A novel dynamic model for the online prediction of rate of penetration and its industrial application to a drilling process

Real-time prediction of rate of penetration by combining attention-based gated recurrent unit network and fully connected neural networks

A novel modeling and drilling optimization method with suitable constraints in geological well

A novel performance assessment method of the carbon efficiency for iron ore sintering process

The rate of penetration (ROP) is an index used to measure drilling efficiency. However, it is restricted by many factors, and there is a coupling relationship among them. In this study, the random forest algorithm is used to sort influencing factors in order of feature importance. In this way, less influential factors can be removed. A fuzzy neural network (FNN) is applied to the field of drilling engineering for the first time, aiming at the coupling problem to predict the ROP. Fuzzification is an important part of training and realizing FNN, but research on this topic is currently lacking. In this study, K-means are used to divide the data with high similarity into a fuzzy set, which is used as the initialization parameter for the second layer of the FNN. The data of Shunbei No. 1 and 5 fault zones in Xinjiang are collected and trained. The results show that the mean value of the coefficient of determination R2 is 0.9668 under 10 experiments, which is higher than those obtained from a back propagation neural network and multilayer perceptron particle swarm optimization methods. Therefore, the effectiveness and feasibility of the model are verified. The proposed model can improve drilling efficiency and save drilling costs.

Haibo Liu

Papers

A novel rate of penetration prediction model with identified condition for the complex geological drilling process