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Method Research On Reservoir Productivity Prediction In The Formation With Low Porosity And Low Permeability

Posted on:2014-04-21Degree:MasterType:Thesis
Country:ChinaCandidate:J C WuFull Text:PDF
GTID:2250330401480761Subject:Earth Exploration and Information Technology
Abstract/Summary:PDF Full Text Request
Productivity reflects how much hydrocarbon a well contains,it is a composite indicator of the dynamic characteristics to oil and gas reservoirs, it is dynamic equilibrium in the process of mutual constraints between oil and gas reservoir production potential and a variety of influencing factors. The so-called productivity analysis, is combined with the reservoir and production data of oil and gas wells, analyze the influencing factors of output in producing wells,and give future production of a more appropriate predication, to provide basic reference data for oil and gas field development programs. The reservoir parameters we acquire by well logging data don’t reflect the dynamic characteristic, but main represent the static characteristic. The intention of reservoir productivity prediction by well logging data is to get the dynamic parameters from static parameters.In the area of reservoir engineering,there have already been multiple methods of production productivity of oil and gas reservoir evaluation and prediction, but they mainy use the oil and gas wells system test data to calculate, such as the formation pressure, the bottom of the well flow stress and the test production etc.,which just consider the production productivity of one well or one productive reservoir,the methods of using logging information to evaluate and forecast reservoir productivity are still deficient.Low porosity and permeability sandstone reservoir and heavy clay content is typical feature of study area. In study area the fault develops well,have more reservoir type and complex distribution of oil-water, there is no uniform oil-water interface. The study is based on studying special geological conditions in the region, by means of using logging data, core laboratory analysis and so on, and combined with the existing logging evaluation techniques to evaluate reservoir in order to acquire the affected factors of reservoir productivity. And then, Bayes discriminant method of pattern recognition be used to discriminate qualitative the level of production, then the productivity of reservoir be predicted quantitative on the basis of discriminate qualitative. Specific study as follows:1、Due to porosity, permeability, clay content and saturation of the reservoir parameters, such as logging data are the key factors of reservoir production predict.Therefore, for the reservoir characteristics of the study area, I used the method of combining core scale logging and volume model to have a comprehensive study on the reservoir parameters, and establish the calculation model of the shale content, porosity, permeability and saturation.2、Reservoir productivity is controled by the reservoir conditions> the external environment and its own oil and gas properties, as for a specific area, the external environmental conditions and oil and gas properties,are all relatively constant. At this point, the level of reservoir productivity depends on the nature of oil and gas reservoir itself. As the static pressure and flow pressure of the study area is difficult to accurately obtain, so this study consideres the oil obtained by dividing the total thickness of the oil and gas production as oil and gas production per meter, in order to characterize the size of natural productivity,which are used to analyze the main factors about affecting productivity. Logging, geological and oil testing data are used to study the regular distribution of oil,gas and water, through the method of grey relational analysis, principal component analysis,we analyze and summarize the relationship of the reservoir properties, logging curve characteristic value and productivity,in order to determine the main factors affecting productivity.3、Using the conventional well logging data to study the relations of the per meter oil production and the characteristic values of well logging (GR, Rt, AC, DEN, CNL) and reservoir parameter (bulk volume fraction of shale Vsh、porosity POR、permeability K, flow zone index FZI, oil saturation So), through the analysis, the key factors influence productivity are physical property of reservoir, oiliness and deep resistivity.According to liquid production per meter, flow zone index FZI can be able to divide pay formation well.4、To carry out the pore structure analysis as the core of the reservoir productivity classification, using bayes discriminant to do reservoir productivity classification, and thus on the basis of that, using displacement pressure classification method, productivity composite index method and reservoir classification index method to do quantitative prediction, then research and analysis these various prediction methods above.The results show that the idea not only for the logging of reservoir productivity evaluation and prediction of in-depth study provides some reference and basis, and also for the deployment and planning of regional development provides an important theoretical basis.
Keywords/Search Tags:productivity prediction, low porosity and low permeability, bayesdiscriminant, logging data
PDF Full Text Request
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