scholarly journals Stochastic reservoir characterization using prestack seismic data

Geophysics ◽  
2004 ◽  
Vol 69 (4) ◽  
pp. 978-993 ◽  
Author(s):  
Jo Eidsvik ◽  
Per Avseth ◽  
Henning Omre ◽  
Tapan Mukerji ◽  
Gary Mavko
Keyword(s):  
Reservoir Characterization ◽  
A Priori ◽  
Rock Physics ◽  
Real Data ◽  
Spatial Coupling ◽  
The North ◽  
Amplitude Versus Offset ◽  
Likelihood Model ◽  
Avo Attributes ◽  
The Impact

Reservoir characterization must be based on information from various sources. Well observations, seismic reflection times, and seismic amplitude versus offset (AVO) attributes are integrated in this study to predict the distribution of the reservoir variables, i.e., facies and fluid filling. The prediction problem is cast in a Bayesian setting. The a priori model includes spatial coupling through Markov random field assumptions and intervariable dependencies through nonlinear relations based on rock physics theory, including Gassmann's relation. The likelihood model relating observations to reservoir variables (including lithology facies and pore fluids) is based on approximations to Zoeppritz equations. The model assumptions are summarized in a Bayesian network illustrating the dependencies between the reservoir variables. The posterior model for the reservoir variables conditioned on the available observations is defined by the a priori and likelihood models. This posterior model is not analytically tractable but can be explored by Markov chain Monte Carlo (MCMC) sampling. Realizations of reservoir variables from the posterior model are used to predict the facies and fluid‐filling distribution in the reservoir. A maximum a posteriori (MAP) criterion is used in this study to predict facies and pore‐fluid distributions. The realizations are also used to present probability maps for the favorable (sand, oil) occurrence in the reservoir. Finally, the impact of seismic AVO attributes—AVO gradient, in particular—is studied. The approach is demonstrated on real data from a turbidite sedimentary system in the North Sea. AVO attributes on the interface between reservoir and cap rock are extracted from 3D seismic AVO data. The AVO gradient is shown to be valuable in reducing the ambiguity between facies and fluids in the prediction.

Hierarchical Bayesian lithology/fluid prediction: A North Sea case study

Geophysics ◽  
2012 ◽  
Vol 77 (2) ◽  
pp. B69-B85 ◽  
Author(s):  
Kjartan Rimstad ◽  
Per Avseth ◽  
Henning Omre
Keyword(s):  
North Sea ◽  
Rock Physics ◽  
Model Parameters ◽  
Gas Oil ◽  
Clear Fluid ◽  
Prior Model ◽  
Spatial Coupling ◽  
The North ◽  
Likelihood Model ◽  
Physics Model

Seismic 3D amplitude variation with offset (AVO) data from the Alvheim field in the North Sea are inverted into lithology/fluid classes, elastic properties, and porosity. Lithology/fluid maps over hydrocarbon prospects provide more reliable estimates of gas/oil volumes and improve the decision concerning further reservoir assessments. The Alvheim field is of turbidite origin with complex sand-lobe geometry and appears without clear fluid contacts across the field. The inversion is phrased in a Bayesian setting. The likelihood model contains a convolutional, linearized seismic model and a rock-physics model that capture vertical trends due to increased sand compaction and possible cementation. The likelihood model contains several global model parameters that are considered to be stochastic to adapt the model to the field under study and to include model uncertainty in the uncertainty assessments. The prior model on the lithology/fluid classes is a Markov random field that captures local vertical/horizontal continuity and vertical sorting of fluids. The predictions based on the posterior model are validated by observations in five wells used as blind tests. Hydrocarbon volumes with reliable gas/oil distributions are predicted. The spatial coupling provided by the prior model is crucial for reliable predictions; without the coupling, hydrocarbon volumes are severely underestimated. Depth trends in the rock-physics likelihood model improve the gas versus oil predictions. The porosity predictions reproduce contrasts observed in the wells, and mean square error is reduced by one-third compared to Gauss-linear predictions.

