Lithologic characterization of a reservoir using continuous-wavelet transforms

Álvarez, Gustavo López; Sansó, Bruno; Michelena, Reinaldo J.; Jiménez, Juan Ramón

doi:10.1109/tgrs.2002.808065

Cited by 25 publications

(5 citation statements)

References 17 publications

(5 reference statements)

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“…used the WTs to analyze the porosity logs. Alvarez et al (2003) characterized the lithology of a reservoir using the GR log and seismic traces around wells. By calculating the wavelet coefficients and the "energy" of the WTs, they showed that the wavelet energy distributions of the wells in sandstones were significantly different from those of the wells in gravel.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Cross Correlations Between Well Logs of Hydrocarbon Reservoirs

et al. 2011

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We carry out a series of cross-correlation analysis of raw well-log data, in order to study the possible connection between natural gamma ray (GR) logs and other types of well logs, such as neutron porosity (NPHI), sonic transient time (denoted usually by DT), and bulk density (RHOB) of oil and gas reservoirs. Three distinct, but complementary, methods are used to analyze the cross correlations, namely, the multifractal detrended cross-correlation analysis (MF-DXA), the so-called Q cc (m) test in conjunction with the statistical test-the χ 2 (m) distribution-and the cross-wavelet transform (XWT) and wavelet coherency. The Q cc (m) test and MF-DXA are used to identify and quantify the strength of long-range cross correlations between the porosities derived based on the NPHI, DT, and RHOB logs on the one hand, and the GR log that is indicative of the presence of clay minerals in reservoir rocks, on the other hand. The Q cc (m) test describes qualitatively the presence of such cross correlations between the porosity and GR logs. Analysis by the MF-DXA method also indicates that the various porosity logs, the GR log, and the cross correlations between them are multifractal, hence confirming the long-range nature of the correlations. The results are confirmed further by the XWT, and indicate that the porosities estimated based on the NPHI logs are only weakly, if at all, affected by the natural GR radioactivity of reservoir rock and are, therefore, most reliable. The effect of length scale on the correlations and cross correlations 123 446 H. Dashtian et al. was studied in detail. It is shown that such correlations exist at all length scales, and that they are of multifractal type that must be characterized by a spectrum of exponents.

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Section: Introductionmentioning

confidence: 99%

Analysis of Cross Correlations Between Well Logs of Hydrocarbon Reservoirs

et al. 2011

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“…Rabiller et al (2003) used signal processing methods in order to speed up log data interpretations in the framework of sequence stratigraphy and sedimentary bodies' architecture while improving their consistency by minimizing any possible operator bias. Alvarez et al (2003) characterized lithologic properties of a reservoir using wavelet transform. Their method is based on an estimation of the power energy coefficient of signals corresponding to gamma ray logs as well as seismic traces penetrating the rock.…”

Section: Introductionmentioning

confidence: 99%

Intelligent sequence stratigraphy through a wavelet-based decomposition of well log data

Kadkhodaie

Rezaee

2017

Journal of Natural Gas Science and Engineering

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Identification of sequence boundaries is an important task in geological characterization of gas reservoirs. In this study, a continuous wavelet transform (CWT) approach is applied to decompose gamma ray and porosity logs into a set of wavelet coefficients at varying scales. A discrete wavelet transform (DWT) is utilized to decompose well logs into smaller frequency bandwidths called Approximations (A) and Details (D). The methodology is illustrated by using a case study from the Ilam and upper Sarvak formations in the Dezful embayment, southwestern Iran. Different graphical visualization techniques of the continuous wavelet transform results allowed a better understanding of the main sequence boundaries. Using the DWT, maximum flooding surface was successfully recognised from both highest frequency and low frequency contents of signals. There is a sharp peak in all A&D corresponding to the maximum flooding surface (MFS), which can specifically be seen in fifth Approximation (a5), fifth Detail (d5), fourth Detail (d4) and third Detail (d3) coefficients. Sequence boundaries were best recognised from the low frequency contents of signals, especially the fifth Approximation (a5). Normally, the troughs of the fifth Approximation correspond to sequence boundaries where higher porosities developed in the Ilam and upper Sarvak carbonate rocks. Through hybridizing both CWT and DWT coefficient a more effective discrimination of sequence boundaries was achieved. The results of this study show that wavelet transform is a successful, fast and easy approach for identification of the main sequence boundaries from well log data. There is a good agreement between core derived system tracts and those derived from decomposition of well logs by using the wavelet transform approach.

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“…For example, several techniques of spectral analyses (Fourier, Walsh, wavelet transforms) and fractal theory have been applied to solve the various geophysical problems. For instance, (i) log analysis (Doveton 1986;Pan et al 2008), (ii) reservoir characterization (Alvarez et al 2003), (iii) climate signal detection (Lau and Weng 1995), (iv) chemical analysis (Leung et al 1998), and (v) conjunction model for groundwater level forecasting (Adamowski and Chan 2011). KTB research geoscientist team has examined the fractal behaviour of well-log signal variability, presuming that well-log signals in the super deep German Continental Deep Drilling Program (KTB) borehole display nonlinear characteristics (Leonardi and Kumpel 1999).…”

Section: Introductionmentioning

confidence: 99%

Modelling discontinuous well log signal to identify lithological boundaries via wavelet analysis: An example from KTB borehole data

2016

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Identification of sharp and discontinuous lithological boundaries from well log signal stemming from heterogeneous subsurface structures assumes a special significance in geo-exploration studies. Well log data acquired from various geological settings generally display nonstationary/nonlinear characteristics with varying wavelengths and frequencies. Modelling of such complex well-log signals using the conventional signal processing techniques either fails to catch-up abrupt boundaries or at the best, do not provide precise information on insidious lithological discontinuities. In this paper, we have proposed a new wavelet transform-based algorithm to model the abrupt discontinuous changes from well log data by taking care of nonstationary characteristics of the signal. Prior to applying the algorithm on the geophysical well data, we analyzed the distribution of wavelet coefficients using synthetic signal generated by the first order nonstationary auto-regressive model and then applied the method on actual well log dataset obtained from the KTB bore hole, Germany. Besides identifying the formation of layered boundaries, the underlying method also maps some additional formation boundaries, which were hitherto undetected at the KTB site. The results match well with known geological lithostratigraphy and will be useful for constraining the future model of KTB bore hole data.

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Lithologic characterization of a reservoir using continuous-wavelet transforms

Cited by 25 publications

References 17 publications

Analysis of Cross Correlations Between Well Logs of Hydrocarbon Reservoirs

Analysis of Cross Correlations Between Well Logs of Hydrocarbon Reservoirs

Intelligent sequence stratigraphy through a wavelet-based decomposition of well log data

Modelling discontinuous well log signal to identify lithological boundaries via wavelet analysis: An example from KTB borehole data

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