Modeling velocity changes associated with a miscible flood in the Prudhoe Bay Field

The Prudhoe Bay Field, Alaska, is produced by a number of recovery processes. A miscible gas (MI) injection pilot was studied to see if repeated seismic surveys could detect the progress of the MI gas. Gassmann's equation was used on the injection, producing and monitor wells where a detailed reservoir simulation was available. Time‐varying saturations of the three fluid phases and the pressure were used to calculate the expected velocity of the reservoir at different stages of the injection. The differences between the modeled velocities at two extremes of gas saturation after the water‐after‐gas (WAG) range up to 500 ft/s (150 m/s). It was concluded that it have been possible to detect the fluid saturation had a baseline survey been collected early in the field's development. Unfortunately, initial production introduced 2% gas into the fluid, muting later attempts to map changes in saturation that varied from between 30% and 60%. Additionally, the use of a WAG process further complicated the gas mapping by both increasing and decreasing the reservoir fluid velocities. Collecting new seismic data over this pilot was not recommended. The modeling exercise highlighted a number of issues that are important in monitoring other reservoirs. Amongst these are the timing of data collection and the weakness of the petrophysical models caused by the numerous assumptions that are required in the absence of field observations. It was demostrated that modeling exercises can both save unnecessary field expenses and provide considerable insight in reservoir behavior.

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Discrimination between pressure and fluid saturation changes from time‐lapse seismic data

Geophysics ◽

10.1190/1.1444973 ◽

2001 ◽

Vol 66 (3) ◽

pp. 836-844 ◽

Cited By ~ 263

Author(s):

Martin Landrø

Keyword(s):

Seismic Data ◽

Time Lapse ◽

Seismic Survey ◽

Baseline Survey ◽

Water Contact ◽

Data Set ◽

Seismic Surveys ◽

Fluid Saturation ◽

Oil Water ◽

Pressure Changes

Explicit expressions for computing saturation‐ and pressure‐related changes from time‐lapse seismic data have been derived and tested on a real time‐lapse seismic data set. Necessary input is near‐and far‐offset stacks for the baseline seismic survey and the repeat survey. The method has been tested successfully in a segment where pressure measurements in two wells verify a pore‐pressure increase of 5 to 6 MPa between the baseline survey and the monitor survey. Estimated pressure changes using the proposed relationships fit very well with observations. Between the baseline and monitor seismic surveys, 27% of the estimated recoverable hydrocarbon reserves were produced from this segment. The estimated saturation changes also agree well with observed changes, apart from some areas in the water zone that are mapped as being exposed to saturation changes (which is unlikely). Saturation changes in other segments close to the original oil‐water contact and the top reservoir interface are also estimated and confirmed by observations in various wells.

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Gassmann's equation and fluid‐saturation effects on seismic velocities

Geophysics ◽

10.1190/1.1707059 ◽

2004 ◽

Vol 69 (2) ◽

pp. 398-405 ◽

Cited By ~ 143

Author(s):

De‐hua Han ◽

Michael L. Batzle

Keyword(s):

Upper And Lower Bounds ◽

Fluid Substitution ◽

Seismic Velocities ◽

Critical Porosity ◽

Fluid Saturation ◽

Gassmann’S Equation ◽

Physical Insight ◽

Velocity Changes ◽

Saturation Effects ◽

Insight Into

Gassmann's (1951) equations commonly are used to predict velocity changes resulting from different pore‐fluid saturations. However, the input parameters are often crudely estimated, and the resulting estimates of fluid effects can be unrealistic. In rocks, parameters such as porosity, density, and velocity are not independent, and values must be kept consistent and constrained. Otherwise, estimating fluid substitution can result in substantial errors. We recast the Gassmann's relations in terms of a porosity‐dependent normalized modulus Kn and the fluid sensitivity in terms of a simplified gain function G. General Voigt‐Reuss bounds and critical porosity limits constrain the equations and provide upper and lower bounds of the fluid‐saturation effect on bulk modulus. The “D” functions are simplified modulus‐porosity relations that are based on empirical porosity‐velocity trends. These functions are applicable to fluid‐substitution calculations and add important constraints on the results. More importantly, the simplified Gassmann's relations provide better physical insight into the significance of each parameter. The estimated moduli remain physical, the calculations are more stable, and the results are more realistic.

