Resolving the Shifting of Pressure, Temperature and Spinner Dataset Using Geostatistical Prediction Data Methods: in Case of Single Error Data Recorded Tools

Abstract

The package of data recorded from routinely well measurement are Pressure, Temperature, and Spinner Measurement either Injection, Production or even Monitoring Wells. Somehow, the measurements operation faces several obstacles which affect the quality of recorded data. In this case, Pressure, Temperature and Spinner measurements in Well “K” during the operation was going well, but after downloading the Log Down and Log Up dataset with speed 20, and 30 ft/min, there was a difference pattern between Log Up and Log Down Data in speed 30 ft/min, through comparing all the data, it can find that the Log Up of speed 30 ft/min is an anomaly among the other data where the data was shifted lower than others. This paper will propose several geostatistical methods such as regression and Monte Carlo. Finally, the best methods to predict and repositioned the data is regression methods with the regression coefficient 0.9. This methods could be assigned for any kind of shifting dataset with the constraint of another normal pattern of dataset.