Estimation of changes in saturation and pressure from 4D seismic AVO and time-shift analysis

Geophysics ◽  
2011 ◽  
Vol 76 (2) ◽  
pp. C1-C17 ◽  
Author(s):  
Mario Trani ◽  
Rob Arts ◽  
Olwijn Leeuwenburgh ◽  
Jan Brouwer
Keyword(s):  
Seismic Data ◽  
Order Approximation ◽  
Random Noise ◽  
A Priori ◽  
Time Lapse ◽  
Time Shift ◽  
Reliable Estimate ◽  
S Wave ◽  
Fluid Saturation ◽  
Avo Attributes

A reliable estimate of reservoir pressure and fluid saturation changes from time-lapse seismic data is difficult to obtain. Existing methods generally suffer from leakage between the estimated parameters. We propose a new method using different combinations of time-lapse seismic attributes based on four equations: two expressing changes in prestack AVO attributes (zero-offset and gradient reflectivities), and two expressing poststack time-shifts of compressional and shear waves as functions of production-induced changes in fluid properties. The effect of using different approximations of these equations was tested on a realistic, synthetic reservoir, where seismic data have been simulated during the 30-year lifetime of a water-flooded oil reservoir. Results found the importance of the porosity in the inversion with a clear attenuation of the porosity imprint on the final estimates in case the porosity field or the vertically averaged porosity field is known a priori. The use of a first-order approximation of the gradient reflectivity equation leads to severely biased estimates of changes in saturation and leakage between the two different parameters. Both the bias and the leakage can be reduced, if not eliminated, by including higher-order terms in the description of the gradient, or by replacing the gradient equation with P- and/or S-wave time-shift data. The final estimates are relatively robust to random noise, as they present fairly high accuracy in the presence of white noise with a standard deviation of 15%. The introduction of systematic noise decreases the inversion accuracy more severely.

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.

scholarly journals A method of combining coherence-constrained sparse coding and dictionary learning for denoising

Geophysics ◽  
2017 ◽  
Vol 82 (3) ◽  
pp. V137-V148 ◽  
Author(s):  
Pierre Turquais ◽  
Endrias G. Asgedom ◽  
Walter Söllner
Keyword(s):  
Dictionary Learning ◽  
Seismic Data ◽  
Random Noise ◽  
A Priori ◽  
Seismic Signal ◽  
Noise Variance ◽  
A Priori Information ◽  
Seismic Section ◽  
Learned Dictionary ◽  
Priori Information

We have addressed the seismic data denoising problem, in which the noise is random and has an unknown spatiotemporally varying variance. In seismic data processing, random noise is often attenuated using transform-based methods. The success of these methods in denoising depends on the ability of the transform to efficiently describe the signal features in the data. Fixed transforms (e.g., wavelets, curvelets) do not adapt to the data and might fail to efficiently describe complex morphologies in the seismic data. Alternatively, dictionary learning methods adapt to the local morphology of the data and provide state-of-the-art denoising results. However, conventional denoising by dictionary learning requires a priori information on the noise variance, and it encounters difficulties when applied for denoising seismic data in which the noise variance is varying in space or time. We have developed a coherence-constrained dictionary learning (CDL) method for denoising that does not require any a priori information related to the signal or noise. To denoise a given window of a seismic section using CDL, overlapping small 2D patches are extracted and a dictionary of patch-sized signals is trained to learn the elementary features embedded in the seismic signal. For each patch, using the learned dictionary, a sparse optimization problem is solved, and a sparse approximation of the patch is computed to attenuate the random noise. Unlike conventional dictionary learning, the sparsity of the approximation is constrained based on coherence such that it does not need a priori noise variance or signal sparsity information and is still optimal to filter out Gaussian random noise. The denoising performance of the CDL method is validated using synthetic and field data examples, and it is compared with the K-SVD and FX-Decon denoising. We found that CDL gives better denoising results than K-SVD and FX-Decon for removing noise when the variance varies in space or time.

