High-resolution passive seismic tomography for 3D velocity, Poisson’s ratio ν, and P-wave quality QP in the Delvina hydrocarbon field, southern Albania

Geophysics ◽  
2011 ◽  
Vol 76 (3) ◽  
pp. B89-B112 ◽  
Author(s):  
G-Akis Tselentis ◽  
Nikolaos Martakis ◽  
Paraskevas Paraskevopoulos ◽  
Athanasios Lois
Keyword(s):  
Wave Velocity ◽  
Seismic Tomography ◽  
Oil Field ◽  
P Wave ◽  
S Wave ◽  
Gas Field ◽  
Data Set ◽  
Geophysical Surveys ◽  
Passive Seismic ◽  
Hydrocarbon Field

We have studied using traveltimes of P- and S-waves and initial seismic-pulse rise-time measurements from natural microearthquakes to derive 3D P-wave velocity VP information (mostly structural) as well as P- and S-wave velocity VP/VS and P-wave quality factor QP information (mostly lithologic) in a known hydrocarbon field in southern Albania. During a 12-month monitoring period, 1860 microearthquakes were located at a 50-station seismic network and were used to obtain the above parameters. The data set included earthquakes with magnitudes ranging from –0.1 to 3.0 R (Richter scale) and focal depths typically occurring between 2 and 10 km. Kohonen neural networks were implemented to facilitate the lithological classification of the passive seismic tomography (PST) results. The obtained results, which agreed with data from nearby wells, helped delineate the structure of the reservoir. Two subregions of the investigated area, one corresponding to an oil field and one to a gas field, were correlated with the PST results. This experiment showed that PST is a powerful new geophysical technique for exploring regions that present seismic penetration problems, difficult topographies, and complicated geologies, such as thrust-belt regions. The method is economical and environmentally friendly, and it can be used to investigate very large regions for the optimal design of planned 2D or 3D conventional geophysical surveys.

Detection of gas in sandstone reservoirs using AVO analysis: A 3-D seismic case history using the Geostack technique

Geophysics ◽  
1994 ◽  
Vol 59 (9) ◽  
pp. 1362-1376 ◽  
Author(s):  
Jan L. Fatti ◽  
George C. Smith ◽  
Peter J. Vail ◽  
Peter J. Strauss ◽  
Philip R. Levitt
Keyword(s):  
Wave Velocity ◽  
High Amplitude ◽  
Igneous Rocks ◽  
P Wave ◽  
S Wave ◽  
Gas Field ◽  
Data Set ◽  
Rock Types ◽  
Fluid Factor ◽  
Clastic Sedimentary

The Geostack technique is a method of analyzing seismic amplitude variation with offset (AVO) information. One of the outputs of the analysis is a set of direct hydrocarbon indicator traces called “fluid factor” traces. The fluid factor trace is designed to be low amplitude for all reflectors in a clastic sedimentary sequence except for rocks that lie off the (mudrock line.) The mudrock line is the line on a crossplot of P‐wave velocity against S‐wave velocity on which water‐saturated sandstones, shales, and siltstones lie. Some of the rock types that lie off the mudrock line are gas‐saturated sandstones, carbonates, and igneous rocks. In the absence of carbonates and igneous rocks, high amplitude reflections on fluid factor traces would be expected to represent gas‐saturated sandstones. Of course, this relationship does not apply exactly in nature, and the extent to which the mudrock line model applies varies from area to area. However, it is a useful model in many basins of the world, including the one studied here. Geostack processing has been done on a 3-D seismic data set over the Mossel Bay gas field on the southern continental shelf of South Africa. We found that anomalously high amplitude fluid factor reflections occurred at the top and base of the gas‐reservoir sandstone. Maps were made of the amplitude of these fluid factor reflections, and it was found that the high amplitude values were restricted mainly to the gas field area as determined by drilling. The highest amplitudes were found to be located roughly in the areas of best reservoir quality (i.e., highest porosity) in areas where the reservoir is relatively thick.

