Application of Wavelet Transform to the Analysis of Pressure-Transient Data

Soliman, Mohamed Y.; Ansah, Joseph; Stephenson, Stan; Mandal, Batakrishna

doi:10.2118/83670-pa

Cited by 24 publications

(6 citation statements)

References 12 publications

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“…But there are also some problems with the derivative method. 22 Soliman et al 22 applied the Daubechies family of wavelets, and specifically combined analysis involving only Db1, Db2, Db3, and Db4 detail coefficient at several levels, for analyzing pressure signals. They took a three-step method in their study.…”

Section: Well Test Analysismentioning

confidence: 99%

Review of the Application of Wavelet Analysis in the Petroleum Industry

Guan¹,

Du²

2004

Canadian International Petroleum Conference

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Wavelet transforms are a family of basis functions that separate a function of a signal into distinct frequency packets that are localized in the time domain. Thus, wavelets are well suited for analyzing nonstationary data. It can smooth the basic signals and keep and even enhance of details. So it provides a multiresolution framework for data representation.Wavelet transform is now used in a wide variety of applications in the areas of medicine, biology, data compression, etc. In recent years, wavelet analysis is increasingly applied to the data analysis in petroleum industry. This paper reviews the recent application of the wavelet transform methodologies to data analysis in petroleum industry, especially the application in reservoir engineering fields. Various field examples are provided to demonstrate the applications of wavelet transform in petroleum industry, especially in the areas of well testing, well logging, seismic, and petroleum geology.

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Section: Well Test Analysismentioning

confidence: 99%

Review of the Application of Wavelet Analysis in the Petroleum Industry

Guan¹,

Du²

2004

Canadian International Petroleum Conference

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“…Efforts in this direction have focused primarily on two aspects. First is the use of wavelet transform to denoise time-dependent data such as well pressure and flow rates measured over a long period of time (Kikani and He 1998;Athichanagorn et al 2002;Soliman et al 2003;Zheng and Li 2007). The objective is to remove artificial effects from recorded data and to extract the relevant information without substantial loss in accuracy.…”

Section: Introductionmentioning

confidence: 99%

A Multiresolution Analysis of the Relationship Between Spatial Distribution of Reservoir Parameters and Time Distribution of Well-Test Data

Awotunde

Horne

2011

SPE Reservoir Evaluation &Amp; Engineering

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With recent advances in permanent downhole technology, well-test analysts now have to deal with enormous amounts of data. Using these data requires the development of efficient algorithms that are able to extract the relevant information from the data at minimal cost. We present a multiresolution wavelet approach to estimate the spatial distribution of reservoir parameters, by performing the nonlinear least-squares regression in the wavelet domains of both time and space. Wavelet transforms have the ability to reveal important events in time signals or spatial images. Thus, we transformed both the model space and the time-series pressure data into spatial wavelet and time wavelet domains, respectively, and used a thresholding to select a subset of wavelet coefficients from each of the transformed domains. These subsets were used subsequently in nonlinear regression to estimate the appropriate description of reservoir parameters. The appropriate subset is not only smaller; the problem is also reduced to the consideration of only the important components of the measured data and only the part of the reservoir description that depends on them.As a test of the approach, we first applied the model to well-test problems involving 1D (radially composite) reservoir systems. The inverse problem was solved to estimate the distributed permeability values by performing the nonlinear least-squares regression in the wavelet domains (time and space).Results obtained were compared with those obtained from the conventional nonlinear-regression approach, using all the pressure-time data and the full set of spatial reservoir parameters. The time/space wavelet approach proved to be efficient. By reducing the dimensions of the model and data spaces, the approach eliminates redundancy in the reservoir description and in the data set. Significantly, the approach reveals the true number of reservoir parameters that can be appropriately estimated from a given data set and also reveals which components of the full data set are active in constraining the reservoir model. Thus, the approach provides a good means to integrate different data properly while avoiding the inclusion of irrelevant data during nonlinear regression.

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“…A mathematical transform of the signal changes it in frequency domain and, consequently, reveals hidden information carried by the signal (Soliman et al 2003). Mathematical analysis of this signal may reveal hidden or masked information.…”

Section: Introductionmentioning

confidence: 99%

“…The wavelet transform is a widespread technique in data analysis, with applications ranging from signal processing to statistical analysis. An important feature of wavelet transform is its ability of smoothing the basic signal and retaining and even amplifying the details (Soliman et al 2003). A broad description of these applications may be found in Guan et al (2004).…”

Section: Introductionmentioning

confidence: 99%

Wavelet Filtering of Permanent Downhole Gauge Data

Pico

Aguiar

Pires

2009

Latin American and Caribbean Petroleum Engineering Conference

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Permanent downhole gauges (PDG) for pressure and temperature are very common in oil and gas wells. The data collected by PDG´s are used for monitoring the well condition and reservoir performance and, additionally, may provide new and complementary information for reservoir characterization. PDG record data at high sample rates, usually as high as thousands per hour. The computational analysis of this large volume of data requires a previous processing for the removal of noise and outliers and for the reduction of the filtered data. The Discrete Wavelet Transform (DWT) and the Multiresolution analysis were used successfully for this purpose. In the Multiresolution analysis, the DWT is used to decompose a signal in approximation and detail coefficients through low pass and high pass filters, respectively. The coefficients are modified, according to a thresholding method, to remove the noise and outliers before the reconstruction of the original signal. The final result depends on the kind of wavelet and resolution level used for the decomposition process, the method used to estimate the noise level and the rules used to modify the coefficients. In this work, the performance of different wavelet decomposition, noise threshold estimators and thresholding methods was investigated. Several combinations were tested on synthetic and actual data to establish a procedure that efficiently removes the noise and preserves the sharp features, characteristic of changes in production or injection rates, with minimum user intervention.

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Application of Wavelet Transform to the Analysis of Pressure-Transient Data

Cited by 24 publications

References 12 publications

Review of the Application of Wavelet Analysis in the Petroleum Industry

Review of the Application of Wavelet Analysis in the Petroleum Industry

A Multiresolution Analysis of the Relationship Between Spatial Distribution of Reservoir Parameters and Time Distribution of Well-Test Data

Wavelet Filtering of Permanent Downhole Gauge Data

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