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Streamline-based integration of time-lapse seismic and production data into petroleum reservoir models

Geophysics ◽

10.1190/geo2011-0346.1 ◽

2012 ◽

Vol 77 (6) ◽

pp. M73-M87 ◽

Cited By ~ 10

Author(s):

Alvaro Rey ◽

Eric Bhark ◽

Kai Gao ◽

Akhil Datta-Gupta ◽

Richard Gibson

Keyword(s):

High Resolution ◽

Finite Difference ◽

Seismic Data ◽

Time Lapse ◽

Seismic Inversion ◽

Absolute Permeability ◽

Production Data ◽

Petroleum Reservoir ◽

Seismic Surveys ◽

Fluid Saturation

We have developed an efficient approach of petroleum reservoir model calibration that integrates 4D seismic surveys together with well-production data. The approach is particularly well-suited for the calibration of high-resolution reservoir properties (permeability) because the field-scale seismic data are areally dense, whereas the production data are effectively averaged over interwell spacing. The joint calibration procedure is performed using streamline-based sensitivities derived from finite-difference flow simulation. The inverted seismic data (i.e., changes in elastic impedance or fluid saturations) are distributed as a 3D high-resolution grid cell property. The sensitivities of the seismic and production surveillance data to perturbations in absolute permeability at individual grid cells are efficiently computed via semianalytical streamline techniques. We generalize previous formulations of streamline-based seismic inversion to incorporate realistic field situations such as changing boundary conditions due to infill drilling, pattern conversion, etc. A commercial finite-difference flow simulator is used for reservoir simulation and to generate the time-dependent velocity fields through which streamlines are traced and the sensitivity coefficients are computed. The commercial simulator allows us to incorporate detailed physical processes including compressibility and nonconvective forces, e.g., capillary pressure effects, while the streamline trajectories provide a rapid evaluation of the sensitivities. The efficacy of our proposed approach was tested with synthetic and field applications. The synthetic example was the Society of Petroleum Engineers benchmark Brugge field case. The field example involves waterflooding of a North Sea reservoir with multiple seismic surveys. In both cases, the advantages of incorporating the time-lapse variations were clearly demonstrated through improved estimation of the permeability heterogeneity, fluid saturation evolution, and swept and drained volumes. The value of the seismic data integration was in particular proven through the identification of the continuity in reservoir sands and barriers, and by the preservation of geologic realism in the calibrated model.

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Seismic surveys at an artificially created field-test cavern within a salt pillar

10.5194/egusphere-egu2020-6843 ◽

2020 ◽

Author(s):

Heike Richter ◽

Rüdiger Giese ◽

Axel Zirkler ◽

Bettina Strauch

Keyword(s):

Field Test ◽

Seismic Data ◽

Field Experiments ◽

Experimental Simulation ◽

Seismic Survey ◽

Baseline Survey ◽

Multiphase System ◽

Rock Interaction ◽

Seismic Surveys ◽

Salt Rocks

Salt rocks serve as host rock for technical caverns due to their impermeability but their can also be influenced by fluid migration due to geological fracture zones. Seismic methods can be used to monitor cavernous structures in the transition zone between cavity and undisturbed salt rocks. Around an artificially created cavity (field-test cavern) in a salt pillar with a volume of approximately 100 litre, travel time tomography was utilized to image structures related to caverns and fluid-storage. Seismic surveys were performed at different stages of an experimental simulation of gas-water-rock interaction in the field-test cavern aiming for a better understanding of the multiphase system in the cavern-near area. The baseline survey (1) was carried out using 8 three-component piezo-electrical sensor rods and a seismic vibrator source at the surface of the salt pillar, first without an installed field-test cavern. After drilling and installing the field-test cavern, seismic cross-hole measurements were performed after producing partial vacuum in the test cavern (2) and infill of gas (3) and water (4). To finalize the field experiments the last seismic survey (5) was again conducted at the surface of the salt pillar as a repeat measurement to the baseline survey. The seismic monitoring of the salt pillar was carried out in a frequency range of 100 Hz to 14000 Hz allowing a spatial resolution in the cm-range. This was followed by pre-processing of the seismic data sets to apply the picked travel times in a tomography program. On the basis of the tomography results and reflection seismic data we want to assess the potential enlargement of the field-test cavern due to water-infill and to image the differences between unaffected salt rocks, cavernous structures and developing transition zones.

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

Geophysics ◽

10.1190/1.2235858 ◽

2006 ◽

Vol 71 (5) ◽

pp. C81-C92 ◽

Cited By ~ 17

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.

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Methods of spectral analysis of seismic data

Bulletin of the Seismological Society of America ◽

10.1785/bssa0540041213 ◽

1964 ◽

Vol 54 (4) ◽

pp. 1213-1232

Author(s):

I. K. McIvor

Keyword(s):

Spectral Analysis ◽

Fourier Analysis ◽

Seismic Data ◽

Seismic Event ◽

Time Varying

Abstract Three different methods of spectral analysis are compared on the basis of a common interpretation in terms of time-varying Fourier analysis. The spectra obtained by these methods for a particular seismic event are given and differences in the results are resolved.