De-risking Appraisal Phase in the Recent Gas Discovery in Banggai-Sula Basin, Central Sulawesi: Application of AVO Analysis and Pre-stack Inversion in the Sub-ophiolite Reef Carbonate Play

2021 ◽  
Author(s):  
M. Ahmad
Keyword(s):  
Reservoir Characterization ◽  
Linear Trend ◽  
Rock Physics ◽  
Seismic Inversion ◽  
Added Value ◽  
Quality Data ◽  
Water Contact ◽  
Avo Analysis ◽  
Amplitude Versus Offset ◽  
Avo Attributes

Following the success in the exploration drilling campaign in the last few years, Pertamina EP puts the recently discovered Wol Structure into the appraisal stage. The exploration wells Wol-001 and Wol-002 were spudded in 2017 and 2019 respectively, and both flowed a significant gas rate from an excellent reservoir of Miocene Reef of Minahaki Formation. A good understanding of the reservoir distribution was essential in such a stage. Therefore, a proper reservoir characterization was then carried out for further appraisal purposes. Using the improved quality data from the latest 5D interpolation-PSDM as input, integration of amplitude versus offset (AVO) techniques and rock physics analysis was conducted to investigate the hydrocarbon extent. The AVO class IIp was observed at the boundary between overlying Kintom Shale and gas saturated Minahaki limestone. It is indicated by a positive intercept (Ro), decreased amplitudes with offsets, and negative amplitudes in the far offsets. This polarity reversal characteristic is clearly seen from both AVO modeling and actual CDP in the well locations. Several CDPs inside and outside the closure were also examined to check the consistency. The slice of partial stack volumes has also exhibited a similar trend within the closure where class IIp is suggestive. Since the AVO attributes such as intercept and gradient solely were not able to visualize the reservoir extent properly, the pre-stack seismic inversion was performed to obtain a more accurate reservoir distribution through quantitative interpretation. A cross plot of P-impedance (Ip) over S-impedance (Is) differentiates the gas zone clearly from the wet linear trend. A depth slice at GWC (gas water contact) level describes that most of the Wol Structure is gas-saturated including the newly identified closure in the northwest. It is a three-way dip closure formed by limestone that was dragged upward by a thrust fault. Interestingly, it has a similar AVO response to the main Wol Structure which suggests a gas-bearing reservoir. This work brings an added value to the use of AVO analysis and pre-stack inversion for hydrocarbon mapping for appraisal purposes. Not only it has largely reduced the subsurface uncertainty, but also revealed an upside potential that is worth considering in future exploration.

The vertical image projection method for migration stretch compensation

2021 ◽  
pp. 1-55
Author(s):  
Arash JafarGandomi
Keyword(s):  
Real Data ◽  
Amplitude Variation ◽  
Seismic Migration ◽  
Amplitude Variation With Offset ◽  
Amplitude Inversion ◽  
Image Projection ◽  
And Migration ◽  
Avo Attributes ◽  
Conditioning Approach ◽  
The Impact

True amplitude inversion is often carried out without taking into account migration distortions to the wavelet. Seismic migration leaves a dip-dependent effect on the wavelet that can cause significant inaccuracies in the inverted impedances obtained from conventional inversion approaches based on 1D vertical convolutional modelling. Neglecting this effect causes misleading inversion results and leakage of dipping noise and migration artifacts from higher frequency bands to the lower frequencies. I have observed that despite dip-dependency of this effect, low-dip and flat events may also suffer if they are contaminated with cross-cutting noise, steep migration artifacts, and smiles. In this paper I propose an efficient, effective and reversible data pre-conditioning approach that accounts for dip-dependency of the wavelet and is applied to migrated images prior to inversion. My proposed method consists of integrating data with respect to the total wavenumber followed by the differentiation with respect to the vertical wavenumber. This process is equivalent to applying a deterministic dip-consistent pre-conditioning that projects the data from the total wavenumber to the vertical wavenumber axis. This preconditioning can be applied to both pre- and post-stack data as well as to amplitude variation with offset (AVO) attributes such as intercept and gradient before inversion. The vertical image projection methodology that I propose here reduces the impact of migration artifacts such as cross-cutting noise and migration smiles and improves inverted impedances in both synthetic and real data examples. In particular I show that neglecting the proposed pre-conditioning leads to anomalously higher impedance values along the steeply dipping structures.