Time-lapse acoustic impedance variations during CO2 injection in Weyburn oilfield, Canada

Geophysics ◽  
2019 ◽  
Vol 85 (1) ◽  
pp. M1-M13 ◽  
Author(s):  
Yichuan Wang ◽  
Igor B. Morozov
Keyword(s):  
Oil Recovery ◽  
Seismic Data ◽  
Acoustic Impedance ◽  
Time Lapse ◽  
Time Shift ◽  
Co2 Injection ◽  
Inversion Method ◽  
Data Sets ◽  
Storage Site ◽  
Data Set

For seismic monitoring injected fluids during enhanced oil recovery or geologic [Formula: see text] sequestration, it is useful to measure time-lapse (TL) variations of acoustic impedance (AI). AI gives direct connections to the mechanical and fluid-related properties of the reservoir or [Formula: see text] storage site; however, evaluation of its subtle TL variations is complicated by the low-frequency and scaling uncertainties of this attribute. We have developed three enhancements of TL AI analysis to resolve these issues. First, following waveform calibration (cross-equalization) of the monitor seismic data sets to the baseline one, the reflectivity difference was evaluated from the attributes measured during the calibration. Second, a robust approach to AI inversion was applied to the baseline data set, based on calibration of the records by using the well-log data and spatially variant stacking and interval velocities derived during seismic data processing. This inversion method is straightforward and does not require subjective selections of parameterization and regularization schemes. Unlike joint or statistical inverse approaches, this method does not require prior models and produces accurate fitting of the observed reflectivity. Third, the TL AI difference is obtained directly from the baseline AI and reflectivity difference but without the uncertainty-prone subtraction of AI volumes from different seismic vintages. The above approaches are applied to TL data sets from the Weyburn [Formula: see text] sequestration project in southern Saskatchewan, Canada. High-quality baseline and TL AI-difference volumes are obtained. TL variations within the reservoir zone are observed in the calibration time-shift, reflectivity-difference, and AI-difference images, which are interpreted as being related to the [Formula: see text] injection.

Fluid-pressure discrimination in anisotropic reservoir rocks — A sensitivity study

Geophysics ◽  
2005 ◽  
Vol 70 (3) ◽  
pp. O1-O11 ◽  
Author(s):  
Alexey Stovas ◽  
Martin Landrø
Keyword(s):  
Pore Pressure ◽  
Seismic Data ◽  
Seismic Anisotropy ◽  
Fluid Pressure ◽  
Rock Physics ◽  
Time Lapse ◽  
Reservoir Rock ◽  
Sand Layer ◽  
Weak Anisotropy ◽  
Fluid Saturation

We investigate how seismic anisotropy influences our ability to distinguish between various production-related effects from time-lapse seismic data. Based on rock physics models and ultrasonic core measurements, we estimate variations in PP and PS reflectivity at the top reservoir interface for fluid saturation and pore pressure changes. The tested scenarios include isotropic shale, weak anisotropic shale, and highly anisotropic shale layers overlaying either an isotropic reservoir sand layer or a weak anisotropic sand layer. We find that, for transverse isotropic media with a vertical symmetry axis (TIV), the effect of weak anisotropy in the cap rock does not lead to significant errors in, for instance, the simultaneous determination of pore-pressure and fluid-saturation changes. On the other hand, changes in seismic anisotropy within the reservoir rock (caused by, for instance, increased fracturing) might be detectable from time-lapse seismic data. A new method using exact expressions for PP and PS reflectivity, including TIV anisotropy, is used to determine pressure and saturation changes over production time. This method is assumed to be more accurate than previous methods.

AVO attribute inversion for finely layered reservoirs

Geophysics ◽  
2006 ◽  
Vol 71 (3) ◽  
pp. C25-C36 ◽  
Author(s):  
Alexey Stovas ◽  
Martin Landrø ◽  
Per Avseth
Keyword(s):  
Seismic Data ◽  
Rock Physics ◽  
Time Lapse ◽  
Oil Saturation ◽  
Seismic Properties ◽  
Seismic Input ◽  
Fluid Properties ◽  
Random Variability ◽  
Net To Gross ◽  
Avo Attributes

Assuming that a turbidite reservoir can be approximated by a stack of thin shale-sand layers, we use standard amplitude variaiton with offset (AVO) attributes to estimate net-to-gross (N/G) and oil saturation. Necessary input is Gassmann rock-physics properties for sand and shale, as well as the fluid properties for hydrocarbons. Required seismic input is AVO intercept and gradient. The method is based upon thin-layer reflectivity modeling. It is shown that random variability in thickness and seismic properties of the thin sand and shale layers does not change significantly the AVO attributes at the top and base of the turbidite-reservoir sequence. The method is tested on seismic data from offshore Brazil. The results show reasonable agreement between estimated and observed N/G and oil saturation. The methodology can be developed further for estimating changes in pay thickness from time-lapse seismic data.