Shear‐wave velocity and density estimation from PS-wave AVO analysis: Application to an OBS dataset from the North Sea

Geophysics ◽  
2000 ◽  
Vol 65 (5) ◽  
pp. 1446-1454 ◽  
Author(s):  
Side Jin ◽  
G. Cambois ◽  
C. Vuillermoz
Keyword(s):  
Wave Velocity ◽  
North Sea ◽  
Random Noise ◽  
P Wave ◽  
S Wave ◽  
Data Set ◽  
Avo Analysis ◽  
The North ◽  
Avo Inversion ◽  
The North Sea

S-wave velocity and density information is crucial for hydrocarbon detection, because they help in the discrimination of pore filling fluids. Unfortunately, these two parameters cannot be accurately resolved from conventional P-wave marine data. Recent developments in ocean‐bottom seismic (OBS) technology make it possible to acquire high quality S-wave data in marine environments. The use of (S)-waves for amplitude variation with offset (AVO) analysis can give better estimates of S-wave velocity and density contrasts. Like P-wave AVO, S-wave AVO is sensitive to various types of noise. We investigate numerically and analytically the sensitivity of AVO inversion to random noise and errors in angles of incidence. Synthetic examples show that random noise and angle errors can strongly bias the parameter estimation. The use of singular value decomposition offers a simple stabilization scheme to solve for the elastic parameters. The AVO inversion is applied to an OBS data set from the North Sea. Special prestack processing techniques are required for the success of S-wave AVO inversion. The derived S-wave velocity and density contrasts help in detecting the fluid contacts and delineating the extent of the reservoir sand.

The 1998 Valhall microseismic data set: An integrated study of relocated sources, seismic multiplets, and S-wave splitting

Geophysics ◽  
2009 ◽  
Vol 74 (5) ◽  
pp. B183-B195 ◽  
Author(s):  
K. De Meersman ◽  
J.-M. Kendall ◽  
M. van der Baan
Keyword(s):  
Seismic Anisotropy ◽  
Amplitude Ratio ◽  
Temporal Variations ◽  
Oil Field ◽  
P Wave ◽  
Wave Form ◽  
S Wave ◽  
Data Set ◽  
Wave Splitting ◽  
Source Mechanisms

We relocate 303 microseismic events recorded in 1998 by sensors in a single borehole in the North Sea Valhall oil field. A semiautomated array analysis method repicks the P- and S-wave arrival times and P-wave polarizations, which are needed to locate these events. The relocated sources are confined predominantly to a [Formula: see text]-thick zone just above the reservoir, and location uncertainties are half those of previous efforts. Multiplet analysis identifies 40 multiplet groups, which include 208 of the 303 events. The largest group contains 24 events, and five groups contain 10 or more events. Within each multiplet group, we further improve arrival-time picking through crosscorrelation, which enhances the relative accuracy of the relocated events and reveals that more than 99% of the seismic activity lies spatially in three distinct clusters. The spatial distribution of events and wave-form similarities reveal two faultlike structures that match well with north-northwest–south-southeast-trending fault planes interpreted from 3D surface seismic data. Most waveform differences between multiplet groups located on these faults can be attributed to S-wave phase content and polarity or P-to-S amplitude ratio. The range in P-to-S amplitude ratios observed on the faults is explained best in terms of varying source mechanisms. We also find a correlation between multiplet groups and temporal variations in seismic anisotropy, as revealed by S-wave splitting analysis. We explain these findings in the context of a cyclic recharge and dissipation of cap-rock stresses in response to production-driven compaction of the underlying oil reservoir. The cyclic nature of this mechanism drives the short-term variations in seismic anisotropy and the reactivation of microseismic source mechanisms over time.