Impact of rock-physics depth trends and Markov random fields on hierarchical Bayesian lithology/fluid prediction

Geophysics ◽  
2010 ◽  
Vol 75 (4) ◽  
pp. R93-R108 ◽  
Author(s):  
Kjartan Rimstad ◽  
Henning Omre
Keyword(s):  
Parameter Uncertainty ◽  
Seismic Data ◽  
Markov Random Fields ◽  
Petroleum Reservoirs ◽  
Rock Physics ◽  
Real Data ◽  
Model Parameter ◽  
Markov Random ◽  
Likelihood Model ◽  
Model Parameter Uncertainty

Early assessments of petroleum reservoirs are usually based on seismic data and observations in a small number of wells. Decision-making concerning the reservoir will be improved if these data can be integrated and converted into a lithology/fluid map of the reservoir. We analyze lithology/fluid prediction in a Bayesian setting, based on prestack seismic data and well observations. The likelihood model contains a convolved linearized Zoeppritz relation and rock-physics models with depth trends caused by compaction and cementation. Well observations are assumed to be exact. The likelihood model contains several global parameters such as depth trend, wavelets, and error parameters; the inference of these is an integral part of the study. The prior model is based on a profile Markov random field parameterized to capture different continuity directions for lithologies and fluids. The posterior model captures prediction and model-parameter uncertainty and is assessed by Markov-chain Monte Carlo simulation-based inference. The inversion model is evaluated on a synthetic and a real data case. It is concluded that geologically plausible lithology/fluid predictions can be made. Rock physics depth trends have influence when cementation is present and/or predictions at depth outside the well range are made. Inclusion of model-parameter uncertainty makes the prediction uncertainties more realistic.

scholarly journals Estimation of rock physics properties from seismic attributes — Part 1: Strategy and sensitivity analysis

Geophysics ◽  
2016 ◽  
Vol 81 (3) ◽  
pp. M35-M53 ◽  
Author(s):  
Bastien Dupuy ◽  
Stéphane Garambois ◽  
Jean Virieux
Keyword(s):  
Input Data ◽  
Reservoir Characterization ◽  
Quantitative Estimation ◽  
A Priori ◽  
Rock Physics ◽  
Fluid Phase ◽  
Quality Factors ◽  
Simultaneous Inversion ◽  
Fluid Saturation ◽  
Wide Range

The quantitative estimation of rock physics properties is of great importance in any reservoir characterization. We have studied the sensitivity of such poroelastic rock physics properties to various seismic viscoelastic attributes (velocities, quality factors, and density). Because we considered a generalized dynamic poroelastic model, our analysis was applicable to most kinds of rocks over a wide range of frequencies. The viscoelastic attributes computed by poroelastic forward modeling were used as input to a semiglobal optimization inversion code to estimate poroelastic properties (porosity, solid frame moduli, fluid phase properties, and saturation). The sensitivity studies that we used showed that it was best to consider an inversion system with enough input data to obtain accurate estimates. However, simultaneous inversion for the whole set of poroelastic parameters was problematic due to the large number of parameters and their trade-off. Consequently, we restricted the sensitivity tests to the estimation of specific poroelastic parameters by making appropriate assumptions on the fluid content and/or solid phases. Realistic a priori assumptions were made by using well data or regional geology knowledge. We found that (1) the estimation of frame properties was accurate as long as sufficient input data were available, (2) the estimation of permeability or fluid saturation depended strongly on the use of attenuation data, and (3) the fluid bulk modulus can be accurately inverted, whereas other fluid properties have a low sensitivity. Introducing errors in a priori rock physics properties linearly shifted the estimations, but not dramatically. Finally, an uncertainty analysis on seismic input data determined that, even if the inversion was reliable, the addition of more input data may be required to obtain accurate estimations if input data were erroneous.