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

Geophysics ◽  
2003 ◽  
Vol 68 (5) ◽  
pp. 1592-1599 ◽  
Author(s):  
Martin Landrø ◽  
Helene Hafslund Veire ◽  
Kenneth Duffaut ◽  
Nazih Najjar
Keyword(s):  
Pore Pressure ◽  
Seismic Data ◽  
Water Saturation ◽  
Synthetic Data ◽  
Time Lapse ◽  
Seismic Survey ◽  
Data Sets ◽  
Amplitude Variation With Offset ◽  
Fluid Saturation ◽  
Pressure Changes

Explicit expressions for computation of saturation and pressure‐related changes from marine multicomponent time‐lapse seismic data are presented. Necessary input is PP and PS stacked data for the baseline seismic survey and the repeat survey. Compared to earlier methods based on PP data only, this method is expected to be more robust since two independent measurements are used in the computation. Due to a lack of real marine multicomponent time‐lapse seismic data sets, the methodology is tested on synthetic data sets, illustrating strengths and weaknesses of the proposed technique. Testing ten scenarios for various changes in pore pressure and fluid saturation, we find that it is more robust for most cases to use the proposed 4D PP/PS technique instead of a 4D PP amplitude variation with offset (AVO) technique. The fit between estimated and “real” changes in water saturation and pore pressure were good for most cases. On the average, we find that the deviation in estimated saturation changes is 8% and 0.3 MPa for the estimated pore pressure changes. For PP AVO, we find that the corresponding average errors are 9% and 1.0 MPa. In the present method, only 4D PP and PS amplitude changes are used in the calculations. It is straightforward to include use of 4D traveltime shifts in the algorithm and, if reliable time shifts can be measured, this will most likely further stabilize the presented method.

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

Geophysics ◽  
2012 ◽  
Vol 77 (6) ◽  
pp. M73-M87 ◽  
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.

scholarly journals Correlation and Calibration of 4D Seismic Vintages for Effective Postproduction Intervention

2021 ◽  
pp. 35-46
Author(s):  
B. T. Ojo ◽  
M. T. Olowokere ◽  
M. I. Oladapo
Keyword(s):  
Seismic Data ◽  
Cross Correlation ◽  
Time Lapse ◽  
Time Shift ◽  
Accurate Analysis ◽  
Hydrocarbon Production ◽  
Matching Process ◽  
Future Production ◽  
Correlation Threshold ◽  
4D Seismic

Poor or low data quality usually has an adverse effect on the quantitative usage of (4D) seismic data for accurate analysis. Repeatability of 4D Seismic or time-lapse survey is considered as a vital tool for effective, potent, and impressive monitoring of productivity of reservoirs. Inconsistencies and disagreement of ‘time-lapse’ data will greatly affect the accuracy and outcome of research when comparing two or more seismic surveys having low repeatability. Correlation is a statistic procedure that measures the linear relation between all points of two variables. Error due to acquisition and processing must be checked for before interpretation in order to minimize exploration failure and the number of dry holes drilled. The seismic data available for this study comprises of 779 crosslines and 494 inlines. The 4D seismic data consisting of the base Seismic shot in 1998 before production and the monitor Seismic shot in 2010 at different stages of hydrocarbon production were cross correlated to ascertain repeatability between the two vintages. A global average matching process was applied while phase and time shift were estimated using the Russell-Liang technique. Two pass full shaping filters were applied for the phase matching. Maximum and minimum ‘cross-correlation’ are 0.85 (85%) and 0.60 (60%) respectively. Statistics of the ‘cross-correlation’ shift show standard deviation  (0.3), variance (0.12), and root mean square (0.78). For high percentage repeatability and maximum correlations, the requested correlation threshold is 0.7 but 1 and 0.99 were obtained for the first and the second matching respectively.  Conclusively, the overall results show that there is high repeatability between the 4D seismic data used and the data can be employed conveniently for accurate ‘time-lapse’ (future) production monitoring and investigation on the field.

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