  With the continuous development of the petroleum industry, long-distance
transportation of oil and gas has been the norm. Due to gravity differentiation
in horizontal wells and highly deviated wells (non-vertical wells), the water
phase at the bottom of the pipeline will cause scaling and corrosion in the
pipeline. Scaling and corrosion will make the transportation process difficult,
and transportation costs will be considerably increased. Therefore, the study
of the oil-water two-phase flow pattern is of great importance to oil
production. In this paper, a fuzzy inference system is used to predict the flow
pattern of the fluid, get the prediction result, and compares it with the
prediction result of the BP neural network. From the comparison of the results,
we found that the prediction results of the fuzzy inference system are more
accurate and reliable than the prediction results of the BP neural network. At
the same time, it can realize real-time monitoring and has less error control.
Experimental results demonstrate that in the entire production logging process
of non-vertical wells, the use of a fuzzy inference system to predict fluid
flow patterns can greatly save production costs while ensuring the safe
operation of production equipment.