scholarly journals An integrated petrophysical-based wedge modeling and thin bed AVO analysis for improved reservoir characterization of Zhujiang Formation, Huizhou sub-basin, China: A case study

2020 ◽  
Vol 12 (1) ◽  
pp. 256-274
Author(s):  
Wasif Saeed ◽  
Hongbing Zhang ◽  
Qiang Guo ◽  
Aamir Ali ◽  
Tahir Azeem ◽  
...  
Keyword(s):  
Seismic Data ◽  
Reservoir Characterization ◽  
Dominant Frequency ◽  
Well Logs ◽  
Amplitude Versus Offset ◽  
Avo Attributes ◽  
Thin Reservoir ◽  
Amplitude Versus

AbstractThe main reservoir in Huizhou sub-basin is Zhujiang Formation of early Miocene age. The petrophysical analysis shows that the Zhujiang Formation contains thin carbonate intervals, which have good hydrocarbon potential. However, the accurate interpretation of thin carbonate intervals is always challenging as conventional seismic interpretation techniques do not provide much success in such cases. In this study, well logs, three-layer forward amplitude versus offset (AVO) model and the wedge model are integrated to analyze the effect of tuning thickness on AVO responses. It is observed that zones having a thickness greater than or equal to 15 m can be delineated with seismic data having a dominant frequency of more than 45 Hz. The results are also successfully verified by analyzing AVO attributes, i.e., intercept and gradient. The study will be helpful to enhance the characterization of thin reservoir intervals and minimize the risk of exploration in the Huizhou sub-basin, China.

scholarly journals Sensitivity of top-down CO source estimates to the modeled vertical structure in atmospheric CO

2015 ◽  
Vol 15 (3) ◽  
pp. 1521-1537 ◽  
Author(s):  
Z. Jiang ◽  
D. B. A. Jones ◽  
H. M. Worden ◽  
D. K. Henze
Keyword(s):  
Surface Layer ◽  
North America ◽  
Vertical Structure ◽  
A Priori Estimates ◽  
A Priori ◽  
Convective Transport ◽  
Free Troposphere ◽  
The North ◽  
Reliable Quantification ◽  
The Impact

Abstract. We assessed the sensitivity of regional CO source estimates to the modeled vertical CO distribution by assimilating multi-spectral MOPITT (Measurements of Pollution In The Troposphere) V5J CO retrievals with the GEOS-Chem model. We compared the source estimates obtained by assimilating the CO profiles and the surface layer retrievals from June 2004 to May 2005. Because the surface layer retrievals are less sensitive to CO in the free troposphere, it is expected that they should provide constraints in the CO source estimates that are less sensitive to the vertical structure of CO in the free troposphere. The inferred source estimates all suggest a reduction in CO emissions in the tropics and subtropics, and an increase in the extratropics over the a priori estimates. The tropical decreases were particularly pronounced for regions where the biogenic source of CO was dominant, suggesting an overestimate of the a priori isoprene source of CO in the model. We found that the differences between the regional source estimates inferred from the profile and surface layer retrievals for 2004–2005 were small, generally less than 10% for the main continental regions, except for estimates for southern Asia, North America, and Europe. Because of discrepancies in convective transport in the model, the CO source estimates for India and southeastern Asia inferred from the CO profiles were significantly higher than those estimated from the surface layer retrievals during June–August 2004. On the other hand, the profile inversion underestimated the CO emissions from North America and Europe compared to the assimilation of the surface layer retrievals. We showed that vertical transport of air from the North American and European boundary layers is slower than from other continental regions, and thus air in the free troposphere from North America and Europe in the model is more chemically aged, which could explain the discrepancy between the source estimates inferred from the profile and surface layer retrievals. We also examined the impact of the OH distribution on the source estimates and found that the discrepancies between the source estimates obtained with two OH fields were larger when using the profile data, which is consistent with greater sensitivity to the more chemically aged air in the free troposphere. Our findings indicate that regional CO source estimates are sensitive to the vertical CO structure. They suggest that diagnostics to assess the age of air from the continental source regions should help interpret the results from CO source inversions. Our results also suggest that assimilating a broader range of composition measurements to provide better constraint on tropospheric OH and the biogenic sources of CO is essential for reliable quantification of the regional CO budget.

