Seismic amplitude variation with offset inversion in a vertical transverse isotropy medium

2019 ◽  
Vol 7 (3) ◽  
pp. T581-T593 ◽  
Author(s):  
Mark Sams ◽  
Annushia Annamalai ◽  
Jeremy Gallop
Keyword(s):  
Transverse Isotropy ◽  
Low Frequency ◽  
Seismic Inversion ◽  
Well Log ◽  
Amplitude Variation ◽  
Frequency Model ◽  
Isotropic Solution ◽  
Amplitude Variation With Offset ◽  
Vertical Transverse Isotropy ◽  
The Impact

Vertical transverse isotropy (VTI) will affect seismic inversion, but it is not possible to solve for the full set of anisotropic elastic parameters from amplitude variation with offset inversion because there exists an isotropic solution to every VTI problem. We can easily approximate the pseudoisotropic properties that result from the isotropic solution to the anisotropic problem for well-log data. We can then use these well-log properties to provide a low-frequency model for inversion and/or a framework for interpreting either absolute or relative inversion results. This, however, requires prior knowledge of the anisotropic properties, which are often unavailable or poorly constrained. If we ignore anisotropy and assume that the amplitude variations caused by VTI are going to be accounted for by effective wavelets, the inversion results would be in error: The impact of anisotropy is not merely a case of linear scaling of seismic amplitudes for any particular angle range. Ignoring VTI does not affect the prediction of acoustic impedance, but it does affect predictions of [Formula: see text] and density. For realistic values of anisotropy, these errors can be significant, such as predicting oil instead of brine. If the anisotropy of the rocks is known, then we can invert for the true vertical elastic properties using the known anisotropy coefficients through a facies-based inversion. This can produce a more accurate result than solving for pseudoelastic properties, and it can take advantage of the sometimes increased separation of isotropic and anisotropic rocks in the pseudoisotropic elastic domain. Because the effect of anisotropy will vary depending on the strength of the anisotropy and the distribution of the rocks, we strongly recommend forward modeling for each case prior to inversion to understand the potential impact on the study objectives.

Prediction of Petrophysical Properties from Seismic Inversion and Neural Network: A case study

2021 ◽  
Author(s):  
Siddharth Garia ◽  
Arnab Kumar Pal ◽  
Karangat Ravi ◽  
Archana M Nair
Keyword(s):  
Neural Network ◽  
Bulk Density ◽  
Low Frequency ◽  
Seismic Inversion ◽  
Well Log ◽  
Petrophysical Properties ◽  
Seismic Section ◽  
Frequency Model ◽  
Seismic Reflection Data ◽  
Reflection Data

<p>Seismic inversion method is widely used to characterize reservoirs and detect zones of interest, i.e., hydrocarbon-bearing zone in the subsurface by transforming seismic reflection data into quantitative subsurface rock properties. The primary aim of seismic inversion is to transform the 3D seismic section/cube into an acoustic impedance (AI) cube. The integration of this elastic attribute, i.e., AI cube with well log data, can thereafter help to establish correlations between AI and different petrophysical properties. The seismic inversion algorithm interpolates and spatially populates data/parameters of wells to the entire seismic section/cube based on the well log information. The case study presented here uses machine learning-neural network based algorithm to extract the different petrophysical properties such as porosity and bulk density from the seismic data of the Upper Assam basin, India. We analyzed three different stratigraphic  units that are established to be producing zones in this basin.</p><p> AI model is generated from the seismic reflection data with the help of colored inversion operator. Subsequently, low-frequency model is generated from the impedance data extracted from the well log information. To compensate for the band limited nature of the seismic data, this low-frequency model is added to the existing acoustic model. Thereafter, a feed-forward neural network (NN) is trained with AI as input and porosity/bulk density as target, validated with NN generated porosity/bulk density with actual porosity/bulk density from well log data. The trained network is thus tested over the entire region of interest to populate these petrophysical properties.</p><p>Three seismic zones were identified from the seismic section ranging from 681 to 1333 ms, 1528 to 1575 ms and 1771 to 1814 ms. The range of AI, porosity and bulk density were observed to be 1738 to 6000 (g/cc) * (m/s), 26 to 38% and 1.95 to 2.46 g/cc respectively. Studies conducted by researchers in the same basin yielded porosity results in the range of 10-36%. The changes in acoustic impedance, porosity and bulk density may be attributed to the changes in lithology. NN method was prioritized over other traditional statistical methods due to its ability to model any arbitrary dependency (non-linear relationships between input and target values) and also overfitting can be avoided. Hence, the workflow presented here provides an estimation of reservoir properties and is considered useful in predicting petrophysical properties for reservoir characterization, thus helping to estimate reservoir productivity.</p>

