scholarly journals Fast probabilistic petrophysical mapping of reservoirs from 3D seismic data

Geophysics ◽  
2012 ◽  
Vol 77 (3) ◽  
pp. O1-O19 ◽  
Author(s):  
Mohammad S. Shahraeeni ◽  
Andrew Curtis ◽  
Gabriel Chao
Keyword(s):  
Seismic Data ◽  
Water Saturation ◽  
Random Noise ◽  
Clay Content ◽  
Inversion Method ◽  
Model Parameters ◽  
Nonlinear Inversion ◽  
Desktop Computer ◽  
Data Set ◽  
Mixture Density

A fast probabilistic inversion method for 3D petrophysical property prediction from inverted prestack seismic data has been developed and tested on a real data set. The inversion objective is to estimate the joint probability density function (PDF) of model vectors consisting of porosity, clay content, and water saturation components at each point in the reservoir, from data vectors with compressional- and shear-wave-impedance components that are obtained from the inversion of seismic data. The proposed inversion method is based on mixture density network (MDN), which is trained by a given set of training samples, and provides an estimate of the joint posterior PDF’s of the model parameters for any given data point. This method is much more time and memory efficient than conventional nonlinear inversion methods. The training data set is constructed using nonlinear petrophysical forward relations and includes different sources of uncertainty in the inverse problem such as variations in effective pressure, bulk modulus and density of hydrocarbon, and random noise in recorded data. Results showed that the standard deviations of all model parameters were reduced after inversion, which shows that the inversion process provides information about all parameters. The reduction of uncertainty in water saturation was smaller than that for porosity or clay content; nevertheless the maximum of the a posteriori (MAP) of model PDF clearly showed the boundary between brine saturated and oil saturated rocks at wellbores. The MAP estimates of different model parameters show the lateral and vertical continuity of these boundaries. Errors in the MAP estimate of different model parameters can be reduced using more accurate petrophysical forward relations. This fast, probabilistic, nonlinear inversion method can be applied to invert large seismic cubes for petrophysical parameters on a standard desktop computer.

Integrated reservoir characterization of a Utica Shale field

2018 ◽  
Vol 37 (9) ◽  
pp. 656-661
Author(s):  
Jinming Zhu
Keyword(s):  
Seismic Data ◽  
Reservoir Characterization ◽  
Water Saturation ◽  
Clay Content ◽  
Seismic Inversion ◽  
Data Set ◽  
Utica Shale ◽  
Multidisciplinary Study ◽  
3D Seismic Data

We performed an integrated multidisciplinary study for reservoir characterization of a Utica Shale field in eastern Ohio covered by a multiclient 3D seismic data set acquired in 2015. Elastic seismic inversion was performed in-house for effective reservoir characterization of the Utica Shale, which covers the interval from the top of Upper Utica (UUTIC) to the top of Trenton Limestone. Accurate, high-fidelity inversion results were obtained, including acoustic impedance, shear impedance, density, and VP/VS. These consistent inversion results allow for the reliable calculation of geomechanical and petrophysical properties of the reservoir. The inverted density clearly divides the Point Pleasant (PPLS) interval as low density from the overlying UUTIC Shale interval. Both Poisson's ratio (PR) and brittleness unmistakably separate the underlying PPLS from the overlying Utica interval. The PPLS Formation is easier to hydraulically fracture due to its much lower PR. Sequence S4 is the best due to its higher Young's modulus to sustain the open fractures. The calculated petrophysical volumes indisputably delineate the traditional Utica Shale into two distinctive sections. The upper section, the UUTIC, can be described as having 1%–2% total organic carbon (TOC), 3.5%–4.8% porosity, 10%–24% water saturation, and 40%–58% clay content. The lower section, PPLS, can be described as having 3%–4.5% TOC, 5%–9% porosity, 2%–10% water saturation, and about 15%–35% clay content. Both sections exhibit spatial variation of the properties. Nevertheless, the underlying PPLS is obviously a significantly better reservoir and operationally easier to produce.

