The drilling rate of penetration (ROP) is defined as the speed of drilling through rock under the bit. ROP is affected by different interconnected factors, which makes it very difficult to infer the mutual effect of each individual parameter. A robust ROP is required to understand the complexity of the drilling process. Therefore, an artificial neural network (ANN) is used to predict ROP and capture the effect of the changes in the drilling parameters. Field data (4525 points) from three vertical onshore wells drilled in the same formation using the same conventional bottom hole assembly were used to train, test, and validate the ANN model. Data from Well A (1528 points) were utilized to train and test the model with a 70/30 data ratio. Data from Well B and Well C were used to test the model. An empirical equation was derived based on the weights and biases of the optimized ANN model and compared with four ROP models using the data set of Well C. The developed ANN model accurately predicted the ROP with a correlation coefficient (R) of 0.94 and an average absolute percentage error (AAPE) of 8.6%. The developed ANN model outperformed four existing models with the lowest AAPE and highest R value.

中文翻译：

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使用人工神经网络预测渗透率的新模型。

钻进速度（ROP）定义为钻头下方岩石的钻进速度。ROP受不同的相互联系的因素影响，这使得很难推断每个单独参数的相互影响。要了解钻孔过程的复杂性，需要鲁棒的ROP。因此，人工神经网络（ANN）可用于预测ROP并捕获钻井参数变化的影响。使用相同的常规井底钻具在相同地层中钻探的三个垂直陆上井的现场数据（4525点）用于训练，测试和验证ANN模型。利用A井的数据（1528点）以70/30的数据比率训练和测试模型。来自B井和C井的数据用于测试模型。根据优化后的ANN模型的权重和偏差导出经验方程，并使用C井数据集与四个ROP模型进行比较。开发的ANN模型以0.94的相关系数（R）和平均值准确预测ROP。绝对百分比误差（AAPE）为8.6％。已开发的ANN模型优于具有最低APE和最高R值的四个现有模型。