Numerical simulation of the Biot slow wave in water‐saturated Nivelsteiner Sandstone

Geophysics ◽  
2001 ◽  
Vol 66 (3) ◽  
pp. 890-896 ◽  
Author(s):  
Børge Arntsen ◽  
José M. Carcione
Keyword(s):  
Numerical Simulation ◽  
Wave Velocity ◽  
Slow Wave ◽  
Seismic Data ◽  
Fluid Type ◽  
Seismic Properties ◽  
Reservoir Rocks ◽  
Acoustic Models ◽  
Fluid Saturation ◽  
Water Saturated

The development of rock acoustic models is important for interpreting seismic data. These models should relate seismic properties such as wave velocity and attenuation to the production and lithological properties of reservoir rocks, such as porosity, permeability, fluid type, and fluid saturation.

APPLICATION OF CONTINUOUS VELOCITY LOGS TO DETERMINATION OF FLUID SATURATION OF RESERVOIR ROCKS

Geophysics ◽  
1956 ◽  
Vol 21 (3) ◽  
pp. 739-754 ◽  
Author(s):  
Warren G. Hicks ◽  
James E. Berry
Keyword(s):  
Field Data ◽  
Acoustic Velocity ◽  
Gas Oil ◽  
Laboratory Measurements ◽  
Reservoir Rocks ◽  
Fluid Saturation ◽  
Saturated Sands ◽  
Similar Character ◽  
Water Saturated

Recent studies of continuous acoustic velocity logs indicate that these logs may provide important assistance in differentiating gas, oil, and water saturations in reservoir rocks. In general, velocities are appreciably lower in sands carrying oil or gas than in water‐saturated sands of otherwise similar character. Specific examples from field logs illustrate this application. Laboratory measurements have been made of acoustic velocity of synthetic and natural rocks. Published studies, both empirical and theoretical, of other workers concerned with the transmission of sound in porous media have been considered. All of these at least qualitatively confirm the conclusions drawn from field data.

scholarly journals Identification of hydrocarbons in chalk reservoirs from surface seismic data: South Arne field, North Sea

2005 ◽  
Vol 7 ◽  
pp. 13-16
Author(s):  
Peter Japsen ◽  
Anders Bruun ◽  
Ida L. Fabricius ◽  
Gary Mavko
Keyword(s):  
Wave Velocity ◽  
North Sea ◽  
Seismic Data ◽  
Pore Space ◽  
Sedimentary Rocks ◽  
Rock Physics ◽  
Acoustic Properties ◽  
P Wave ◽  
S Wave ◽  
Reservoir Rocks

Seismic data are mainly used to map out structures in the subsurface, but are also increasingly used to detect differences in porosity and in the fluids that occupy the pore space in sedimentary rocks. Hydrocarbons are generally lighter than brine, and the bulk density and sonic velocity (speed of pressure waves or P-wave velocity) of hydrocarbon-bearing sedimentary rocks are therefore reduced compared to non-reservoir rocks. However, sound is transmitted in different wave forms through the rock, and the shear velocity (speed of shear waves or S-wave velocity) is hardly affected by the density of the pore fluid. In order to detect the presence of hydrocarbons from seismic data, it is thus necessary to investigate how porosity and pore fluids affect the acoustic properties of a sedimentary rock. Much previous research has focused on describing such effects in sandstone (see Mavko et al. 1998), and only in recent years have corresponding studies on the rock physics of chalk appeared (e.g. Walls et al. 1998; Røgen 2002; Fabricius 2003; Gommesen 2003; Japsen et al. 2004). In the North Sea, chalk of the Danian Ekofisk Formation and the Maastrichtian Tor Formation are important reservoir rocks. More information could no doubt be extracted from seismic data if the fundamental physical properties of chalk were better understood. The presence of gas in chalk is known to cause a phase reversal in the seismic signal (Megson 1992), but the presence of oil in chalk has only recently been demonstrated to have an effect on surface seismic data (Japsen et al. 2004). The need for a better link between chalk reservoir parameters and geophysical observations has, however, strongly increased since the discovery of the Halfdan field proved major reserves outside four-way dip closures (Jacobsen et al. 1999; Vejbæk & Kristensen 2000).

