Logging reservoir identification and prediction method based on machine learning

Abstract

The invention provides a logging reservoir identification and prediction method based on machine learning. The invention aims at solving the problems that when a conventional well logging interpretation method is used for tight gas low-porosity and low-permeability reservoir fluid recognition, due to the fact that the reservoir is high in heterogeneity and the pore structure is complex, a well logging interpretation model is poor in applicability and low in accuracy, and a traditional single machine learning algorithm is prone to falling into the problems of overfitting, local optimum and the like. According to the invention, the classic ensemble learning algorithm xgboost is adopted, the gas testing logging result and the conventional logging data are combined, the tight sandstone reservoir is recognized, the prediction precision can be effectively improved, the time for testing personnel to judge the reservoir is shortened, and therefore a large amount of cost is saved.

Description

technical field [0001] The invention belongs to the field of machine learning and relates to a method for identifying and predicting well logging reservoirs based on machine learning. Background technique [0002] Integrated learning is a branch direction of machine learning. It mainly forms a strong learner by combining multiple weak learners, and adopts an appropriate combination strategy to synthesize the prediction results of each learner to obtain a more comprehensive and reliable judgment, thereby improving The accuracy of the model's predictions. Compared with a single machine learning algorithm, it is not easy to fall into problems such as overfitting and local optimum. Among them, xgboost is a more classic algorithm for integrated learning. xgboost is based on the idea of ​​GBDT, using first-order and second-order derivative information. It can also be said that it is a kind of GBDT because it is also based on Gradient and Boosting ideas, but The difference from t...

AI Technical Summary

Problems solved by technology

Compared with the traditional well logging interpretation method, when identifying tight gas, low porosity and low permeability reservoir fluids, due to the strong heterogeneity and complex pore structure of the reservoir, the well logging interpretation model has poor applicability and low accuracy.

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