Study On Methods Of Reservoir Evaluation And Remaining Oil Prediction Based On Static And Dynamic Logging In Water Flooded Interval Injected By Fresh Water And Sewage

During the exploitation process of an oil field, it is of great importance to make an accurate estimate of the saturation and distribution of remaining oil, so as to further enhance the secondary, or even the third oil recovery. Therefor, dynamic and static logging evaluation methods and remaining oil prediction methods were discussed in this dissertation, which provides some new research ideas for accurate positioning of remaining oil in water flooded interval injected by fresh water and sewage.The major research and results of the dissertation are as follows.(1) First of all, under two conditions assumed, the change law of the mixed formation water salinity was researched under the conditions of variable injection water salinity. Moreover, the parameters in the saturation assessment model during waterflooding were confirmed based on rock electricity mechanism experiments data. Integrating the core experiment data on the water-flooding layer in Gasikule oil field N1-N21reservior, the water-flooding development has no great influence on porosity index m. However, the saturation index n in the various stages of water-flooding development shows stage-based changes, with the change of injected water salinity, the change characteristics of n are all different. Finally, a mixed formation water dynamic analysis model was hereby established integrating the simulative researches and experimental data, such model could carry out more careful considerations on the ion exchange phenomenon between injected water and original formation water, so as to set up foundations for the elaborative calculation of remaining oil and water saturation.(2) When a programmable calculation was made on the remaining oil saturation in the water-flooded layer by a large amount of open-hole log information, in order to resolve such two difficulties as uncertainty of the mixed formation water resistivity and incapably-effective consideration of parameter changes in saturation calculation models with the increase of formation water saturation, the following research was done in this dissertation.First of all, the flooding characteristics of the study area was analyzed, and then relative models were established, including porosity,permeability,original oil saturation, original salinity of formation water, mixed formation water resistivity and so on, so as to establish a rational matrix between water saturation and injected water salinity by the Ariche formula of variable element based on the change characteristics of the mixed formation water salinity in the water-drive reservoir. Under the conditions of no complete injected water salinity data, the injected water salinity was confirmed by the original oil saturation so as to finally determine the mixed formation water salinity and fluid saturation at each sampling point of the interpretative layer. This method can evade the defects of traditional methods and batches of applications can have favorable effects after insertion of the interpretative procedures, which open up new thoughts for the quantitative evaluation on the water-flooded layer. This method can also be applied to the through casing saturation logging evaluation.(3) The distribution of remaining oil and the changes of reservoir properties can be dynamically reflected by PNN logging. However, the accuracy is affected by many factors.In different regions,and even in different wells in the same area,the parameters of models are not identical. In time dimension, in order to obtain more accurate parameters in horizontal and vertical directions, a method has been discussed in this dissertation. Firstly, the relatively sealed layers which are not affected by exploitation and waterflood development were selected as marker bed. The vertical distance between marker bed and major reservoirs should be within a certain range. Then necessarily improved adaptive genetic algorithm with evolution range had been properly compiled into the interpretation program, so that the second correction of log data could be considered and the most suitable parameters could be determined for each well.The practical application shows that this method accurately reflects the heterogeneity of the measured environments. The interpretation process effectively compensates for the errors caused by difference between wells and layers,and the evaluation results are more consistent with the practical production performance.(4) The salinity of mixed water in water flooded zones injected by fresh water and sewage is variable. Therefore, as an important parameter in remaining oil saturation calculation model in cased hole, the macroscopic capture cross section of mixed formation water is difficult to determine. The distribution regulation of salinity and solutes of mixed formation water in the water flooded layer in study area shows that it is very necessary to calculate the macroscopic capture cross section of mixed formation water layer by layer.Pulsed neutron-neutron well logging evaluation in water-flooded zone was taken as an example in this dissertation. First of all, original oil saturation model was established. And then the macroscopic capture cross section of formation fluid was obtained. Finally, macroscopic capture cross section of mixed formation water was calculated by static and dynamic logging information. In view of the water flooding degree of some sampling points is inhomogeneous in the same layer, the method can also calculate the corresponding macroscopic capture cross section of mixed formation water.The practical application shows that this method accurately reflects the variability of the salinity of formation water. The calculation accuracy has been improved because the difference between layers has been taken into consideration.This method also provides a new idea for the quantitative assessment of water flooded zones injected by fresh water and sewage by using some comprehensive methods which take dynamic and static logging data into consideration.(5) Several defects of the calculation of remaining oil saturation based on single factor were pointed out and remaining oil prediction method based on multi-factor were discussed in this dissertation. It is very necessary to consider the weight of each flooding strength indicator in calculation of multifactorial flooding index. Therefore, a fuzzy neural network prediction system of multifactorial flooding index based on ellipse basis function was established on the basis of the analysis of a variety of static and dynamic data of Gasikule oil field N1-N21reservior. This prediction system can create or delete fuzzy rules by analyzing samples and take the dynamic weight values of the input variables into consideration. The information contained in the log data is enormous. By using this prediction system with self-learning mechanism, the extraction and utilization of information is more effective. Practical application shows that the accuracy of identification is high. Especially for complex reservoirs, the application of this Fuzzy Neural Networks on reservoir characteristic parameters prediction improves the precision of prediction results and reduces the dependency on prior informations.