Quantitative interpretation using facies-based seismic inversion

2017 ◽  
Vol 5 (3) ◽  
pp. SL1-SL8 ◽  
Author(s):  
Ehsan Zabihi Naeini ◽  
Russell Exley
Keyword(s):  
Seismic Data ◽  
Low Frequency ◽  
Seismic Inversion ◽  
Quantitative Interpretation ◽  
Frequency Model ◽  
Amplitude Variation With Offset ◽  
Limited Bandwidth ◽  
Seismic Amplitude ◽  
Primary Signal

Quantitative interpretation (QI) is an important part of successful exploration, appraisal, and development activities. Seismic amplitude variation with offset (AVO) provides the primary signal for the vast majority of QI studies allowing the determination of elastic properties from which facies can be determined. Unfortunately, many established AVO-based seismic inversion algorithms are hindered by not fully accounting for inherent subsurface facies variations and also by requiring the addition of a preconceived low-frequency model to supplement the limited bandwidth of the input seismic. We apply a novel joint impedance and facies inversion applied to a North Sea prospect using broadband seismic data. The focus was to demonstrate the significant advantages of inverting for each facies individually and iteratively determine an optimized low-frequency model from facies-derived depth trends. The results generated several scenarios for potential facies distributions thereby providing guidance to future appraisal and development decisions.

Brittleness index calculation based on amplitude-variation-with-offset inversion for coal-bed methane reservoir: A case study of the Qinshui Basin, China

2020 ◽  
Vol 8 (1) ◽  
pp. SA63-SA72
Author(s):  
Wu Haibo ◽  
Cheng Yan ◽  
Zhang Pingsong ◽  
Dong Shouhua ◽  
Huang Yaping
Keyword(s):  
Seismic Data ◽  
Brittleness Index ◽  
Coal Bed ◽  
Coal Bed Methane ◽  
Well Log ◽  
Amplitude Variation ◽  
Frequency Model ◽  
Log Data ◽  
Amplitude Variation With Offset ◽  
Avo Inversion

The brittleness index (BI) is an important parameter for coal-bed methane (CBM) reservoir fracturing characterization. Most published studies have relied on petrophysical and well-log data to estimate the geomechanical properties of reservoir rocks. The major drawback of such methods is the lack of control away from well locations. Therefore, we have developed a method of combining BI calculation from well logs with that inverted from 3D seismic data to overcome the limitation. A real example is given here to indicate the workflow. A traditional amplitude-variation-with-offset (AVO) inversion was conducted first. BI for the CBM reservoir was then calculated from the Lamé constants inverted from prestack seismic data through a traditional AVO inversion method. We build an initial low-frequency model based on the well-log data. Comparison of the seismic inverted BI at the target reservoir and BI extracted from the well-log data showed satisfactory results. This method has been proved to be efficient and effective enough at identifying BI sweet spots in CBM reservoirs.

The impact of reservoir scale on amplitude variation with offset

2016 ◽  
Vol 65 (3) ◽  
pp. 736-746 ◽  
Author(s):  
Chao Xu ◽  
Jianxin Wei ◽  
Bangrang Di
Keyword(s):  
Amplitude Variation ◽  
Amplitude Variation With Offset ◽  
The Impact

Reservoir Prediction Under Control of Sedimentary Facies

2017 ◽  
Vol 25 (03) ◽  
pp. 1750022
Author(s):  
Xiuwei Yang ◽  
Peimin Zhu
Keyword(s):  
Seismic Data ◽  
Low Frequency ◽  
Prediction Method ◽  
Rock Physics ◽  
Real Data ◽  
Sedimentary Facies ◽  
Seismic Inversion ◽  
Rock Properties ◽  
Reservoir Prediction ◽  
Frequency Model

Acoustic impedance (AI) from seismic inversion can indicate rock properties and can be used, when combined with rock physics, to predict reservoir parameters, such as porosity. Solutions to seismic inversion problem are almost nonunique due to the limited bandwidth of seismic data. Additional constraints from well log data and geology are needed to arrive at a reasonable solution. In this paper, sedimentary facies is used to reduce the uncertainty in inversion and rock physics modeling; the results not only agree with seismic data, but also conform to geology. A reservoir prediction method, which incorporates seismic data, well logs, rock physics and sedimentary facies, is proposed. AI was first derived by constrained sparse spike inversion (CSSI) using a sedimentary facies dependent low-frequency model, and then was transformed to reservoir parameters by sequential simulation, statistical rock physics and [Formula: see text]-model. Two numerical experiments using synthetic model and real data indicated that the sedimentary facies information may help to obtain a more reasonable prediction.