Estimation of porosity and fluid saturation in carbonates from rock-physics templates based on seismic Q

Geophysics ◽  
2019 ◽  
Vol 84 (6) ◽  
pp. M25-M36 ◽  
Author(s):  
Mengqiang Pang ◽  
Jing Ba ◽  
José M. Carcione ◽  
Stefano Picotti ◽  
Jian Zhou ◽  
...  
Keyword(s):  
Seismic Data ◽  
Rock Physics ◽  
Clay Content ◽  
Spectral Ratio ◽  
Gas Production ◽  
Ratio Method ◽  
Well Log ◽  
Reservoir Properties ◽  
Seismic Properties ◽  
Fluid Saturation

Rock-physics templates establish a link between seismic properties (e.g., velocity, density, impedance, and attenuation) and reservoir properties such as porosity, fluid saturation, permeability, and clay content. We focus on templates based on attenuation (seismic [Formula: see text] or quality factor), which are highly affected by those properties, and we consider carbonate reservoirs that constitute 60% of the world oil reserves and a potential for additional gas reserves. The seismic properties are described with mesoscopic-loss models, such as the White model of patchy saturation and the double double-porosity model, which include frame and fluid heterogeneities. We have performed ultrasonic experiments, and we estimate the attenuation of the samples and the reservoir by using the spectral ratio method and the improved frequency-shift method. Then, multiscale calibrations of the templates are performed by using laboratory, well log, and seismic data. On this basis, reservoir porosity and fluid saturation are quantitatively evaluated. We first apply the templates to ultrasonic data of limestone using the White model. Then, we consider seismic data of a carbonate gas reservoir of MX work area in the Sichuan Basin, southwest China. A survey line in the area is selected to detect the reservoir by using the templates. The results indicate that the estimated porosity and saturation are consistent with well-log data and actual gas production results. The methodology indicates that the microstructural characteristics of a high-quality reservoir can effectively be predicted using seismic [Formula: see text].

Stochastic inversion of pressure and saturation changes from time-lapse AVO data

Geophysics ◽  
2006 ◽  
Vol 71 (5) ◽  
pp. C81-C92 ◽  
Author(s):  
Helene Hafslund Veire ◽  
Hilde Grude Borgos ◽  
Martin Landrø
Keyword(s):  
Stochastic Model ◽  
Seismic Data ◽  
Synthetic Data ◽  
Time Lapse ◽  
Bayesian Framework ◽  
Reservoir Modeling ◽  
Data Set ◽  
The North ◽  
Fluid Saturation ◽  
Likelihood Model

Effects of pressure and fluid saturation can have the same degree of impact on seismic amplitudes and differential traveltimes in the reservoir interval; thus, they are often inseparable by analysis of a single stacked seismic data set. In such cases, time-lapse AVO analysis offers an opportunity to discriminate between the two effects. We quantify the uncertainty in estimations to utilize information about pressure- and saturation-related changes in reservoir modeling and simulation. One way of analyzing uncertainties is to formulate the problem in a Bayesian framework. Here, the solution of the problem will be represented by a probability density function (PDF), providing estimations of uncertainties as well as direct estimations of the properties. A stochastic model for estimation of pressure and saturation changes from time-lapse seismic AVO data is investigated within a Bayesian framework. Well-known rock physical relationships are used to set up a prior stochastic model. PP reflection coefficient differences are used to establish a likelihood model for linking reservoir variables and time-lapse seismic data. The methodology incorporates correlation between different variables of the model as well as spatial dependencies for each of the variables. In addition, information about possible bottlenecks causing large uncertainties in the estimations can be identified through sensitivity analysis of the system. The method has been tested on 1D synthetic data and on field time-lapse seismic AVO data from the Gullfaks Field in the North Sea.

A reality check on full-wave inversion applied to land seismic data for near-surface modeling

2022 ◽  
Vol 41 (1) ◽  
pp. 40-46
Author(s):  
Öz Yilmaz ◽  
Kai Gao ◽  
Milos Delic ◽  
Jianghai Xia ◽  
Lianjie Huang ◽  
...  
Keyword(s):  
Wave Velocity ◽  
Seismic Data ◽  
Surface Modeling ◽  
P Wave ◽  
Borehole Data ◽  
Near Surface ◽  
Traveltime Tomography ◽  
Wave Inversion ◽  
Full Wave ◽  
Elastic Inversion