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Research on the Influence Law and Suppression Methods of Seismic Wavelet Time-varying Nature on Seismic Data Processing

Proceedings of the 2015 International Conference on Computer Science and Intelligent Communication ◽

10.2991/csic-15.2015.21 ◽

2015 ◽

Author(s):

Peng Zhang ◽

Yongshou Dai ◽

Manman Zhang ◽

Rongrong Wang

Keyword(s):

Data Processing ◽

Seismic Data ◽

Time Varying ◽

Seismic Data Processing ◽

Seismic Wavelet

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Time-Lapse Monitoring of Daily Velocity Changes in Binchuan, Southwestern China, Using Large-Volume Air-Gun Source Array Data

Seismological Research Letters ◽

10.1785/0220210160 ◽

2022 ◽

Author(s):

Yi Luan ◽

Hongfeng Yang ◽

Baoshan Wang ◽

Wei Yang ◽

Weitao Wang ◽

...

Keyword(s):

Large Volume ◽

Seismic Data ◽

Temporal Changes ◽

Air Pressure ◽

Southwestern China ◽

Long Period ◽

Air Gun ◽

Velocity Variations ◽

Velocity Changes ◽

Source Array

Abstract Temporal changes of seismic velocities in the Earth’s crust can be induced by stress perturbations or material damage from reasons such as strong ground motion, volcanic activities, and atmospheric effects. However, monitoring the temporal changes remains challenging, because most of them generally exist in small travel-time differences of seismic data. Here, we present an excellent case of daily variations of the subsurface structure detected using a large-volume air-gun source array of one-month experiment in Binchuan, Yunnan, southwestern China. The seismic data were recorded by 12 stations within ∼10 km away from the source and used to detect velocity change in the crust using the deconvolution method and sliding window cross-correlation method, which can eliminate the “intercept” error when cutting the air-gun signals and get the real subsurface variations. Furthermore, the multichannel singular spectral analysis method is used to separate the daily change (∼1 cycle per day) from the “long-period” change (<1 cycle per day) or noise. The result suggests that the daily velocity changes at the two nearest stations, 53277 (offset ∼700 m) and 53278 (offset ∼2.3 km), are well correlated with air temperature variation with a time lag of 5.0 ± 1.5 hr, which reflects that the velocity variations at the subsurface are likely attributed to thermoelastic strain. In contrast, both daily and long-period velocity changes at distant stations correlate better with the varying air pressure than the temperature, indicating that the velocity variations at deeper depth are dominated by the elastic loading of air pressure. Our results demonstrate that the air-gun source is a powerful tool to detect the velocity variation of the shallow crust media.

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Machine Learning analysis of seismic signals recorded at Stromboli Volcano

10.5194/egusphere-egu21-13166 ◽

2021 ◽

Author(s):

Darius Fenner ◽

Georg Rümpker ◽

Horst Stöcker ◽

Megha Chakraborty ◽

Wei Li ◽

...

Keyword(s):

Machine Learning ◽

Seismic Data ◽

Noise Level ◽

Volcanic Eruptions ◽

Time Varying ◽

Seismic Signals ◽

Machine Learning Model ◽

Automatic Adaptation ◽

Consistent Manner ◽

Recent Developments

At Stromboli, minor volcanic eruptions occur at time intervals of approximately five minutes on average, making it one of the most active volcanoes worldwide. In addition to these mostly harmless events, there are also stronger eruptions and paroxysms which pose a serious threat to residents and tourists. In light of recent developments in Machine Learning, this study attempts to apply these new tools for the analysis of the time-varying volcanic eruptions at Stromboli. As input for the Machine-Learning approach, we use continuous recordings of seismic signals from two seismometers on the island. The data is available from IRIS &#160;and includes records starting in 2012 up to the present.&#160;One primary challenge is to label and classify the data, i.e., to discriminate events of interest from noise. The variety of signal-appearance in the recorded data is wide, in some periods the events are clearly distinguishable from noise whereas, in other cases relevant events are obscured by the high noise level. To enable the event-detection in all cases, we developed the following algorithm: in the first step, the seismic data is pre-processed with an STA/LTA-Filter, which allows detection of events based on a prominence threshold. However, due to the diversity of signal patterns, a fixed set of hyperparameters (STA- and LTA-window length, prominence threshold, correlation coefficient) fails to reliably extract the relevant events in a consistent manner. Therefore, the (time-varying) noise level of the recordings is used as an additional key indicator. After this, the hyperparameters are optimized. The automatic adaptation is then used for labeling the continuous seismic data.After extracting the events based on this approach, a machine learning model is trained to analyze the recordings for possible patterns in the interval times and the event amplitudes. This study is expected to provide constraints on the possibility to detect complex time-dependent patterns of the eruption history at Stromboli.

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