Accuracy of wavelets, seismic inversion, and thin-bed resolution

2017 ◽  
Vol 5 (4) ◽  
pp. T523-T530
Author(s):  
Ehsan Zabihi Naeini ◽  
Mark Sams
Keyword(s):  
Seismic Data ◽  
Reservoir Characterization ◽  
Low Frequency ◽  
Seismic Inversion ◽  
Inversion Method ◽  
Frequency Content ◽  
Frequency Model ◽  
North West ◽  
The North ◽  
Well Data

Broadband reprocessed seismic data from the North West Shelf of Australia were inverted using wavelets estimated with a conventional approach. The inversion method applied was a facies-based inversion, in which the low-frequency model is a product of the inversion process itself, constrained by facies-dependent input trends, the resultant facies distribution, and the match to the seismic. The results identified the presence of a gas reservoir that had recently been confirmed through drilling. The reservoir is thin, with up to 15 ms of maximum thickness. The bandwidth of the seismic data is approximately 5–70 Hz, and the well data used to extract the wavelet used in the inversion are only 400 ms long. As such, there was little control on the lowest frequencies of the wavelet. Different wavelets were subsequently estimated using a variety of new techniques that attempt to address the limitations of short well-log segments and low-frequency seismic. The revised inversion showed greater gas-sand continuity and an extension of the reservoir at one flank. Noise-free synthetic examples indicate that thin-bed delineation can depend on the accuracy of the low-frequency content of the wavelets used for inversion. Underestimation of the low-frequency contents can result in missing thin beds, whereas underestimation of high frequencies can introduce false thin beds. Therefore, it is very important to correctly capture the full frequency content of the seismic data in terms of the amplitude and phase spectra of the estimated wavelets, which subsequently leads to a more accurate thin-bed reservoir characterization through inversion.

scholarly journals Analysis of AVO and Seismic Inversion for Reservoir Characterization (Case Study: EP Field, South Sumatera Basin)

2020 ◽  
Vol 2 (1) ◽  
Author(s):  
H Budiman, M.Y.N Khakim, A.K Affandi
Keyword(s):  
Reservoir Characterization ◽  
Seismic Inversion ◽  
Well Log ◽  
Class Iii ◽  
Amplitude Variation With Offset ◽  
Time Migration ◽  
Plot Analysis ◽  
Impedance Contrast ◽  
Sonic Log ◽  
Input Control

Abstract   A research about reservoir characterization with analysis of AVO (Amplitude Variation with Offset) and seismic inversion, to extract the petrophysics properties on the EP field South Sumatra Basin. This research was conducted to identify rock lithology and its spread, to see the sensitive parameters of physical properties of rocks. This research uses the 3D seismic data PSTM (Pre Stack Time Migration) as input control with data from the EP-036 well containing sonic log, density, gamma rays, neutron and resistivity log.  From the results of data analysis on the well log chart EP-036, reservoir target zones are at a depth of 714 to 722 m (TVD) or time domain 768 to 780 ms.  The results of the analysis AVO is able to detect the presence of reservoir gas sand, based on the classification of Rutherford and Williams (1989) the gas sand layer into AVO class III that indicates low impedance contrast sands. To analyze the results of well log data in the cross plot EP-036 indicates lithology is a hydrocarbon. It is also reinforced with cross plot analysis and seismic inversion results in the form of the parameter value ??, Vp/Vs and Acoustic impedance with low porosity averaging 22 to 35%, indicating that the zone is a zone reservoir potential gas sand.   Keywords: Inversion, AVO, LMR, Reservoir Characterization.

Improved seismic inversion and reservoir description of the Ichthys gas-condensate field, Browse Basin, Western Australia

2010 ◽  
Vol 50 (2) ◽  
pp. 716
Author(s):  
Masamichi Fujimoto ◽  
Takeshi Yoshida ◽  
Andrew Long
Keyword(s):  
Low Frequency ◽  
Seismic Inversion ◽  
Gas Condensate ◽  
Sensor Technology ◽  
Frequency Model ◽  
Porosity Distribution ◽  
Low Frequencies ◽  
Simultaneous Inversion ◽  
Dual Sensor ◽  
Well Data

