Oil well production prediction method based on deep learning algorithm

Abstract

The invention provides a method for predicting oil well production based on a deep learning algorithm. The method for predicting oil well production based on a deep learning algorithm includes: step 1, acquiring data and performing quality inspection; step 2, performing data processing and division; step 3, establishing Learning model; step 4, use the model built in step 3 to carry out training and verification; step 5, predict oil well production. The oil well production prediction method based on deep learning algorithm establishes the relationship between reservoir physical properties, working system, development stage and other factors and oil production and liquid production through training, and takes advantage of the data-driven algorithm to establish multi-factor oil well production prediction Model.

Description

technical field

[0001] The invention relates to the technical field of oilfield development, in particular to an oil well production prediction method based on a deep learning algorithm. Background technique

[0002] Oil well and oil field production prediction is one of the most important tasks in oil field production management, and the prediction results directly determine subsequent oil field development decisions. However, limited by geological conditions, technological level, development history, data quality and other conditions, it is very difficult to predict the change of oil well production with time. At present, the commonly used methods in mines include: reservoir engineering method and numerical simulation method. The reservoir engineering method represented by Arps decline curve is a direct fitting to the phenomenon of oil well production decline. It is simple to operate and is not limited by the type of oil and gas reservoir. The defects of this method are v...

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