A supervised machine learning method based on well test data to predict reservoir production in buried-hill metamorphic rock
The vertical and radial heterogeneity of multi-zone dispersed reservoirs is strong,which brings difficulties to reservoir evaluation.Well test dynamic characteristics machine learning technology,improve the evaluation level.This article analyzes the double logarithmic curves of 10 wells/layers in the complex reservoir of Bohai A structure's ancient,buried hill,and forms the characteristic curve of this complex reservoir.At the same time,through the fusion data analysis technology,the typical feature template of exploration and well testing in the complex reservoir of Bohai A structure's ancient,buried hill is formed by analyzing the well testing parameters of the complex reservoir.The study suggests that the ancient,buried hill test wells in Bohai A structure belong to the same reservoir,and it also proves that the continuity of the reservoir is good.At the same time,this article successfully achieved node-based production capacity prediction through regional well testing results and provided a production capacity prediction formula.The reverse validation of the prediction model achieved a compliance rate of 75%.Through this study,a dynamic interpretation template for the ancient buried hill test of Bohai A structure was provided,which achieved the identification of the dynamic change trend of the oil reservoir.The regional well testing method studied in this article is derived from traditional regional well testing techniques and effectively combines data analysis techniques,which can broaden the application scenarios of well testing.
exploration and developmentEngineering TechnologyMetamorphic rock buried hillproductivity forecastmachine learningdata usemodelQuantitative evaluation of productivity
万方数据
维普
