Processing and Interpretation of Long-Term Data Acquired From Permanent Pressure Gauges

Abstract

Summary Long-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 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.

Upscaling of the permeability by multiscale wavelet transformations and simulation of multiphase flows in heterogeneous porous media

“…In addition to analyzing the seismic data which were one of the prime motivations for developing the WT, the applications also include numerical simulation of flow and transport equations [62], upscaling of a conductivity or permeability field [26,60,82], pressure transient analysis [5,25,28,47,87], upscaling of miscible displacements in heterogeneous porous media [27], reservoir characterization [11,55,59,63,66], identification of the fracture density along wells in oil and gas reservoirs [81], as well as problems involving turbulent transport and chemical reactions in the atmosphere [38]; for a recent review, see [78,80].…”

Fourier analysis of short-period water level variations in the Rotorua geothermal field, New Zealand

“…Hu et al (2006) found band-pass filtering using wavelets effective at identifying the amplitude and frequency of atmospheric tides. In a more general reservoir engineering context, Athichanagorn et al (2002) applied wavelet analysis to reduce the ambiguity and uncertainty in long-term subsurface data by identifying outlying data points and filtering unwanted noise before assessing aquifer parameters using the filtered data. Leaver (2006) undertook a comparative study of spectral analysis on data from three geothermal fields.…”

Hydrocarbon reservoir model detection from pressure transient data using coupled artificial neural network—Wavelet transform approach

“…In the last two decades, wavelet transform has appeared several times in the petroleum engineering for detecting changes in flow rate, transient identification, de-noising and up-scaling of reservoir properties [13][14][15]. Kikani and He used the wavelet transform for data de-noising and transient detection among synthetic pressure transient data [13].…”

“…Kikani and He used the wavelet transform for data de-noising and transient detection among synthetic pressure transient data [13]. Athichanagorn et al developed a wavelet based model for pre-processing and interpretation both long-term simulated as well as actual field data [14]. Olsen and Nordtvedt investigated the wavelet transform ability for filtering and noises removal from production data [15].…”