Seismic inversion has become a standard geophysical tool to enhance seismic resolution, predict the reservoir porosity distribution, and to discriminate between reservoir and non-reservoir pay zones. Conventional seismic data does not record the low frequencies necessary for inversion. To enable a complete bandwidth, low frequencies are modelled from well data and are typically interpolated throughout the volume using seismic velocities. This often causes the resultant porosity distribution calculated from the inverted P-impedance to be biased by the well data and the geometry of well locations. Dual-sensor GeoStreamer technology was used to acquire a regional multi-client 2D survey by PGS in 2008, including some lines over the Ichthys gas-condensate field in the Browse Basin. Dual-sensor streamer processing recovers a wider frequency bandwidth than conventional seismic. Receiver ghost removal combined with deep streamer towing simultaneously boosts both the low and high frequencies. The improved bandwidth enables a higher quality of velocity analysis, which further improves resolution throughout the section. Simultaneous inversion of the data validated the uplift of the low frequency data, and significantly reduced the bias towards well data for the low frequency model. The resultant P-impedance data demonstrated an excellent tie to well data. The dual-sensor technology promises to improve the description of the porosity distribution within our reservoir model.

The vertical image projection method for migration stretch compensation

2021 ◽  
pp. 1-55
Author(s):  
Arash JafarGandomi
Keyword(s):  
Real Data ◽  
Amplitude Variation ◽  
Seismic Migration ◽  
Amplitude Variation With Offset ◽  
Amplitude Inversion ◽  
Image Projection ◽  
And Migration ◽  
Avo Attributes ◽  
Conditioning Approach ◽  
The Impact

True amplitude inversion is often carried out without taking into account migration distortions to the wavelet. Seismic migration leaves a dip-dependent effect on the wavelet that can cause significant inaccuracies in the inverted impedances obtained from conventional inversion approaches based on 1D vertical convolutional modelling. Neglecting this effect causes misleading inversion results and leakage of dipping noise and migration artifacts from higher frequency bands to the lower frequencies. I have observed that despite dip-dependency of this effect, low-dip and flat events may also suffer if they are contaminated with cross-cutting noise, steep migration artifacts, and smiles. In this paper I propose an efficient, effective and reversible data pre-conditioning approach that accounts for dip-dependency of the wavelet and is applied to migrated images prior to inversion. My proposed method consists of integrating data with respect to the total wavenumber followed by the differentiation with respect to the vertical wavenumber. This process is equivalent to applying a deterministic dip-consistent pre-conditioning that projects the data from the total wavenumber to the vertical wavenumber axis. This preconditioning can be applied to both pre- and post-stack data as well as to amplitude variation with offset (AVO) attributes such as intercept and gradient before inversion. The vertical image projection methodology that I propose here reduces the impact of migration artifacts such as cross-cutting noise and migration smiles and improves inverted impedances in both synthetic and real data examples. In particular I show that neglecting the proposed pre-conditioning leads to anomalously higher impedance values along the steeply dipping structures.

Prestack waveform inversion of three-dimensional seismic data — An example from the Rock Springs Uplift, Wyoming, USA

Geophysics ◽  
2017 ◽  
Vol 82 (1) ◽  
pp. B1-B12 ◽  
Author(s):  
Josiane Pafeng ◽  
Subhashis Mallick ◽  
Hema Sharma
Keyword(s):  
Seismic Data ◽  
Reservoir Characterization ◽  
Waveform Inversion ◽  
Three Dimensional ◽  
Seismic Inversion ◽  
Amplitude Variation ◽  
Amplitude Variation With Offset ◽  
Seismic Waveform ◽  
Rock Springs Uplift ◽  
Rock Springs

Applying seismic inversion to estimate subsurface elastic earth properties for reservoir characterization is a challenge in exploration seismology. In recent years, waveform-based seismic inversions have gained popularity, but due to high computational costs, their applications are limited, and amplitude-variation-with-offset/angle inversion is still the current state-of-the-art. We have developed a genetic-algorithm-based prestack seismic waveform inversion methodology. By parallelizing at multiple levels and assuming a locally 1D structure such that forward computation of wave equation synthetics is computationally efficient, this method is capable of inverting 3D prestack seismic data on parallel computers. Applying this inversion to a real prestack seismic data volume from the Rock Springs Uplift (RSU) located in Wyoming, USA, we determined that our method is capable of inverting the data in a reasonable runtime and producing much higher quality results than amplitude-variation-with-offset/angle inversion. Because the primary purpose for seismic data acquisition at the RSU was to characterize the subsurface for potential targets for carbon dioxide sequestration, we also identified and analyzed some potential primary and secondary storage formations and their associated sealing lithologies from our inversion results.

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