We evaluate the performance of traveltime tomography and full-wave inversion (FWI) for near-surface modeling using the data from a shallow seismic field experiment. Eight boreholes up to 20-m depth have been drilled along the seismic line traverse to verify the accuracy of the P-wave velocity-depth model estimated by seismic inversion. The velocity-depth model of the soil column estimated by traveltime tomography is in good agreement with the borehole data. We used the traveltime tomography model as an initial model and performed FWI. Full-wave acoustic and elastic inversions, however, have failed to converge to a velocity-depth model that desirably should be a high-resolution version of the model estimated by traveltime tomography. Moreover, there are significant discrepancies between the estimated models and the borehole data. It is understandable why full-wave acoustic inversion would fail — land seismic data inherently are elastic wavefields. The question is: Why does full-wave elastic inversion also fail? The strategy to prevent full-wave elastic inversion of vertical-component geophone data trapped in a local minimum that results in a physically implausible near-surface model may be cascaded inversion. Specifically, we perform traveltime tomography to estimate a P-wave velocity-depth model for the near-surface and Rayleigh-wave inversion to estimate an S-wave velocity-depth model for the near-surface, then use the resulting pairs of models as the initial models for the subsequent full-wave elastic inversion. Nonetheless, as demonstrated by the field data example here, the elastic-wave inversion yields a near-surface solution that still is not in agreement with the borehole data. Here, we investigate the limitations of FWI applied to land seismic data for near-surface modeling.

Using Seismic Data to Predict Shale Pore Pressure and Overpressure Sweet Spots: A Case Study from the Lower Silurian Longmaxi Formation in Sichuan Basin, China

2021 ◽  
Author(s):  
Sheng Chen ◽  
Qingcai Zeng ◽  
Xiujiao Wang ◽  
Qing Yang ◽  
Chunmeng Dai ◽  
...  
Keyword(s):  
Prediction Model ◽  
Wave Velocity ◽  
Shale Gas ◽  
Seismic Data ◽  
P Wave ◽  
Formation Pressure ◽  
S Wave ◽  
P Wave Velocity ◽  
New Formation ◽  
Sweet Spots

Abstract Practices of marine shale gas exploration and development in south China have proved that formation overpressure is the main controlling factor of shale gas enrichment and an indicator of good preservation condition. Accurate prediction of formation pressure before drilling is necessary for drilling safety and important for sweet spots predicting and horizontal wells deploying. However, the existing prediction methods of formation pore pressures all have defects, the prediction accuracy unsatisfactory for shale gas development. By means of rock mechanics analysis and related formulas, we derived a formula for calculating formation pore pressures. Through regional rock physical analysis, we determined and optimized the relevant parameters in the formula, and established a new formation pressure prediction model considering P-wave velocity, S-wave velocity and density. Based on regional exploration wells and 3D seismic data, we carried out pre-stack seismic inversion to obtain high-precision P-wave velocity, S-wave velocity and density data volumes. We utilized the new formation pressure prediction model to predict the pressure and the spatial distribution of overpressure sweet spots. Then, we applied the measured pressure data of three new wells to verify the predicted formation pressure by seismic data. The result shows that the new method has a higher accuracy. This method is qualified for safe drilling and prediction of overpressure sweet spots for shale gas development, so it is worthy of promotion.

scholarly journals Discussion on the Theoretical Basis for Cross-Over Method Applied to Downhole Wave Velocity Test

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Qing Dong ◽  
Zheng-hua Zhou ◽  
Su Jie ◽  
Bing Hao ◽  
Yuan-dong Li
Keyword(s):  
Numerical Simulation ◽  
Numerical Simulations ◽  
Wave Velocity ◽  
Theoretical Basis ◽  
Influence Factors ◽  
P Wave ◽  
Engineering Practice ◽  
S Wave ◽  
Simulation Results ◽  
The Cross

At engineering practice, the theoretical basis for the cross-over method, used to obtain shear wave arrival time in the downhole method of the wave velocity test by surface forward and backward strike, is that the polarity of P-wave keeps the same, while the polarity of S-wave transforms when the direction of strike inverted. However, the characteristics of signals recorded in tests are often found to conflict with this theoretical basis for the cross-over method, namely, the polarity of the P-wave also transforms under the action of surface forward and backward strike. Therefore, 3D finite element numerical simulations were conducted to study the validity of the theoretical basis for the cross-over method. The results show that both shear and compression waves are observed to be in 180° phase difference between horizontal signal traces, consistent with the direction of excitation generated by reversed impulse. Furthermore, numerical simulation results prove to be reliable by the analytic solution; it shows that the theoretical basis for the cross-over method applied to the downhole wave velocity test is improper. In meanwhile, numerical simulations reveal the factors (inclining excitation, geophone deflection, inclination, and background noise) that may cause the polarity of the P-wave not to reverse under surface forward and backward strike. Then, as to reduce the influence factors, we propose a method for the downhole wave velocity test under surface strike, the time difference of arrival is based between source peak and response peak, and numerical simulation results show that the S-wave velocity by this method is close to the theoretical S-wave velocity of soil.

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