Exploiting surface consistency for surface-wave characterization and mitigation — Part 2: Application to 3D data

Geophysics ◽  
2017 ◽  
Vol 82 (1) ◽  
pp. V39-V50 ◽  
Author(s):  
Christine E. Krohn ◽  
Partha S. Routh
Keyword(s):  
Surface Wave ◽  
Low Frequency ◽  
Single Mode ◽  
Complex Surface ◽  
Surface Elevation ◽  
Inversion Method ◽  
Model Parameters ◽  
Nonlinear Inversion ◽  
Data Set ◽  
Near Surface

We present a case history demonstrating the 3D implementation of the surface-wave impulse estimation and removal (SWIPER) method. SWIPER is a tomographic inversion method that is able to predict and remove complex surface waves, which are multimodal and heterogeneous. The inversion generates surface-consistent model parameters, which correlate with near-surface elevation. These parameters include a surface map of the propagation velocity and attenuation values for each surface-wave mode as a function of frequency. The method also determines variations in source coupling as a function of frequency, which also correlate with the near-surface elevation changes. We show that the method works equally well with a fully sampled and decimated 3D dynamite-sourced data set. We start with a linear single-mode inversion and use the results to generate the starting model for a subsequent three-mode nonlinear inversion. The resulting velocity-dispersion grid has greater lateral resolution and extends to higher frequencies than that generated by a conventional array beam forming method. The propagation and source coupling parameters can be used together to predict the surface-wave waveforms, which are then adaptively subtracted from the data on a trace-to-trace basis. We demonstrate with decimated data that low-frequency reflections can be preserved, even when the data are highly aliased and would be removed by traditional multichannel filters.

Bayesian linearized petrophysical AVO inversion

Geophysics ◽  
2018 ◽  
Vol 83 (3) ◽  
pp. M1-M13 ◽  
Author(s):  
Xiaozheng Lang ◽  
Dario Grana
Keyword(s):  
Seismic Data ◽  
Elastic Moduli ◽  
Water Saturation ◽  
Forward Model ◽  
Inversion Method ◽  
Data Set ◽  
Critical Porosity ◽  
Explicit Analytical Solution ◽  
Fluid Properties ◽  
Model Linearization

Seismic reservoir characterization aims to provide a 3D model of rock and fluid properties based on measured seismic data. Petrophysical properties, such as porosity, mineral volumes, and water saturation, are related to elastic properties, such as velocity and impedance, through a rock-physics model. Elastic attributes can be obtained from seismic data through seismic modeling. Estimation of the properties of interest is an inverse problem; however, if the forward model is nonlinear, computationally demanding inversion algorithms should be adopted. We have developed a linearized forward model, based on a convolutional model and a new amplitude variation with offset approximation that combined Gray’s linearization of the reflectivity coefficients with Gassmann’s equation and Nur’s critical porosity model. Physical relations between the saturated elastic moduli and the matrix elastic moduli, fluid bulk modulus, and porosity are almost linear, and the model linearization can be obtained by computing the first-order Taylor series approximation. The inversion method for the estimation of the reservoir properties of interest is then developed in the Bayesian framework. If we assume that the distributions of the prior model and error term are Gaussian, then the explicit analytical solution of the posterior distribution of rock and fluid properties can be analytically derived. Our method has first been validated on synthetic seismic data and then applied to a 2D seismic section extracted from a real data set acquired in the Norwegian Sea.

Stochastic nonlinear inversion of seismic data for the estimation of petroelastic properties using the ensemble smoother and data reparameterization

Geophysics ◽  
2018 ◽  
Vol 83 (3) ◽  
pp. M25-M39 ◽  
Author(s):  
Mingliang Liu ◽  
Dario Grana
Keyword(s):  
Seismic Data ◽  
Reservoir Characterization ◽  
Computational Cost ◽  
Rock Physics ◽  
Inversion Method ◽  
Nonlinear Inversion ◽  
Seismic Velocities ◽  
Reservoir Properties ◽  
Data Set ◽  
Ensemble Smoother

We have developed a new stochastic nonlinear inversion method for seismic reservoir characterization studies to jointly estimate elastic and petrophysical properties and to quantify their uncertainty. Our method aims to estimate multiple reservoir realizations of the entire set of reservoir properties, including seismic velocities, density, porosity, mineralogy, and saturation, by iteratively updating the initial ensemble of models based on the mismatch between their seismic response and the measured seismic data. The initial models are generated using geostatistical methods and the geophysical forward operators include rock-physics relations and a seismic forward model. The optimization is achieved using an iterative ensemble-based algorithm, namely, the ensemble smoother with multiple data assimilation, in which each iteration is based on a Bayesian updating step. The advantages of the proposed method are that it can be applied to nonlinear inverse problems and it can provide an ensemble of solutions from which we can quantify the uncertainty of the model properties of interest. To reduce the computational cost of the inversion, we perform the optimization in a lower dimensional data space reparameterized by singular value decomposition. The proposed methodology is validated on a synthetic case in which the set of petroelastic properties is recovered with satisfactory accuracy. Then, we applied the inversion method to a real seismic data set from the Norne field in the Norwegian Sea.

