Processing and Interpretation of Long-term Data from Permanent Downhole Pressure Gauges

Abstract

TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractLong-term data from permanent gauges have the potential to provide more information about a reservoir than data from traditional pressure transient tests that last for a relatively small duration. Besides reducing ambiguity and uncertainties in the interpretation, long-term data also provide an insight on how reservoir properties may change as the reservoir is produced. This type of long-term surveillance provides the opportunity to look at the reservoir information in four dimensions rather than obtaining a glimpse or snapshot in time. However, the installation of permanent downhole gauges is only a recent phenomenon, and a methodology for the interpretation of the data has yet to be developed. The use of long-term data requires special handling and interpretation techniques due to the instability of in-situ permanent data acquisition systems, extremely large volume of data, incomplete flow rate history caused by unmeasured and uncertain rate changes, and dynamic changes in reservoir conditions and properties throughout the life of the reservoir.This study developed a multistep procedure for the processing and interpretation of long-term pressure data. The procedure was tested with several sets of simulated and actual field data. It was found to be an effective approach for the analysis of long-term pressure data from permanent gauges.

A comparison of methods for denoising of well test pressure data

“…Reservoir characteristics have long been studied by means of a variety of techniques such as well log data (Dashtian et al 2011), well test data (Braester and Zeitoun 1993;Vaferi et al 2011), seismic data (Arora and Tomar 2010), and well drilling data (Weber 1993). Among these methods, recording transient well pressures created by flow rate alteration is a useful measure to obtain reservoir properties (Athichanagorn and Horne 1995;Gringarten 1987;Sanni and Gringarten 2008;Vaferi and Eslamloueyan 2015). Reservoir pressure is one of the key variables that is used to obtain reservoir properties, monitor reservoir condition, and forecast reservoir performance (Athichanagorn et al 2002).…”

“…Among these methods, recording transient well pressures created by flow rate alteration is a useful measure to obtain reservoir properties (Athichanagorn and Horne 1995;Gringarten 1987;Sanni and Gringarten 2008;Vaferi and Eslamloueyan 2015). Reservoir pressure is one of the key variables that is used to obtain reservoir properties, monitor reservoir condition, and forecast reservoir performance (Athichanagorn et al 2002). In well pressure testing, wellbore pressure and flow rate are monitored to evaluate reservoir characteristics by matching a simplified reservoir model on pressure responses (Blasingame et al 1989;Bourdet and Gringarten 1980;Ghaffarian et al 2014;Horne and Reyner 1995;Jeirani and Mohebbi 2006).…”

“…On the other hand, there are some anomalies in the recorded data which are distinguished from noise. For example, outliers are data points that lie away from the trend of data, while noise is considered as data points around the trend of overall data and in the same neighborhood as the true data (Athichanagorn et al 2002). Either noise or other artifacts such as outliers may cause high degree of uncertainty in well test interpretations, and hence, it is essential to remove noise and other artifacts or reduce them to acceptable levels before further analysis.…”

Data Integration for the Generation of High Resolution Reservoir Models

“…In the petroleum engineering literature, the wavelet transform has been applied to permanent pressure gauge data to remove noise (denoising) and identify events (Kikani and He, 1998;Athichanagorn et al, 1999). Kikani and He (1998) recommend a translation invariant wavelet transform using the Haar wavelet with soft thresholding for denoising and use the modulus maximus principal to identify rate changes and maintain its position in time.…”

“…Kikani and He (1998) recommend a translation invariant wavelet transform using the Haar wavelet with soft thresholding for denoising and use the modulus maximus principal to identify rate changes and maintain its position in time. Athichanagorn et al (1999) indicate that a spline wavelet is more suitable for event detection (e.g., identification of rate changes) and introduce a hybrid thresholding procedure for denoising. Specifically, they use soft thresholding in regions where data is continuous and hard thresholding near discontinuities.…”