MWin），伽马射线（GR）和钻孔参数来构建AI模型。

获得的结果证实了三种AI技术可用于以高精度预测复杂岩性的ROP。在预测未见数据的ROP方面，ANN的性能优于SVM和ANFIS（验证的972个数据点）。所开发的ROP-ANN模型可用于高精度预测ROP（可用的两口井的均方根误差（RMSE）小于0.659）。发达的经验相关性能够高精度地预测ROP，RMSE为0.66。无需ANN Matlab代码或专用软件即可使用新的ROP方程。