Seismic resolution enhancement in shale-oil reservoirs

Geophysics ◽  
2018 ◽  
Vol 83 (5) ◽  
pp. B281-B287 ◽  
Author(s):  
Xiwu Liu ◽  
Fengxia Gao ◽  
Yuanyin Zhang ◽  
Ying Rao ◽  
Yanghua Wang
Keyword(s):  
Seismic Data ◽  
Resolution Enhancement ◽  
Oil Reservoirs ◽  
Inversion Method ◽  
Shale Oil ◽  
Data Set ◽  
Extension Method ◽  
Seismic Resolution ◽  
Full Band ◽  
Shale Oil Reservoirs

We developed a case study of seismic resolution enhancement for shale-oil reservoirs in the Q Depression, China, featured by rhythmic bedding. We proposed an innovative method for resolution enhancement, called the full-band extension method. We implemented this method in three consecutive steps: wavelet extraction, filter construction, and data filtering. First, we extracted a constant-phase wavelet from the entire seismic data set. Then, we constructed the full-band extension filter in the frequency domain using the least-squares inversion method. Finally, we applied the band extension filter to the entire seismic data set. We determined that this full-band extension method, with a stretched frequency band from 7–70 to 2–90 Hz, may significantly enhance 3D seismic resolution and distinguish reflection events of rhythmite groups in shale-oil reservoirs.

A New Inversion Method Using a Modified Bat Algorithm for Analysis of Seismic Refraction Data in Dam Site Investigation

2019 ◽  
Vol 24 (2) ◽  
pp. 201-214
Author(s):  
Rashed Poormirzaee ◽  
Siamak Sarmady ◽  
Yusuf Sharghi
Keyword(s):  
Seismic Data ◽  
Seismic Refraction ◽  
Bat Algorithm ◽  
Inversion Method ◽  
Model Parameters ◽  
Inversion Algorithm ◽  
Dam Site ◽  
Seismic Refraction Data ◽  
Modified Bat Algorithm ◽  
Refraction Data

Similar to any other geophysical method, seismic refraction method faces non-uniqueness in the estimation of model parameters. Recently, different nonlinear seismic processing techniques have been introduced, particularly for seismic inversion. One of the recently developed metaheuristic algorithms is bat optimization algorithm (BA). Standard BA is usually quick at the exploitation of the solution, while its exploration ability is relatively poor. In order to improve exploration ability of BA, in the current study, a hybrid metaheuristic algorithm by inclusion a mutation operator into BA, so-called mutation based bat algorithm (MBA), is introduced to inversion of seismic refraction data. The efficiency and stability of the proposed inversion algorithm were tested on different synthetic cases. Finally, the MBA inversion algorithm was applied to a real dataset acquired from Leylanchay dam site at East-Azerbaijan province, Iran, to determine alluvium depth. Then, the performance of MBA on both synthetic and real datasets was compared with standard BA. Moreover, the dataset was further processed following a tomographic approach and the results were compared to the results of the proposed MBA inversion method. In general, the MBA inversion results were superior to standard BA inversion and results of MBA were in good agreement with available boreholes data and geological sections at the dam site. The analysis of the seismic data showed that the studied site comprises three distinct layers: a saturated alluvial, an unsaturated alluvial, and a dolomite bedrock. The measured seismic velocity across the dam site has a range of 400 to 3,500 m/s, with alluvium thickness ranging from 5 to 19 m. Findings showed that the proposed metaheuristic inversion framework is a simple, fast, and powerful tool for seismic data processing.

Nonlinear velocity inversion by a two‐step Monte Carlo method.

