Study Of Logging Interpretation In Glutenite Reservoir

With the development of oil prospecting, it is very important to find new reservoir. So we pay more attention on conceal reservoir and glutenite reservoir is one type of good conceal oil-gas reservoir. It is difficult to evaluate glutenite reservoir, which has strong anisotropy and complex lithology and low porosity and permeability. It’s hard to identify effective reservoir and oil-water layer. The reservoir evaluation with logging data, the discrimination of oil-water and the application of geology are all difficult problems to the oil prospecting field at home and abroad, so it requires new theories and advanced technologies to support.Because of the complexity of the glutenite reservoir, it is difficult to make sure the reservoir parameter and the reservoir fluid nature by using the conventional logging interpreting method, leading to the unmeet of the result between logging interpretation result and the testing, oil testing and test mining result.The Neocene Shawan formation of Pai2well area belongs to delta foreset sandstone lithologic hydrocarbon reservoir. In this paper, starting with the geologic reservoir features and the characteristic of well logging data, based on the geology of Pai2well area, we analyze the "four characteristics" relationship, and then set the interpreting model of the study area glutenite reservoir physical parameters to identify the reservoir fluid by using different method. In the paper, the consistency of shale content calculated by natural gamma and with it analyzed by the laboratory granularity is very well. Porosity that calculated by using the dual minerals volume model method is well consistent with core analysis porosity, correlation between porosity and core analysis porosity is high, and the porosity calculated by the NMR logging data is meet with the core analysis porosity. In the case of the presence of the secondary porosity, single porosity and permeability relationship is poor. And in this paper the correlation of permeability model set with the granularity analysis data is more than0.9, it basically meet with the glutenite reservoir permeability interpreting requires. Draw crossplot of the porosity calculated by the dual mineral volume model and resistivity, set identification plates based on oil-water layer of different lithology; besides, when use NMR logging data to identify fluid features, distribution pattern of T2spectrum is a great criterion. Through this study, we obtained the following results and knowledge:1. Set the model to calculate the glutenite porosity, regard the glutenite as the mixture of sandstone skeleton, conglomerate skeleton, shale and pore. Based on the density, acoustic wave, neutron log response equation to set dual-mineral volume model, programming to calculate the volume content of each part, compare the calculated porosity result with the core analysis porosity, the anastomosis of the result is well and the average absolute error is1.33%; Compare the average absolute error between the porosity calculated by the single porosity density curve and core porosity(4.42%), the accuracy is significantly increased.2. In the condition of single porosity and permeability, introduced the relationship between median grain size and the permeability, and calculate the permeability by using median grain size and shale content or the multiple regression formula of between porosity and permeability to calculate the permeability, the correlation of set permeability model is more than0.9, while absolute error is5.42×10-3μm2.3. Using the rock power experimental data, different rock calculate the water saturation using different rock power parameter, the error between the obtained saturation model and the rock analysis correction saturation is1.4%, according with the standard for reserves.4. On the basis of the promotion of the reservoir logging data calculation accuracy, we use porosity calculated by the dual mineral volume model and the resistivity to draw the identification plate of water oil layer based on different lithology. Set the study area reservoir water-oil identification standard, promote the coincidence rate from65%to85%, and it can basically solve the difficulty of judging the water-oil layer.5. NMR logging data is not only has a average absolute error of0.56%between calculated porosity and core conventional interpreting porosity, it also incomparable in identify fluid that conventional logging can hardly compare to it. We should promote the place of NMR logging data in the glutenite reservoir logging interpretation, to find a breakthrough point for the use of conventional logging data to evaluate glutenite.