Logging Identification And Productivity Prediction Of Gas Reservoir In Daliuquan Block

Daliuquan block is mainly developed high porosity-high permeability sandstone and mudstone reservoir,and the resistivity response of oil,gas,and water layer are complex,the evaluation accuracy of parameters calculation,fluid type identification and productivity prediction are low.Aimed at gas reservoir evaluation of the study block,undertaking the research of key technologies to reservoir parameter modeling,gas reservoir recognition and productivity prediction which based on petrophysics experiments and comprehensive utilization of well logging,logging and test data.Based on petrophysics experiments,the four-property characteristics of reservoir and their relations are analyzing,clear the main controlling factors of reservoir.Porosity models using multiple regression method and optimization method based on multi-mineral model are established.The flow unit method is adopted to build permeability model,and we obtain a suitable Coates and SDR permeability calculation formula for the study block using nuclear magnetic resonance(NMR)data,at the same time,the permeability calculation method based on granularity is explored.According to the rock electricity experiment to determine the applicable electrical parameters,water saturation is obtained by using the Archie formula,in addition,using NMR experimental data to established irreducible water saturation model.Analyzed the conventional logging response of reservoir fluid characteristics in the study block,based on the idea of gradual stripping,reservoir with high gas saturation is identified through the three porosity ratio and new neutron gamma plate.In view of reservoir with low gas saturation,make full use of the array acoustic logging data to construct a cross-plot that the ratio of shear slowness to compressional slowness and compressional wave slowness,and fluid factor identification method based on Biot-Gassmann theory is used to identify the gas.When lacking of array acoustic data,multi-variate regression method and the equivalent medium model method are applied to forecast the shear wave velocity.Depended on the difference of diffusion coefficient of oil,gas,and water,we attempted to identify gas by building nuclear magnetic water spectrum method.Using the comprehensive index method,BP neural network technique,and based on the array acoustic logging data to extract sensitive factors of capacity in gas productivity prediction,has a good correspondence with the testing result.