Heavy Oil Petrophysical Modeling And Reservoir Prediction

With the acceleration of global industrialization,after decades of exploration and development of oil and gas resources,the exploration and development of conventional oil and gas resources have reached a bottleneck,so now the world is vigorously exploring and developing unconventional oil and gas resources.Heavy oil is a representative unconventional oil and gas resource,and there are abundant reserves in many oil fields in China,so it is necessary to carry out research on heavy oil reservoirs.In this thesis,the viscoelastic properties of heavy oil are firstly studied.Heavy oil is different from other conventional fluids,it has the characteristics of high density and high viscosity,and its shear modulus is not negligible,and the CCM model is used to simulate the variation of shear modulus of heavy oil with temperature and frequency,and then the petrophysical modeling of heavy oil is carried out,because the shear modulus of heavy oil is not zero,the conventional Gassmann equation cannot be used for heavy oil fluid replacement.In this thesis,the coherent potential approximation method(CPA)is used for heavy oil fluid replacement.and the variation of elastic modulus of heavy oil-bearing rocks with temperature and frequency is finally obtained.The established heavy oil rock physical model is applied to the actual work area,and the S-wave prediction is carried out for all wells in the actual work area,then the pre-stack inversion is carried out for the actual work area using HRS software to obtain the inverse data.The favorable heavy oil reservoir can be predicted by combining the intersection analysis results of logging data,logging interpretation results and pre-stack inversion results.A BP neural network is established for the actual work area,the input data are P-wave velocity,S-wave velocity and density,the output data are porosity,the training data are the actual logging data and the data generated by the petrophysical model of heavy oil,the results of the pre-stack inversion of the actual workings are input into the trained BP neural network,and the porosity prediction results of the actual workings are output.Based on the prediction results of favorable heavy oil reservoir and porosity prediction results of actual work area,it can provide guidance for the subsequent exploration and development of this work area.