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The Method Research Of The Reservoir Productivity Estimate Based On Logging Data

Posted on:2003-01-27Degree:MasterType:Thesis
Country:ChinaCandidate:J H GaoFull Text:PDF
GTID:2120360062486568Subject:Earth Exploration and Information Technology
Abstract/Summary:PDF Full Text Request
The productivity evaluation and estimate of the reservoir is an important part in the exploration^ and development of an oil field, since it is an key proportion of enhance the exploration effect and it can obtain elementary data which is important for the deployment and programming of the development. In this study, the evaluation method of the productivity and the influence factor of the productivity are analyzed, then a method of the productivity evaluation and estimate according to logging data of the reservoir is brought up. This kind of method can be applied to the test of multi layers and a layer. In this paper, some aspects as below are researched deeply, and some results are obtained.1. According to some right logging data and the theories of nerve network and fuzzy identify, an accurate reasonable classification model for the reservoir is established;2. According to the reservoir classification, the test productivity of multi layers is divided into the productivities of single layers, furthermore the productivities of oil and gas in a layer of a meter are obtained;3. Logging data as input of the network, the productivities of oil and gas in a layer of a meter as output of the network, the predict model for of genetic nerve network can be established according to the application of the theories of BP nurve network and genetic algorithms;4. According to the estimate model of step 3, the curve of the productivity of oil and gas in a layer of a meter can be computed. The parameter is first brought up by us.The method and the parameters in this paper can offer a new idea for the logging company of the oil field, and serve the exploration and development of the oil field.
Keywords/Search Tags:the productivity evaluation estimate, the logging data, the reservoir classification, the genetic nerve network, the productivities of oil and gas in a layer of a meter
PDF Full Text Request
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