One of the important factors during drilling times is the rate of penetration (ROP), which is controlled based on different variables. Factors affecting different drillings are of paramount importance. In the current research, an attempt was made to better recognize drilling parameters and optimize them based on an optimization algorithm. For this purpose, 618 data sets, including RPM, flushing media, and compressive strength parameters, were measured and collected. After an initial investigation, the compressive strength feature of samples, which is an important parameter from the rocks, was used as a proper criterion for classification. Then using intelligent systems, three different levels of the rock strength and all data were modeled. The results showed that systems which were classified based on compressive strength showed a better performance for ROP assessment due to the proximity of features. Therefore, these three levels were used for classification. A new artificial bee colony algorithm was used to solve this problem. Optimizations were applied to the selected models under different optimization conditions, and optimal states were determined. As determining drilling machine parameters is important, these parameters were determined based on optimal conditions. The obtained results showed that this intelligent system can well improve drilling conditions and increase the ROP value for three strength levels of the rocks. This modeling system can be used in different drilling operations.

中文翻译：

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适当特征选择对使用智能技术预测和优化钻速的影响

钻井时间的重要因素之一是钻速 (ROP)，它根据不同的变量进行控制。影响不同钻井的因素至关重要。在目前的研究中，试图更好地识别钻井参数并基于优化算法对其进行优化。为此，测量并收集了 618 个数据集，包括 RPM、冲洗介质和抗压强度参数。经过初步调查，样品的抗压强度特征是岩石的一个重要参数，被用作适当的分类标准。然后使用智能系统，对岩石强度的三个不同级别和所有数据进行建模。结果表明，由于特征的邻近性，基于抗压强度分类的系统在 ROP 评估方面表现出更好的性能。因此，将这三个级别用于分类。一种新的人工蜂群算法被用来解决这个问题。在不同的优化条件下对所选模型进行优化，并确定最佳状态。由于确定钻机参数很重要，因此这些参数是根据最佳条件确定的。结果表明，该智能系统可以很好地改善钻井条件，提高岩石三个强度等级的机械钻速值。该建模系统可用于不同的钻井作业。这三个级别用于分类。一种新的人工蜂群算法被用来解决这个问题。在不同的优化条件下对所选模型进行优化，并确定最佳状态。由于确定钻机参数很重要，因此这些参数是根据最佳条件确定的。结果表明，该智能系统可以很好地改善钻井条件，提高岩石三个强度等级的机械钻速值。该建模系统可用于不同的钻井作业。这三个级别用于分类。一种新的人工蜂群算法被用来解决这个问题。在不同的优化条件下对所选模型进行优化，并确定最佳状态。由于确定钻机参数很重要，因此这些参数是根据最佳条件确定的。结果表明，该智能系统可以很好地改善钻井条件，提高岩石三个强度等级的机械钻速值。该建模系统可用于不同的钻井作业。由于确定钻机参数很重要，因此这些参数是根据最佳条件确定的。结果表明，该智能系统可以很好地改善钻井条件，提高岩石三个强度等级的机械钻速值。该建模系统可用于不同的钻井作业。由于确定钻机参数很重要，因此这些参数是根据最佳条件确定的。获得的结果表明，该智能系统可以很好地改善钻井条件，提高岩石三个强度等级的 ROP 值。该建模系统可用于不同的钻井作业。