Confirmation of the validity and determination of the accuracy of permeability computation from Array Induction Tool (AIT) logs /
A method has been developed to estimate formation permeability using data from an Array Induction Tool (AIT) log. The method simulates mud filtrate invasion that occurs from the time a zone is drilled to the time it is logged. The invasion profile depends on the reservoir, mud, and fluid properties...
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| Format: | Thesis Book |
| Language: | English |
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[Place of publication not identified] :
[publisher not identified] ;
1998.
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| Online Access: | http://proxy.library.tamu.edu/login?url=http://proquest.umi.com/pqdweb?did=737708561&sid=1&Fmt=2&clientId=2945&RQT=309&VName=PQD |
| Summary: | A method has been developed to estimate formation permeability using data from an Array Induction Tool (AIT) log. The method simulates mud filtrate invasion that occurs from the time a zone is drilled to the time it is logged. The invasion profile depends on the reservoir, mud, and fluid properties and on drilling conditions. Mud filtrate invasion causes the resistivity profile within the formation near the borehole to change with time. To analyze the reservoir, our method compares a simulated resistivity profile to one measured by the AIT log. Reservoir permeability and three other problem parameters are allowed to vary during the calculations. Variations of these parameters change the simulated resistivity profile. The technique determines the set of parameters that produces the best match between simulated and observed resistivity profiles. The permeability that produces the best match is an estimate of the formation absolute permeability at a certain depth. The software we have developed during this research project is called Permlog. We found that Permlog works well for estimating the values of each parameter, when only one parameter is allowed to vary during any single computer run. try to resolve two or more parameters during a single computer run. We have investigated the relative importance of accuracy of different input data. We have found that water saturation and the data used to calculate water saturation must be known accurately before good estimates of absolute reservoir permeability can be computed. We evaluated the accuracy of Pen-PermLog using Monte Carlo error analysis. We found that if errors in the PermLog input parameters are random, PermLog will produce an unbiased estimate of permeability. Monte Carlo error analysis and testing of non linear egression algorithm (NLRA) showed that it may be difficult to improve the accuracy of permeability estimates from PermLog beyond a factor of 3 of the true value. We used stochastic methods to address the problem of scale. The stochastic procedure was based upon the correlation of core and log porosity values that we obtained from a tight gas sand. The results of our analysis indicate that the existence of a trend of change in the reservoir properties, and relatively small variations of individual measurements around the trend are critical for a good correlation of properties obtained from core and log measurements. Random errors of both core and log measurements decrease the value of the correlation coefficient. A field example shows that PermLog provides reasonably accurate permeability estimates in moderate permeability reservoirs. The reservoir permeability distribution provided by Pen-PermLog combined with the permeability estimate from welltest analysis allows a significantly more accurate forecast of production performance than welltest data only. |
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| Item Description: | Vita. "Major Subject: Petroleum Engineering". |
| Physical Description: | xviii, 267 leaves : illustrations ; 28 cm. Issued also on microfiche from University Microfilms Inc. |
| Bibliography: | Includes bibliographical references: pages 205-212. |