# Data Knowledge Dual-Driven Rate of Penetration Prediction Method for Horizontal Wells
## 1 Introduction  
Horizontal wells are one of the important ways for the development of unconventional oil and gas reservoirs. Horizontal wells face more complex downhole conditions, hole cleaning [1], drag and torque [2], complex bottom hole assembly (BHA) [3], trajectory control [4], etc., which makes the prediction and optimization of ROP in horizontal wells more complicated than in vertical wells [5]. However, most of the current ROP prediction studies are for vertical wells, which cannot be adapted to the complex drilling characteristics of horizontal wells. Therefore, it is important to analyze the drilling characteristics of horizontal wells, establish a horizontal well ROP prediction model, and clarify the potential relationship between drilling parameters and ROP for horizontal well ROP optimization and management.  
Currently, ROP prediction models are mainly divided into mechanistic and data-driven models. [PERSON] [6] and [PERSON] [7] proposed that the classical mechanistic ROP prediction model has been successfully applied in the field and widely accepted by the industry, but it is difficult to promote the application of the mechanistic model due to the high requirement of empirical parameters [8]. The data-driven approach can tap the implicit information in the data and is suitable for dealing with complex or unknown mechanism modeling problems, and the study shows that the data-driven ROP prediction approach obtains a better accuracy [9]. In recent years, data-driven ROP prediction models have been developing rapidly. For the problems of strong noise, discontinuity, and outliers in drilling data, wavelet noise reduction [10], kriging difference [11], outlier rejection [12] and other methods are proposed to pre-process the data. To address the complex mechanism in whichmany factors influence the ROP, the methods of correlation analysis [13] and principal component analysis [14] are introduced to preferentially selected decision variables to improve the training speed and performance of the model. To address the shortcomings of the machine learning algorithm, the bionic optimization algorithm is combined with the machine learning method to optimize the hyperparameters of the machine learning algorithm, which improves the accuracy of the model to a certain extent [13; 15; 16]. To address the problem in which the model cannot be dynamically updated, the moving-window support vector regression (MW-SVR) algorithm based on a moving window is proposed to dynamically update the ROP model to adapt to the changes in downhole conditions [17; 18]. For the special characteristics of horizontal wells and highly deviated wells, [PERSON] et al. [19] considered the influence of well trajectory on the ROP and developed an artificial neural network (ANN) model for predicting the ROP in highly deviated wells. [PERSON] et al. [20] evaluated the performance of 11 ROP prediction methods with directional section data. [PERSON] et al. [21] proposed a new method based on the apparent thickness of the formation to highlight the difference between horizontal well soundings and vertical depths for ROP management in horizontal wells.  
In the past, many scientists have noted that drilling data are noisy, missing and anomalous, but they ignored the validity of drilling data. Due to the high cost of downhole measurement devices, the data currently used for ROP modeling are surface measurements, mainly for vertical wells, and are not applicable to horizontal wells in the absence of downhole measurement tools. Downhole drilling data in horizontal wells are a function of surface drilling parameters and downhole drilling conditions [22]. This paper couples the drag and torque (D&T) model and the data-driven method to propose a data knowledge dual-driven horizontal well ROP prediction model for horizontal wells, which provides a basis for intelligent ROP optimization and management during the drilling process of horizontal wells.