Statistical rock physics inversion of elastic and electrical properties for CO2 sequestration studies

2020 ◽  
Vol 223 (1) ◽  
pp. 707-724
Author(s):  
Mohit Ayani ◽  
Dario Grana
Keyword(s):  
Electrical Properties ◽  
Co2 Sequestration ◽  
Rock Physics ◽  
Real Data ◽  
Inversion Method ◽  
Model Parameters ◽  
The North ◽  
Physics Model ◽  
Rock Physics Model ◽  
Statistical Rock Physics

SUMMARY We present a statistical rock physics inversion of the elastic and electrical properties to estimate the petrophysical properties and quantify the associated uncertainty. The inversion method combines statistical rock physics modeling with Bayesian inverse theory. The model variables of interest are porosity and fluid saturations. The rock physics model includes the elastic and electrical components and can be applied to the results of seismic and electromagnetic inversion. To describe the non-Gaussian behaviour of the model properties, we adopt non-parametric probability density functions to sample multimodal and skewed distributions of the model variables. Different from machine learning approach, the proposed method is not completely data-driven but is based on a statistical rock physics model to link the model parameters to the data. The proposed method provides pointwise posterior distributions of the porosity and CO2 saturation along with the most-likely models and the associated uncertainty. The method is validated using synthetic and real data acquired for CO2 sequestration studies in different formations: the Rock Springs Uplift in Southwestern Wyoming and the Johansen formation in the North Sea, offshore Norway. The proposed approach is validated under different noise conditions and compared to traditional parametric approaches based on Gaussian assumptions. The results show that the proposed method provides an accurate inversion framework where instead of fitting the relationship between the model and the data, we account for the uncertainty in the rock physics model.

Modeling for geophysical monitoring of multiple phase saturation of rocks: Applications to CO2 sequestration

2014 ◽  
Vol 2 (2) ◽  
pp. SC47-SC60 ◽  
Author(s):  
Stephen Brown
Keyword(s):  
Reservoir Characterization ◽  
Numerical Study ◽  
Carbonate Reservoir ◽  
Rock Properties ◽  
Small Scale ◽  
Petrophysical Properties ◽  
Amplitude Versus Offset ◽  
Phase Saturation ◽  
Avo Attributes ◽  
Subsurface Reservoir

A numerical study of measured petrophysical properties was used to develop and demonstrate a workflow for monitoring [Formula: see text] sequestration in a subsurface reservoir. Amplitude versus offset (AVO) attributes can be sensitive discriminators for [Formula: see text] presence. The sensitivity of AVO attributes to [Formula: see text] saturation increased when an upscaling scheme that propagated the effects of small-scale heterogeneities was used. A global sensitivity analysis was then performed to study the importance of various rock properties that would be used as inputs in reservoir characterization. An extensive rock properties database for a carbonate reservoir containing natural variability was used. The various rock properties were ranked by their importance to the quality of the remote characterization. Defining and using the intrinsic correlations among petrophysical properties were essential to evaluating whether a particular reservoir was amenable (sensitive) to this AVO characterization method.

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