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dc.contributorJensen, Jerry L.
dc.creatorBouffin, Nicolas
dc.date.accessioned2010-01-15T00:15:12Z
dc.date.accessioned2010-01-16T02:24:36Z
dc.date.accessioned2017-04-07T19:56:55Z
dc.date.available2010-01-15T00:15:12Z
dc.date.available2010-01-16T02:24:36Z
dc.date.available2017-04-07T19:56:55Z
dc.date.created2007-08
dc.date.issued2009-06-02
dc.identifier.urihttp://hdl.handle.net/1969.1/ETD-TAMU-1953
dc.description.abstractNet pay (NP) and net-to-gross ratio (NGR) are often crucial quantities to characterize a reservoir and assess the amount of hydrocarbons in place. Numerous methods in the industry have been developed to evaluate NP and NGR, depending on the intended purposes. These methods usually involve the use of cut-off values of one or more surrogate variables to discriminate non-reservoir from reservoir rocks. This study investigates statistical issues related to the selection of such cut-off values by considering the specific case of using porosity () as the surrogate. Four methods are applied to permeability-porosity datasets to estimate porosity cut-off values. All the methods assume that a permeability cut-off value has been previously determined and each method is based on minimizing the prediction error when particular assumptions are satisfied. The results show that delineating NP and evaluating NGR require different porosity cut-off values. In the case where porosity and the logarithm of permeability are joint normally distributed, NP delineation requires the use of the Y-on-X regression line to estimate the optimal porosity cut-off while the reduced major axis (RMA) line provides the optimal porosity cut-off value to evaluate NGR. Alternatives to RMA and regression lines are also investigated, such as discriminant analysis and a data-oriented method using a probabilistic analysis of the porosity-permeability crossplots. Joint normal datasets are generated to test the ability of the methods to predict accurately the optimal porosity cut-off value for sampled sub datasets. These different methods have been compared to one another on the basis of the bias, standard error and robustness of the estimates. A set of field data has been used from the Travis Peak formation to test the performance of the methods. The conclusions of the study have been confirmed when applied to field data: as long as the initial assumptions concerning the distribution of data are verified, it is recommended to use the Y-on-X regression line to delineate NP while either the RMA line or discriminant analysis should be used for evaluating NGR. In the case where the assumptions on data distribution are not verified, the quadrant method should be used.
dc.language.isoen_US
dc.subjectnet pay
dc.subjectcut-off
dc.subjectsurrogate
dc.subjectestimator
dc.subjectbias
dc.subjectstandard error
dc.subjectMonte Carlo simulation
dc.titleNet pay evaluation: a comparison of methods to estimate net pay and net-to-gross ratio using surrogate variables
dc.typeBook
dc.typeThesis


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