Oil and gas drilling machine drilling speed prediction and optimization method based on CART algorithm

Abstract

The invention relates to an oil and gas drilling machine drilling speed prediction and optimization method based on a CART algorithm. The method comprises the steps: 1, acquiring data; 2, carrying outdata preprocessing separately, taking the eight drilling parameters as different characteristic attributes and taking drilling data contained in the eight drilling parameters as input variables X, taking the mechanical drilling speed as an output variable Y, and obtaining an initial data set D1; 3, performing data correlation analysis to obtain training data sets D2 of different opening times; step 4, establishing a regression tree model between the input variable and the mechanical drilling speed in the training data set D2 with different opening times by utilizing a CART algorithm; 5, analyzing each piece of leaf node information of the generated binary tree, wherein the mean value of the leaf nodes is used as a predicted value of the mechanical drilling speed; 6, traversing the node division result of each layer from top to bottom to obtain different drilling parameter recommendation values; and step 7, obtaining optimal judgment of the mechanical drilling speed. The drilling period can be shortened, the drilling cost is reduced, and therefore the development efficiency of oil and gas resources is greatly improved.

Description

technical field [0001] The invention relates to a method for predicting and optimizing ROP in the field of petroleum exploration and development, in particular to a method for predicting and optimizing ROP based on a CART algorithm. Background technique [0002] In recent years, with the increase in the scale of oil and gas resource exploration and the strengthening of development efforts, the oil and gas exploration targets of various oilfields have gradually shifted from shallow formations to deep formations. The best way to develop. In the process of cobalt mining in deep and ultra-deep wells, due to complex geological conditions and harsh downhole conditions, engineering construction operations face great challenges. In order to better develop deep oil and gas resources, it is the general trend to shorten the drilling cycle, reduce drilling costs and improve drilling efficiency, and the most direct and effective way to solve these problems is to increase the ROP. There...

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Problems solved by technology

In recent years, with the rapid development of big data technology and the rapid growth of drilling data, there have been many cases where machine learning methods have been used to mine data and applied to the drilling industry. In drill bit optimization (Bi Xueliang, Yan Tie, Tao Lijie. Research on drill bit selection by neural network method in Qingshen Oilfield[J]. Journal of Harbin Engineering University, 2006, 27(z1):111-114), lithology identification (Shan Jingfu, Chen Xinxin, Zhao Zhongjun, etc. Using BP neural network The identification of complex lithology of tight sandstone gas reservoirs using the method[J]. Geophysics Advances, 2015(3):1257-1263) has been effectively applied in fields such as However, there are few studies and reports on the optimization of ROP

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