The Well Logging Evaluation Study Of Shenquan Low-resistivity Reservoir

With the further exploration of oil and gas field, low resistivity reservoir has become a major focus of current exploration. The causes of low resistivity reservoir are different, it brings a huge difficult to the well logging evaluation because of the small differences between oil and water. Based on the study of Shenquan Sanjianfang low resistivity reservoir, a system of effective logging evaluation. in this area is formed in order to provide support for the production forecast.In this paper, we firstly study the general geology situation of Shenquan area and make sure of structure, stratum, sedimentation and reservoir characteristics. We get an overall comprehend of the formation characteristics after we study it. Based on the above research we study and analyze four-property relationship of low resistivity reservoir. The petro physical lower limit values for the effective reservoir are determind by the use of ptero physical oil testing method, the displacement pressure inflection point method, porosity-permeability crossplot method and empirical statistical method. The main courses of the low resistivity are the small particle size, the high salinity of formation water, the complex pore structure and the conductive minerals. Based on the corrected core experimental data several quantitative models are established, including the shale content, the median grain diameter, porosity, permeability and the saturation model. These models provide theoretical basis for logging identification of fluid.Several quantitative interpretation models are established, a variety of mothods are used to evaluate saturation of low resistivity reservoir. The actual application shows that the W-S and dual porosity saturation model have a good result. in this area. The logging response characteristics of different fluid types in the low resistivity are analysed, array induction logging, curve shape method, dual porosity overlap method and crossplot method are used to identify reservoir fluid properties comprehensively.At last, based on the experimental data of capillary pressure we put forward a method to identify different types of reservoirs by the use of clustering analysis. Finally, we realize classifying the reservoirs by using logging data and we build the corresponding relation between reservoir types and reservoir productions at the same time. In addition, PNN probabilistic neural network is also applied to distinguish the reservoirs production. Finally sensitive parameters of well logging and geological relating to production forecast are selected, we established the quantitative calculation models of production layers by this parameters. Using the achievements above, we take forecast for more than fifty wells about eighty five small layers of low resistivity reservoirs in western Tuha oil field. The producton forecast has a high coincidence rate of eighty five percent. So the research results in this paper have a good geological application results of low resistivity reservoir in western Tuha oil field.