A Bayesian model for gas saturation estimation using marine seismic AVA and CSEM data

Geophysics ◽  
2007 ◽  
Vol 72 (2) ◽  
pp. WA85-WA95 ◽  
Author(s):  
Jinsong Chen ◽  
G. Michael Hoversten ◽  
Donald Vasco ◽  
Yoram Rubin ◽  
Zhangshuan Hou
Keyword(s):  
Bayesian Model ◽  
Field Data ◽  
Time Window ◽  
Joint Inversion ◽  
Synthetic Data ◽  
Shear Moduli ◽  
Unknown Variable ◽  
Gas Saturation ◽  
Fluid Saturation ◽  
Marine Seismic

We develop a Bayesian model to jointly invert marine seismic amplitude versus angle (AVA) and controlled-source electromagnetic (CSEM) data for a layered reservoir model. We consider the porosity and fluid saturation of each layer in the reservoir, the bulk and shear moduli and density of each layer not in the reservoir, and the electrical conductivity of the overburden and bedrock as random variables. We also consider prestack seismic AVA data in a selected time window as well as real and quadrature components of the recorded electrical field as data. Using Markov chain Monte Carlo (MCMC) sampling methods, wedraw a large number of samples from the joint posterior distribution function. With these samples, we obtain not only the estimates of each unknown variable, but also various types of uncertainty information associated with the estimation. This method is applied to both synthetic and field data to investigate the combined use of seismic AVA and CSEM data for gas saturation estimation. Results show that the method is effective for joint inversion; the incorporation of CSEM data reduces uncertainty in fluid saturation estimation compared to inversion of seismic AVA data alone. The improvement in gas saturation estimation obtained from joint inversion for field data is less significant than for synthetic data because of the large number of unknown noise sources inherent in the field data.

scholarly journals Cross-streamer wavefield reconstruction through wavelet domain learning

Geophysics ◽  
2020 ◽  
Vol 85 (6) ◽  
pp. V457-V471
Author(s):  
Thomas Andre Larsen Greiner ◽  
Volodya Hlebnikov ◽  
Jan Erik Lie ◽  
Odd Kolbjørnsen ◽  
Andreas Kjelsrud Evensen ◽  
...  
Keyword(s):  
Field Data ◽  
Barents Sea ◽  
Interpolation Method ◽  
Synthetic Data ◽  
Seismic Exploration ◽  
Wavelet Domain ◽  
Spatial Aliasing ◽  
Industry Standard ◽  
Marine Source ◽  
Marine Seismic

Seismic exploration in complex geologic settings and shallow geologic targets has led to a demand for higher spatial and temporal resolution in the final migrated image. Conventional marine seismic and wide-azimuth data acquisition lack near-offset coverage, which limits imaging in these settings. A new marine source-over-cable survey, with split-spread configuration, known as TopSeis, was introduced in 2017 to address the shallow-target problem. However, wavefield reconstruction in the near offsets is challenging in the shallow part of the seismic record due to the high temporal frequencies and coarse sampling that leads to severe spatial aliasing. We have investigated deep learning as a tool for the reconstruction problem, beyond spatial aliasing. Our method is based on a convolutional neural network (CNN) approach trained in the wavelet domain that is used to reconstruct the wavefield across the streamers. We determine the performance of the proposed method on broadband synthetic data and TopSeis field data from the Barents Sea. From our synthetic example, we find that the CNN can be learned in the inline direction and applied in the crossline direction, and that the approach preserves the characteristics of the geologic model in the migrated section. In addition, we compare our method to an industry-standard Fourier-based interpolation method, in which the CNN approach shows an improvement in the root-mean-square (rms) error close to a factor of two. In our field data example, we find that the approach reconstructs the wavefield across the streamers in the shot domain, and it displays promising characteristics of a reconstructed 3D wavefield.

scholarly journals Pemodelan Anomali Gravitasi Menggunakan Grablox untuk Identifikasi Injeksi Fluida pada Reservoir

