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dc.contributor.advisorJohn P. Grotzinger.en_US
dc.contributor.authorAina, Clement Olajide, 1963-en_US
dc.contributor.otherMassachusetts Institute of Technology. Dept. of Earth, Atmospheric, and Planetary Sciences.en_US
dc.date.accessioned2005-08-22T19:04:23Z
dc.date.available2005-08-22T19:04:23Z
dc.date.copyright1999en_US
dc.date.issued1999en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/9529
dc.descriptionThesis (S.M.)--Massachusetts Institute of Technology, Dept. of Earth, Atmospheric, and Planetary Sciences, 1999.en_US
dc.descriptionIncludes bibliographical references (leaves 60-63).en_US
dc.description.abstractDetailed understanding of the heterogeneities and complexity of reservoir architecture and flow properties are crucial to development and exploitation of commercial hydrocarbon reservoirs. Thus, reservoir characterization and simulation studies are done on a continuous basis during the life of a field from initial exploration through appraisal, development and eventual abandonment. A key component of these studies is the knowledge of the reservoir permeability across the field. However, permeability is only measured directly at the pore scale from core, and since cores are rarely taken in a significant percentage of the wells in a field, estimation methods are commonly used to predict the permeability in wells without core data. These methods have included empirical and statistical approaches, as well as the emerging pattern recognition techniques. The accuracy of most methods are greatly enhanced when the reservoir is subdivided into units with common flow properties. In this thesis, a case study is carried out in the Benin River/Gbokoda field in Nigeria, with the aim of developing from existing tools, a facies based, simple to use, accurate and readily available technique to predict permeability in fields where there is at least one well that has core data for calibration of the reservoir properties and facies. The use of the facies data to constrain the prediction greatly improved the match between the predicted and the actual. The reservoir is subdivided into depositional groupings based on lithofacies and facies association, flow properties, and ease of recognition on wireline logs. Linear equations were developed from multiple regression of wire line log data to predict this groupings. The predicted groupings and the wireline Jog data were used in a multiple regression to develop another set of linear equations to predict permeability in each grouping. The equations produced were applied to a test well that had core data but was not used in the study. The predicted groupings and permeability from the test well was in very close agreement with the original data. The equations are next applied to other wells in the field.en_US
dc.description.statementofresponsibilityby Clement Olajide Aina.en_US
dc.format.extent63 leavesen_US
dc.format.extent4233654 bytes
dc.format.extent4233415 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypeapplication/pdf
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsM.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582
dc.subjectEarth, Atmospheric, and Planetary Sciences.en_US
dc.titlePermeability prediction in Benin River/Gbokoda field in Nigeria : a case study using facies derived from core studies and multiple regression of wireline dataen_US
dc.typeThesisen_US
dc.description.degreeS.M.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Earth, Atmospheric, and Planetary Sciencesen_US
dc.identifier.oclc43876378en_US


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