Back propagation neural network model for controlling artificial rocks petrophysical properties during manufacturing process

Abstract

Artificial rocks are increasingly used in experiments, and it is important to make artificial rocks’ petrophysical properties similar to real rocks to obtain more valuable results. The effect of widely used five factors, grain size (GS), grain size distribution (GSD), mass fraction of cementing agent (MC), pressing pressure (PP), and pressing time (PT), on artificial rock petrophysical properties are analyzed. Three factors, GS, MC, and PP, which are suitable for establishing a quantitative model are opted. The relationships of 80 rock plugs’ petrophysical properties and manufacturing factors are studied, which indicates MC and PP are negatively correlated with porosity and permeability, and GS has a significant effect on permeability but little effect on porosity. Furthermore, a novel back propagation (BP) neural network model is proposed, which can be used to determine factor values during artificial rock manufacturing process. A series of artificial core plugs are manufactured relying on manufacturing factor values calculated by the BP neural network model, and their petrophysical properties are tested and compared with design petrophysical properties value to verify the model. Verification results show that comprehensive average error of porosity and permeability of artificial rock, including calculating error and manufacturing error, is 2.38 and 18.68%, respectively.