Full-waveform matching of VP and VS models from surface waves

2019 ◽  
Vol 218 (3) ◽  
pp. 1873-1891 ◽  
Author(s):  
Farbod Khosro Anjom ◽  
Daniela Teodor ◽  
Cesare Comina ◽  
Romain Brossier ◽  
Jean Virieux ◽  
...  
Keyword(s):  
Surface Waves ◽  
Wave Velocity ◽  
Real Data ◽  
Test Site ◽  
P Wave ◽  
S Wave ◽  
Surface Wave Dispersion ◽  
Data Set ◽  
Near Surface ◽  
Velocity Models

SUMMARY The analysis of surface wave dispersion curves (DCs) is widely used for near-surface S-wave velocity (VS) reconstruction. However, a comprehensive characterization of the near-surface requires also the estimation of P-wave velocity (VP). We focus on the estimation of both VS and VP models from surface waves using a direct data transform approach. We estimate a relationship between the wavelength of the fundamental mode of surface waves and the investigation depth and we use it to directly transform the DCs into VS and VP models in laterally varying sites. We apply the workflow to a real data set acquired on a known test site. The accuracy of such reconstruction is validated by a waveform comparison between field data and synthetic data obtained by performing elastic numerical simulations on the estimated VP and VS models. The uncertainties on the estimated velocity models are also computed.

Advances in surface-wave tomography for near-surface applications

2021 ◽  
Vol 40 (8) ◽  
pp. 567-575
Author(s):  
Myrto Papadopoulou ◽  
Farbod Khosro Anjom ◽  
Mohammad Karim Karimpour ◽  
Valentina Laura Socco
Keyword(s):  
Surface Wave ◽  
Wave Velocity ◽  
P Wave ◽  
Seismic Exploration ◽  
S Wave ◽  
Data Set ◽  
Near Surface ◽  
Processing Times ◽  
Spatial Coverage ◽  
Wave Tomography

Surface-wave (SW) tomography is a technique that has been widely used in the field of seismology. It can provide higher resolution relative to the classical multichannel SW processing and inversion schemes that are usually adopted for near-surface applications. Nevertheless, the method is rarely used in this context, mainly due to the long processing times needed to pick the dispersion curves as well as the inability of the two-station processing to discriminate between higher SW modes. To make it efficient and to retrieve pseudo-2D/3D S-wave velocity (VS) and P-wave velocity (VP) models in a fast and convenient way, we develop a fully data-driven two-station dispersion curve estimation, which achieves dense spatial coverage without the involvement of an operator. To handle higher SW modes, we apply a dedicated time-windowing algorithm to isolate and pick the different modes. A multimodal tomographic inversion is applied to estimate a VS model. The VS model is then converted to a VP model with the Poisson's ratio estimated through the wavelength-depth method. We apply the method to a 2D seismic exploration data set acquired at a mining site, where strong lateral heterogeneity is expected, and to a 3D pilot data set, recorded with state-of-the-art acquisition technology. We compare the results with the ones retrieved from classical multichannel analysis.

Bayesian linearized AVO inversion

Geophysics ◽  
2003 ◽  
Vol 68 (1) ◽  
pp. 185-198 ◽  
Author(s):  
Arild Buland ◽  
Henning Omre
Keyword(s):  
Wave Velocity ◽  
Posterior Distribution ◽  
Acoustic Impedance ◽  
Velocity Ratio ◽  
P Wave ◽  
Inversion Method ◽  
Inversion Algorithm ◽  
S Wave ◽  
Data Set ◽  
Avo Inversion

A new linearized AVO inversion technique is developed in a Bayesian framework. The objective is to obtain posterior distributions for P‐wave velocity, S‐wave velocity, and density. Distributions for other elastic parameters can also be assessed—for example, acoustic impedance, shear impedance, and P‐wave to S‐wave velocity ratio. The inversion algorithm is based on the convolutional model and a linearized weak contrast approximation of the Zoeppritz equation. The solution is represented by a Gaussian posterior distribution with explicit expressions for the posterior expectation and covariance; hence, exact prediction intervals for the inverted parameters can be computed under the specified model. The explicit analytical form of the posterior distribution provides a computationally fast inversion method. Tests on synthetic data show that all inverted parameters were almost perfectly retrieved when the noise approached zero. With realistic noise levels, acoustic impedance was the best determined parameter, while the inversion provided practically no information about the density. The inversion algorithm has also been tested on a real 3‐D data set from the Sleipner field. The results show good agreement with well logs, but the uncertainty is high.

