The invention discloses a drilling speed prediction method based on a BP (Back Propagation) neural network, which is used for acquiring torque, drilling pressure,
pump pressure and displacement of a drilling tool and predicting the drilling speed based on the BP neural network, and specifically comprises the following steps of: 1, measuring parameters of the torque Nm, the drilling pressure Pm, the
pump pressure Pb and the displacement Qm of the drilling tool through a sensor according to a sampling period; 2, normalizing the parameters in sequence, and determining an input layer vector x = {x1, x2, x3, x4} of the three-layer BP neural network; wherein x1 is the
torque coefficient of the drilling tool, x2 is the bit pressure coefficient, x3 is the
pump pressure coefficient, and x4 is the displacement coefficient; 3, mapping the input layer vector to an intermediate layer; 4, obtaining an output layer vector o = {o1}; wherein o1 is a drilling speed prediction coefficient; and 5, predicting the drilling speed of the drilling tool, namely outputting a drilling speed prediction coefficient of the layer vector in the ith sampling period,
omega m _ max being the maximum drilling speed ofthe drilling tool, and
omega m (i + 1) being the predicted drilling speed of the drilling tool in the (i + 1) th sampling period. The invention discloses a drilling speed optimization method based ona BP neural network and a
particle swarm algorithm.