A Method To Normalize Log Data by Calibration to Large-Scale Data Trends

摘要

Wireline log data is subject to numerous potential errors due to the acquisition environment and assumptions made during acquisition. The literature sites log quality problems on the order of fifty percent of all well logs. The implication of this is that wireline log normalization is a critical step in obtaining meaningful log data for any reservoir characterization or modeling study. Often insufficient information is available to properly normalize logs using common or traditional methods. A new method is introduced in this paper that relies only on a sufficiently thick log interval to properly estimate the parameters needed for the normalization. Through the use of first vertical averaging of data by well to minimize localized reservoir variations, followed by areal averaging of multiple wells to estimate correction terms for tool calibration affects, a robust method of well log normalization is presented. An example using data from the Fullerton Clear Fork Field is presented to illustrate the method.