Inversion of P-wave VSP data for local anisotropy: Theory and case study

Geophysics ◽  
2007 ◽  
Vol 72 (4) ◽  
pp. D69-D79 ◽  
Author(s):  
Vladimir Grechka ◽  
Albena Mateeva
Keyword(s):  
Wave Velocity ◽  
Transversely Isotropic ◽  
P Wave ◽  
Depth Interval ◽  
Clear Correlation ◽  
S Wave ◽  
Local Anisotropy ◽  
Data Set ◽  
Rock Formations ◽  
Vsp Data

We discuss, improve, and apply the slowness-polarization method for estimating local anisotropy from VSP data. Although the idea of fitting a given anisotropic model to the apparent slownesses measured along a well and polarization vectors recorded by three-component downhole geophones is hardly new, we extend the area of applicability of the technique and make the anisotropic inversion more robust by eliminating the most operationally difficult and noisy portion of the data, the shear waves. We show that the shear-wave velocity is actually unnecessary for fitting the slowness-of-polarization dependence of P-wave VSP data. For the most common geometry of a vertical borehole in a vertically transversely isotropic subsurface, such data are governed by the P-wave vertical velocity [Formula: see text] and two quantities, [Formula: see text] and [Formula: see text], that describe the influence of anisotropy. These quantities depend on conventional anisotropic coefficients [Formula: see text] and [Formula: see text] and absorb the S-wave velocity. We apply the developed theory to a 2D walkaway VSP acquired over a subsalt prospect in the Gulf of Mexico. Our data set contains geophones placed both inside the salt and beneath it, allowing us to estimate the anisotropy of different rock formations. We find the salt to be nearly isotropic in the examined [Formula: see text] [Formula: see text] depth interval. In contrast, the sediments below the salt exhibit substantial anisotropy. While the physical origins of subsalt anisotropy are still to be fully understood, we observe a clear correlation between lithology and the values of [Formula: see text] and [Formula: see text]: both anisotropic coefficients are greater in shales and smaller in the sandier portion of the well.

Quantitative prediction of total organic carbon content in shale-gas reservoirs using seismic data: A case study from the Lower Silurian Longmaxi Formation in the Chang Ning gas field of the Sichuan Basin, China

2018 ◽  
Vol 6 (4) ◽  
pp. SN153-SN168 ◽  
Author(s):  
Sheng Chen ◽  
Wenzhi Zhao ◽  
Qingcai Zeng ◽  
Qing Yang ◽  
Pei He ◽  
...  
Keyword(s):  
Wave Velocity ◽  
Shale Gas ◽  
Rock Physics ◽  
Elastic Parameter ◽  
P Wave ◽  
Quantitative Prediction ◽  
S Wave ◽  
Gas Field ◽  
Lower Silurian ◽  
Data Volume

We present a quantitative prediction of total organic carbon (TOC) content for shale-gas development in the Chang Ning gas field of the Sichuan Basin (China). We have used the rock-physics analysis method to define the geophysical characteristics of the reservoir and the most sensitive elastic parameter to TOC content. We established a quantitative prediction template of the TOC content by rock-physics modeling. Well data and 3D seismic data were combined for prestack simultaneous inversion to obtain the most sensitive elastic parameter data volume. According to the prediction template, we transformed the sensitive elastic parameter data volume to the TOC content volume. The rock-physics analysis indicates that the reservoir with a high TOC content in the Lower Silurian Longmaxi Formation (Fm) of the Chang Ning (CN) gas field is characterized by low density, low P-wave velocity ([Formula: see text]), low S-wave velocity ([Formula: see text]), low Poisson’s ratio (PR), and low ratio of P-wave velocity to S-wave velocity ([Formula: see text]). Density is the most sensitive elastic parameter to TOC content. The rock-physics model suggests that density is negatively correlated with TOC content, and the relationship between them changes under different porosities. The reservoir with high TOC content is mainly distributed at the bottom of the Longmaxi Fm and in the central and east central area of the study field. The quantitative prediction results are in good agreement with the log interpretation and production test. Therefore, it has important implications for the efficient development of the shale-gas reservoir in the basin.

