Fuzzy Neural Network for Studying Coupling between Drilling Parameters.

Area of Science:

• Drilling engineering
• Artificial intelligence
• Machine learning

Background:

• Rate of penetration (ROP) is crucial for drilling efficiency but is influenced by complex, coupled factors.
• Existing methods struggle to effectively model the interdependencies between these influencing factors.
• Lack of research exists on fuzzification techniques for fuzzy neural networks (FNNs) in drilling applications.

Purpose of the Study:

• To develop and validate a novel fuzzy neural network (FNN) model for predicting drilling rate of penetration (ROP).
• To address the challenge of coupled influencing factors in drilling engineering.
• To introduce an effective fuzzification method using K-means clustering for FNN initialization.

Main Methods:

• Feature importance analysis using the random forest algorithm to identify and potentially remove less influential factors.
• Application of a fuzzy neural network (FNN) for ROP prediction, incorporating a novel fuzzification approach.
• Utilizing K-means clustering to initialize the FNN's fuzzy sets, enhancing model training.

Main Results:

• The proposed FNN model achieved a mean coefficient of determination (R²) of 0.9668 across 10 experiments.
• Performance of the FNN model surpassed traditional methods like backpropagation neural networks and multilayer perceptron particle swarm optimization.
• Validation of the model's effectiveness and feasibility using real-world drilling data from Xinjiang fault zones.

Conclusions:

• The developed fuzzy neural network (FNN) model demonstrates high accuracy and feasibility for predicting drilling rate of penetration (ROP).
• The novel fuzzification technique using K-means clustering effectively initializes the FNN, addressing factor coupling.
• The model offers a promising solution for enhancing drilling efficiency and reducing operational costs in the oil and gas industry.