Kappa Journal ◽  
2020 ◽  
Vol 4 (2) ◽  
pp. 240-249
Author(s):  
Muhammad Zuhdi ◽  
◽  
Jannatin Ardhuha ◽  
Kosim Kosim ◽  
Wahyudi Wahyudi ◽  
...  
Keyword(s):  
Field Data ◽  
Oil And Gas ◽  
Synthetic Data ◽  
Reservoir Rock ◽  
Fluid Injection ◽  
Physical Parameters ◽  
Oil Saturation ◽  
Rock Density ◽  
Gas Saturation ◽  
Fluid Infiltration

The 4D microgravity method is a development of the gravity method with the time as the fourth dimension. This research was conducted to find a better way of interpreting the 4D gravity anomaly due to fluid injection around the reservoir. Researchers used GRABLOX for the interpretation of 4D anomalies around the reservoir. The results of the inversion of field data using GRABLOX provide the value of the injection fluid infiltration volume, which shows the distribution of the injection fluid movement on the reservoir. Another physical parameter that can be generated from GRABLOX with a modified value is the reduction in oil and gas saturation due to fluid injection. The inversion results using GRABLOX in the field data indicate a change in reservoir rock density up to 0.28 gr/cc associated with a reduction in gas and oil saturation. The reduction in gas saturation due to the injection fluid has the smallest value of 0% and the largest is up to 66%. The reduction in oil saturation only contributes to a density change of 20% of the reduction in gas saturation. The results of the GRABLOX trial on synthetic data and field data show that both can provide an identification of the movement of the injection fluid in the reservoir, as well as provide other physical parameters, ie. the reduction in oil saturation due to fluid injection.

On the accuracy and efficiency of the joint source scanning algorithm for hydraulic fracturing monitoring

Geophysics ◽  
2018 ◽  
Vol 83 (5) ◽  
pp. KS77-KS85 ◽  
Author(s):  
Yangyang Yu ◽  
Chuntao Liang ◽  
Furong Wu ◽  
Xuben Wang ◽  
Gang Yu ◽  
...  
Keyword(s):  
Field Data ◽  
Joint Inversion ◽  
Synthetic Data ◽  
Computation Time ◽  
Focal Mechanisms ◽  
Inversion Method ◽  
Actual Field ◽  
Microseismic Events ◽  
Reduce Computation Time ◽  
Scanning Algorithm

We have developed the joint source scanning algorithm (JSSA) to determine the locations and focal mechanisms (FMs) of microseismic events simultaneously. However, the computational expense of using JSSA is too high to meet the requirements of real-time monitoring in industrial production. We have developed several scanning schemas to reduce computation time. A multistage scanning schema can significantly improve efficiency while retaining accuracy. For the optimized joint inversion method, a series of tests has been carried out using actual field data and synthetic data to evaluate the accuracy of the method, as well as its dependence on the noise level, source depths, FMs, and other factors. The surface-based arrays better constrain horizontal location errors ([Formula: see text]) and angular errors of P-axes (within 10° for [Formula: see text]). For sources with varying rakes, dips, strikes, and depths, the errors are mostly controlled by the partition of positive and negative polarities in different quadrants. More evenly partitioned polarities in different quadrants yield better results for locations and FMs. Nevertheless, even when some FMs have bad resolutions, the optimized JSSA method can still significantly improve location accuracies.

Attenuation of free-surface multiples by up/down deconvolution for marine towed-streamer data

Geophysics ◽  
2011 ◽  
Vol 76 (6) ◽  
pp. V129-V138 ◽  
Author(s):  
Mariusz Majdański ◽  
Clément Kostov ◽  
Ed Kragh ◽  
Ian Moore ◽  
Mark Thompson ◽  
...  
Keyword(s):  
Free Surface ◽  
Field Data ◽  
Near Field ◽  
Synthetic Data ◽  
Ocean Bottom ◽  
The North ◽  
The North Sea ◽  
Marine Seismic ◽  
Streamer Data ◽  
Multiple Elimination