scholarly journals Development a Statistical Relationship between Compressional Wave Velocity and Petrophysical Properties from Logs Data for JERIBE Formation ASMARI Reservoir in FAUQI Oil Field

2021 ◽  
Vol 22 (3) ◽  
pp. 1-9
Author(s):  
Qahtan Abdul Aziz ◽  
Hassan Abdul Hussein
Keyword(s):  
Wave Velocity ◽  
Compressional Wave ◽  
Oil Field ◽  
P Wave ◽  
Statistical Relationship ◽  
Compressional Wave Velocity ◽  
S Wave ◽  
Petrophysical Parameters ◽  
Fluid Saturation ◽  
P Wave Velocity

The Compressional-wave (Vp) data are useful for reservoir exploration, drilling operations, stimulation, hydraulic fracturing employment, and development plans for a specific reservoir. Due to the different nature and behavior of the influencing parameters, more complex nonlinearity exists for Vp modeling purposes. In this study, a statistical relationship between compressional wave velocity and petrophysical parameters was developed from wireline log data for Jeribe formation in Fauqi oil field south Est Iraq, which is studied using single and multiple linear regressions. The model concentrated on predicting compressional wave velocity from petrophysical parameters and any pair of shear waves velocity, porosity, density, and fluid saturation in carbonate rocks. A strong linear correlation between P-wave velocity and S-wave velocity and between P-wave velocity and density rock was found. The resulting linear equations can be used to estimate P-wave velocity from the S-wave velocity in the case of both. The results of multiple regression analysis indicated that the density, porosity, water-saturated, and shear wave velocity (VS) are strongly related to Vp.

scholarly journals Identifying recharge under subtle ephemeral features in a flat-lying semi-arid region using a combined geophysical approach

2020 ◽  
Vol 24 (9) ◽  
pp. 4353-4368 ◽  
Author(s):  
Brady A. Flinchum ◽  
Eddie Banks ◽  
Michael Hatch ◽  
Okke Batelaan ◽  
Luk J. M. Peeters ◽  
...  
Keyword(s):  
Surface Water ◽  
Wave Velocity ◽  
Arid Region ◽  
P Wave ◽  
Seismic Survey ◽  
Small Scale ◽  
S Wave ◽  
Geophysical Surveys ◽  
Semi Arid Region ◽  
Semi Arid

Abstract. Identifying and quantifying recharge processes linked to ephemeral surface water features is challenging due to their episodic nature. We use a combination of well-established near-surface geophysical methods to provide evidence of a surface and groundwater connection under a small ephemeral recharge feature in a flat, semi-arid region near Adelaide, Australia. We use a seismic survey to obtain P-wave velocity through travel-time tomography and S-wave velocity through the multichannel analysis of surface waves. The ratios between P-wave and S-wave velocities are used to calculate Poisson's ratio, which allow us to infer the position of the water table. Separate geophysical surveys were used to obtain electrical conductivity measurements from time-domain electromagnetics and water contents from downhole nuclear magnetic resonance. The geophysical observations provide evidence to support a groundwater mound underneath a subtle ephemeral surface water feature. Our results suggest that recharge is localized and that small-scale ephemeral features may play an important role in groundwater recharge. Furthermore, we show that a combined geophysical approach can provide a perspective that helps shape the hydrogeological conceptualization of a semi-arid region.

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