A Research Of The Application Of Reservoir Prediction Method Based On Logging Data And Seismic Data

Compared with other exploration methods,seismic exploration methods have been widely used in oil and gas exploration because of their high accuracy and high return.Seismic exploration methods have produced a variety of reservoir prediction techniques,such as wave impedance inversion and seismic attribute analysis methods,which are commonly used in reservoir prediction.Wave impedance inversion method is simple,effective and widely used in qualitative analysis of reservoirs.However,this method is often limited by the quality of seismic data,and it is difficult to show the detailed characteristics of reservoirs.Andit`s necessary to directly analyze and evaluate the well logging data to obtain the lithological and physical characteristics of the target stratum.There are abundant kinds of seismic attributes,which provides varies of angles to observe and analyze the seismic data.Seismic attribute analysis method can analyze the correlation between reservoir parameters and seismic wave attributes after the correlation between some seismic attributes and the standard parameters of effective reservoirs for coring data analysis is learned by neural network algorithm,the items can be analyzed through these relationships.Quantitative prediction of lithological and physical parameters is made by seismic attributes data of standard beds.Especially when the target reservoir is widely distributed and the effective reservoir is discrete,it is more advantageous to use neural network algorithm to analyze seismic attributes and find effective reservoirs.Su10 well field,the practical object we choose,is located in Sulige gas field,which is exactly a large-scale and inefficient gas field with discontinuous effective storage.Firstly,we established the standard model of log interpretation by logging curve and core sample data,which means we searched the range of lithologic and physical parameterof the effective sand bodies and carried outthe core depth location andhorizon calibration.Then,using the standard horizon model of log interpretation as constraints,wave impedance inversion is carried out to find the favorable distribution range of sand body.And the weight relationship between the standard reservoir parameters and these attributes is studied by using the autonomous neural network algorithm from seismic attributes data,which will help us to calculate the lithologic and physical parameters of the target reservoir.Finally,the qualitative analysis results of reservoir obtained by wave impedance inversion and the definite analysis results obtained by seismic attributeanalysis are synthetically analyzed to provide reference opinions for further reservoir prediction.