Free-surface-related multiples in marine seismic data are commonly attenuated using adaptive subtraction of the predicted multiple energy. An alternative method, based on deconvolution of the upgoing wavefield by the downgoing wavefield, was previously applied to ocean-bottom data. We apply the deconvolution method to towed-streamer data acquired in an over/under configuration. We also use direct arrival deconvolution that results in source wavelet designature only, as a benchmark to verify the full multiple deconvolution result. Detailed synthetic data analysis, including sensitivity tests, explains each data processing step and its effects on the final result. We then apply this verified preprocessing sequence to field data from the Kristin area of the North Sea, with a focus on the direct arrival prediction using the near-field hydrophone method. Prestack evaluation of the results shows that the method applied to the field data provides designature, source-side deghosting, and attenuation of multiples. We show comparable stacked results from our method and from 2D iterative surface-related multiple elimination. The workflow has the benefit that it does not require an adaptive subtraction step or iterative application. However, an accurate direct arrival prediction is essential for the successful application of the method. This prediction is obtained using near-field hydrophone measurements that can be recorded with some commercial acquisition systems.

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 Joint inversion of seismic AVO and EM data for gas saturation estimation using a sampling‐based stochastic model

2004 ◽  
Author(s):  
Jinsong Chen ◽  
G. Michael Hoversten ◽  
D. W. Vasco ◽  
Yoram Rubin ◽  
Zhangshuan Hou
Keyword(s):  
Stochastic Model ◽  
Joint Inversion ◽  
Gas Saturation

Quasi-2D inversion of DCR and TDEM data for shallow investigations

Geophysics ◽  
2011 ◽  
Vol 76 (4) ◽  
pp. F239-F250 ◽  
Author(s):  
Fernando A. Monteiro Santos ◽  
Hesham M. El-Kaliouby
Keyword(s):  
Joint Inversion ◽  
Synthetic Data ◽  
Data Sets ◽  
Complex Environments ◽  
Inversion Algorithm ◽  
2D Inversion ◽  
New Approach ◽  
Earth Models ◽  
Time Domain Electromagnetic ◽  
Inversion Techniques

Joint or sequential inversion of direct current resistivity (DCR) and time-domain electromagnetic (TDEM) data commonly are performed for individual soundings assuming layered earth models. DCR and TDEM have different and complementary sensitivity to resistive and conductive structures, making them suitable methods for the application of joint inversion techniques. This potential joint inversion of DCR and TDEM methods has been used by several authors to reduce the ambiguities of the models calculated from each method separately. A new approach for joint inversion of these data sets, based on a laterally constrained algorithm, was found. The method was developed for the interpretation of soundings collected along a line over a 1D or 2D geology. The inversion algorithm was tested on two synthetic data sets, as well as on field data from Saudi Arabia. The results show that the algorithm is efficient and stable in producing quasi-2D models from DCR and TDEM data acquired in relatively complex environments.

scholarly journals Coupled hydrogeophysical parameter estimation using a sequential Bayesian approach

2010 ◽  
Vol 14 (3) ◽  
pp. 545-556 ◽  
Author(s):  
J. Rings ◽  
J. A. Huisman ◽  
H. Vereecken
Keyword(s):  
Water Content ◽  
Particle Filtering ◽  
Field Data ◽  
Synthetic Data ◽  
Time Domain Reflectometry ◽  
Second Step ◽  
Parameter Estimates ◽  
Posterior Distributions ◽  
Data Set ◽  
Resistivity Tomography

Abstract. Coupled hydrogeophysical methods infer hydrological and petrophysical parameters directly from geophysical measurements. Widespread methods do not explicitly recognize uncertainty in parameter estimates. Therefore, we apply a sequential Bayesian framework that provides updates of state, parameters and their uncertainty whenever measurements become available. We have coupled a hydrological and an electrical resistivity tomography (ERT) forward code in a particle filtering framework. First, we analyze a synthetic data set of lysimeter infiltration monitored with ERT. In a second step, we apply the approach to field data measured during an infiltration event on a full-scale dike model. For the synthetic data, the water content distribution and the hydraulic conductivity are accurately estimated after a few time steps. For the field data, hydraulic parameters are successfully estimated from water content measurements made with spatial time domain reflectometry and ERT, and the development of their posterior distributions is shown.

