Abstract: With the increase of drilling operating depth, formation conditions and well structure become complicated, resulting in increasement in drilling investment. In order to improve the drilling efficiency and reduce the drilling cost, this paper used the logging data obtained in the drilling process, combined with the neural network and GA, and finds the optimal ROP and its corresponding drilling parameters, and ensured the efficient drilling operations. Collect 8 000 sets of data for 2 stratigraphic sections of different wells in a block in the Bohai Area (4 000 sets per layer), and train the machine learning model separately for each stratum. Taking one of the layers as an example, 3 900 sets of drilling parameters are taken as inputs, and the corresponding 3 900 sets of ROP are used as output to train the BP neural network; then the remaining 100 sets of drilling parameters are taken as input data. The obtained neural network is used to predict 100 sets of ROP. Finally, 4 000 sets of drilling parameters are used as the population individuals in the genetic algorithm, and the predicted ROP is used as one important parameter in the GA-the individual fitness value, the optimal ROP and its corresponding drilling parameters are derived by GA. The method makes full use of the data of the oilfield site, and obtains machine learning models suitable for different strata in the Bohai Area, which improves the ROP and realizes the drilling speed.

Key words: drilling speed, drilling parameter optimization, neural network, genetic algorithm, data-driven