Lower Congo Basin HauterA Block Reservoir Information Mining And Exploration Target Evaluation

In this paper, by analyzing rock geophysical response characteristics of Lower Cretaceous carbonate reservoir in he Lower Congo Basin Hauter A block, the scale and scope of favorable macro-facies is researched by means of structural analysis of seismic reflection, forward modeling analysis, Spectral Decomposition processing, seismic waveform classification, and a suite of technology workflow for deep carbonate reservoir prediction is established through Poststack and Prestack inversion, spectral absorption properties etc. for the region buried deep carbonate reservoirs, less wells , thin reservoirs and low seismic resolution on how to maximize the information of seismic data and improve the reliability of inversion has done the following innovations: (1) This will combine AVO attribute seismic processing and frequency extension technology, a suitable geological characteristics of the reservoir to improve the resolution method is established, it provides a reliable data basis for subsequent inversion of the data. (2) for less wells and quick lateral reservoir change, in order to reduce the inversion of multiple solutions, this research will combine sedimentary facies study and reservoir modeling and seismic inversion to integrate inversion through the use of sediment control studies. (3) through the use of improved Fatti equation, prestack inversion simultaneously obstain P-wave velocity, shear wave velocity and density of information, this method is considered in the inversion process of Vp, Vs and density relationship, it make the inversion results more accurate and Greatly enhance the ability to predict lithology and physical properties. (4) Because of less well and a different belt, it couple with the economic risks of deep drilling, this study based on the research in the reservoir, other structural and organic geochemical data develop a comprehensive evaluation for reducing drilling risk. On this basis, a comprehensive evaluation of reservoir distribution is done by using the probability method and the information fusion method.