Oil well yield prediction method based on deep learning algorithm

Abstract

The invention provides an oil well yield prediction method based on a deep learning algorithm. The oil well yield prediction method based on the deep learning algorithm comprises the following steps:the step 1, acquiring data and performing quality inspection; the step 2, performing data processing and division; the step 3, establishing a learning model; the step 4, carrying out training by adopting the model built in the step 3, and carrying out verification; and the step 5, predicting the oil well yield. According to the oil well yield prediction method based on the deep learning algorithm,the relations between reservoir physical properties, working systems, development stages and other factors and the oil yield and the liquid yield are established through training, the advantages of adata driving algorithm are exerted, and a multi-factor oil well yield prediction model is established.

Description

technical field [0001] The invention relates to the technical field of oilfield development, in particular to a method for predicting oil well production based on a deep learning algorithm. Background technique [0002] Oil well and oilfield production prediction is one of the most important tasks in oilfield production management, and the prediction results directly determine the subsequent oilfield 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 over time. At present, commonly used methods in mine field include: reservoir engineering method and numerical simulation method. The reservoir engineering method represented by the Arps decline curve is a direct fitting of the production decline phenomenon of oil wells. It is easy to operate and is not limited by the type of reservoir. The defect of this method is very ob...

AI Technical Summary

Problems solved by technology

The reservoir engineering method represented by the Arps decline curve is a direct fitting of the production decline phenomenon of oil wells. It is easy to operate and is not limited by the type of reservoir. The defect of this method is very obvious. The prediction must assume that the historical production conditions will remain unchanged in the future change, cannot be used to analyze data in unsteady flow state

Although the subsequent improved methods make up for the differences in reservoir types and flow stages to varying degrees, they are always limited to the basic process of typical mathematical models-field data fitting, and the assumptions of typical theoretical models are the limitations of this method.

Reservoir numerical simulation is based on the understanding of the real flow process of underground porous media. It is a typical physical-driven data analysis method, which can consider more factors in more detail, and the prediction results are more objective than reservoir engineering. However, the modeling digital simulation process is very time-consuming , especially when the geological conditions are complex or the seepage mechanism is not clear, the prediction results still need to be improved

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