Geophysics ◽  
1994 ◽  
Vol 59 (4) ◽  
pp. 577-590 ◽  
Author(s):  
Side Jin ◽  
Raul Madariaga
Keyword(s):  
Monte Carlo ◽  
Velocity Model ◽  
Small Scale ◽  
Inversion Method ◽  
Nonlinear Inversion ◽  
Ray Theory ◽  
Data Set ◽  
Seismic Reflection Data ◽  
Velocity Models ◽  
Reference Velocity

Seismic reflection data contain information on small‐scale impedance variations and a smooth reference velocity model. Given a reference velocity model, the reflectors can be obtained by linearized migration‐inversion. If the reference velocity is incorrect, the reflectors obtained by inverting different subsets of the data will be incoherent. We propose to use the coherency of these images to invert for the background velocity distribution. We have developed a two‐step iterative inversion method in which we separate the retrieval of small‐scale variations of the seismic velocity from the longer‐period reference velocity model. Given an initial background velocity model, we use a waveform misfit‐functional for the inversion of small‐scale velocity variations. For this linear step we use the linearized migration‐inversion method based on ray theory that we have recently developed with Lambaré and Virieux. The reference velocity model is then updated by a Monte Carlo inversion method. For the nonlinear inversion of the velocity background, we introduce an objective functional that measures the coherency of the short wavelength components obtained by inverting different common shot gathers at the same locations. The nonlinear functional is calculated directly in migrated data space to avoid expensive numerical forward modeling by finite differences or ray theory. Our method is somewhat similar to an iterative migration velocity analysis, but we do an automatic search for relatively large‐scale 1-D reference velocity models. We apply the nonlinear inversion method to a marine data set from the North Sea and also show that nonlinear inversion can be applied to realistic scale data sets to obtain a laterally heterogeneous velocity model with a reasonable amount of computer time.

MATLAB code for data-driven initial model of 1D Schlumberger sounding curve

Geophysics ◽  
2018 ◽  
Vol 83 (2) ◽  
pp. F21-F28 ◽  
Author(s):  
Jide Nosakare Ogunbo
Keyword(s):  
Random Noise ◽  
Electrode Array ◽  
Automatic Process ◽  
Model Parameters ◽  
Curve Matching ◽  
Nonlinear Inversion ◽  
Array Configuration ◽  
Matlab Code ◽  
Resistivity Model ◽  
Schlumberger Sounding

A MATLAB code automatically performs partial curve matching of 1D apparent resistivity data recorded with the Schlumberger electrode array configuration. The two-layer master and auxiliary curves are used to systematically match through the branches of data extracting the corresponding model properties. Partial curve matching is a classical interpretation procedure of the sounding curve, which has been done manually. Results from the manual and automatic procedures are compared. The matched geoelectric models from the automatic process are retrieved more quickly, and these results are consistent because the process is digitalized and are not dependent on human numerical accuracy judgment. Magnitudes of random noise affect the final matched model parameters, yet these values are sufficient to be initial models for subsequent nonlinear inversion. It is hoped that for an inversion workflow, the code can be included to automatically find an initial resistivity model.

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.

Simultaneous inversion of PP and PS seismic data

Geophysics ◽  
2006 ◽  
Vol 71 (3) ◽  
pp. R1-R10 ◽  
Author(s):  
Helene Hafslund Veire ◽  
Martin Landrø
Keyword(s):  
Seismic Data ◽  
Reservoir Characterization ◽  
Model Building ◽  
Joint Inversion ◽  
P Wave ◽  
Inversion Method ◽  
System Of Equations ◽  
S Wave ◽  
Data Set ◽  
Simultaneous Inversion

Elastic parameters derived from seismic data are valuable input for reservoir characterization because they can be related to lithology and fluid content of the reservoir through empirical relationships. The relationship between physical properties of rocks and fluids and P-wave seismic data is nonunique. This leads to large uncertainties in reservoir models derived from P-wave seismic data. Because S- waves do not propagate through fluids, the combined use of P-and S-wave seismic data might increase our ability to derive fluid and lithology effects from seismic data, reducing the uncertainty in reservoir characterization and thereby improving 3D reservoir model-building. We present a joint inversion method for PP and PS seismic data by solving approximated linear expressions of PP and PS reflection coefficients simultaneously using a least-squares estimation algorithm. The resulting system of equations is solved by singular-value decomposition (SVD). By combining the two independent measurements (PP and PS seismic data), we stabilize the system of equations for PP and PS seismic data separately, leading to more robust parameter estimation. The method does not require any knowledge of PP and PS wavelets. We tested the stability of this joint inversion method on a 1D synthetic data set. We also applied the methodology to North Sea multicomponent field data to identify sand layers in a shallow formation. The identified sand layers from our inverted sections are consistent with observations from nearby well logs.

Sign in / Sign up

Export Citation Format

Share Document