Single-station SVD-based polarization filtering of ground roll: Perfection and investigation of limitations and pitfalls

Geophysics ◽  
2012 ◽  
Vol 77 (2) ◽  
pp. V41-V59 ◽  
Author(s):  
Olena Tiapkina ◽  
Martin Landrø ◽  
Yuriy Tyapkin ◽  
Brian Link
Keyword(s):  
Time Window ◽  
Synthetic Data ◽  
Vertical Component ◽  
Horizontal Component ◽  
Frequency Bandwidth ◽  
Polarization Filter ◽  
Data Set ◽  
Single Station ◽  
Ground Roll ◽  
Polarization Filtering

The advent of single receiver point, multi-component geophones has necessitated that ground roll be removed in the processing flow rather than through acquisition design. A wide class of processing methods for ground-roll elimination is polarization filtering. A number of these methods use singular value decomposition (SVD) or some related transformations. We focus on a single-station SVD-based polarization filter that we consider to be one of the best in the industry. The method is comprised of two stages: (1) ground-roll detection and (2) ground-roll estimation and filtering. To detect the ground roll, a special attribute dependent on the singular values of a three-column matrix formed by a sliding time window is used. The ground roll is approximated and subtracted using the first two eigenimages of this matrix. To limit the possible damage to the signal, the filter operates within the record intervals where the ground roll is detected and within the ground-roll frequency bandwidth only. We improve the ground-roll detector to make it theoretically insensitive to ambient noise and more sensitive to the presence of ground roll. The advantage of the new detector is demonstrated on synthetic and field data sets. We estimate theoretically and with synthetic data the attenuation of the underlying reflections that can be caused by the polarization filter. We show that the underlying signal always loses almost all the energy on the vertical component and on the horizontal component in the ground-roll propagation plane and within the ground-roll frequency bandwidth. The only signal component, if it exists, that can retain a significant part of its energy is the horizontal component orthogonal to the above plane. When 2D 3C field operations are conducted, the signal particle motion can deviate from the ground-roll propagation plane and can therefore retain some of its energy due to a set of offline reflections. In the case of 3D 3C seismic surveys, the reflected signal always deviates from the ground-roll propagation plane on the receiver lines that do not contain the source. This is confirmed with a 2.5D 3C synthetic data set. We discuss when the ability of the filter to effectively subtract the ground roll may, or may not, allow us to ignore the inevitable harm that is done to the underlying reflected waves.

On the reconstruction of the shape of a seismic streamer

Geophysics ◽  
2010 ◽  
Vol 75 (1) ◽  
pp. H1-H6
Author(s):  
Bruno Goutorbe ◽  
Violaine Combier
Keyword(s):  
A Priori ◽  
Synthetic Data ◽  
Seismic Survey ◽  
Random Errors ◽  
Generalized Inversion ◽  
Reconstruction Methods ◽  
Seismic Acquisition ◽  
Marine Seismic ◽  
Gps Devices ◽  
Vessel Reconstruction

In the frame of 3D seismic acquisition, reconstructing the shape of the streamer(s) for each shot is an essential step prior to data processing. Depending on the survey, several kinds of constraints help achieve this purpose: local azimuths given by compasses, absolute positions recorded by global positioning system (GPS) devices, and distances calculated between pairs of acoustic ranging devices. Most reconstruction methods are restricted to work on a particular type of constraint and do not estimate the final uncertainties. The generalized inversion formalism using the least-squares criterion can provide a robust framework to solve such a problem — handling several kinds of constraints together, not requiring an a priori parameterization of the streamer shape, naturally extending to any configuration of streamer(s), and giving rigorous uncertainties. We explicitly derive the equations governing the algorithm corresponding to a marine seismic survey using a single streamer with compasses distributed all along it and GPS devices located on the tail buoy and on the vessel. Reconstruction tests conducted on several synthetic examples show that the algorithm performs well, with a mean error of a few meters in realistic cases. The accuracy logically degrades if higher random errors are added to the synthetic data or if deformations of the streamer occur at a short length scale.

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