Seismic inversion for reservoir properties combining statistical rock physics and geostatistics: A review

Geophysics ◽  
2010 ◽  
Vol 75 (5) ◽  
pp. 75A165-75A176 ◽  
Author(s):  
Miguel Bosch ◽  
Tapan Mukerji ◽  
Ezequiel F. Gonzalez
Keyword(s):  
Elastic Properties ◽  
Quantitative Estimation ◽  
Rock Physics ◽  
Seismic Inversion ◽  
Elastic Parameters ◽  
Reservoir Properties ◽  
Sequential Approach ◽  
Geostatistical Methods ◽  
Bayesian Formulation ◽  
Statistical Rock Physics

There are various approaches for quantitative estimation of reservoir properties from seismic inversion. A general Bayesian formulation for the inverse problem can be implemented in two different work flows. In the sequential approach, first seismic data are inverted, deterministically or stochastically, into elastic properties; then rock-physics models transform those elastic properties to the reservoir property of interest. The joint or simultaneous work flow accounts for the elastic parameters and the reservoir properties, often in a Bayesian formulation, guaranteeing consistency between the elastic and reservoir properties. Rock physics plays the important role of linking elastic parameters such as impedances and velocities to reservoir properties of interest such as lithologies, porosity, and pore fluids. Geostatistical methods help add constraints of spatial correlation, conditioning to different kinds of data and incorporating subseismic scales of heterogeneities.

Probabilistic petrophysical-properties estimation integrating statistical rock physics with seismic inversion

Geophysics ◽  
2010 ◽  
Vol 75 (3) ◽  
pp. O21-O37 ◽  
Author(s):  
Dario Grana ◽  
Ernesto Della Rossa
Keyword(s):  
Seismic Data ◽  
Measurement Errors ◽  
Rock Physics ◽  
Seismic Inversion ◽  
Joint Estimation ◽  
Physical Models ◽  
Inversion Method ◽  
Petrophysical Properties ◽  
Reservoir Properties ◽  
Statistical Rock Physics

A joint estimation of petrophysical properties is proposed that combines statistical rock physics and Bayesian seismic inversion. Because elastic attributes are correlated with petrophysical variables (effective porosity, clay content, and water saturation) and this physical link is associated with uncertainties, the petrophysical-properties estimation from seismic data can be seen as a Bayesian inversion problem. The purpose of this work was to develop a strategy for estimating the probability distributions of petrophysical parameters and litho-fluid classes from seismics. Estimation of reservoir properties and the associated uncertainty was performed in three steps: linearized seismic inversion to estimate the probabilities of elastic parameters, probabilistic upscaling to include the scale-changes effect, and petrophysical inversion to estimate the probabilities of petrophysical variables andlitho-fluid classes. Rock-physics equations provide the linkbetween reservoir properties and velocities, and linearized seismic modeling connects velocities and density to seismic amplitude. A full Bayesian approach was adopted to propagate uncertainty from seismics to petrophysics in an integrated framework that takes into account different sources of uncertainty: heterogeneity of the real data, approximation of physical models, measurement errors, and scale changes. The method has been tested, as a feasibility step, on real well data and synthetic seismic data to show reliable propagation of the uncertainty through the three different steps and to compare two statistical approaches: parametric and nonparametric. Application to a real reservoir study (including data from two wells and partially stacked seismic volumes) has provided as a main result the probability densities of petrophysical properties and litho-fluid classes. It demonstrated the applicability of the proposed inversion method.

Introduction to this special section: Rock physics

2019 ◽  
Vol 38 (5) ◽  
pp. 332-332
Author(s):  
Yongyi Li ◽  
Lev Vernik ◽  
Mark Chapman ◽  
Joel Sarout
Keyword(s):  
Reservoir Characterization ◽  
Model Building ◽  
Seismic Analysis ◽  
Focal Point ◽  
Special Section ◽  
Rock Physics ◽  
Seismic Inversion ◽  
Reservoir Properties ◽  
Model Studies ◽  
Experimental Rock

Rock physics links the physical properties of rocks to geophysical and petrophysical observations and, in the process, serves as a focal point in many exploration and reservoir characterization studies. Today, the field of rock physics and seismic petrophysics embraces new directions with diverse applications in estimating static and dynamic reservoir properties through time-variant mechanical, thermal, chemical, and geologic processes. Integration with new digital and computing technologies is gradually gaining traction. The use of rock physics in seismic imaging, prestack seismic analysis, seismic inversion, and geomechanical model building also contributes to the increase in rock-physics influence. This special section highlights current rock-physics research and practices in several key areas, namely experimental rock physics, rock-physics theory and model studies, and the use of rock physics in reservoir characterizations.

Quantifying reservoir properties of the East Texas Woodbine through rock physics and multiattribute seismic inversion

2005 ◽  
Author(s):  
Ron McWhorter ◽  
Duane Pierce ◽  
Niranjan Banik ◽  
Haibin Xu ◽  
George Bunge ◽  
...  
Keyword(s):  
Rock Physics ◽  
Seismic Inversion ◽  
East Texas ◽  
Reservoir Properties

scholarly journals Integrated Well Data And 3D Seismic Inversion Study For Reservoir Delineation And Description

2020 ◽  
Vol 70 (1) ◽  
pp. 209-220
Author(s):  
Qazi Sohail Imran ◽  
◽  
Numair Ahmad Siddiqui ◽  
Abdul Halim Abdul Latif ◽  
Yasir Bashir ◽  
...  
Keyword(s):  
Reservoir Characterization ◽  
Rock Physics ◽  
Seismic Inversion ◽  
Depositional Environments ◽  
Well Log ◽  
Reservoir Properties ◽  
Rock Physics Modeling ◽  
Physics Modeling ◽  
And Performance ◽  
Well Log Analysis

Offshore petroleum systems are often very complex and subtle because of a variety of depositional environments. Characterizing a reservoir based on conventional seismic and well-log stratigraphic analysis in intricate settings often leads to uncertainties. Drilling risks, as well as associated subsurface uncertainties can be minimized by accurate reservoir delineation. Moreover, a forecast can also be made about production and performance of a reservoir. This study is aimed to design a workflow in reservoir characterization by integrating seismic inversion, petrophysics and rock physics tools. Firstly, to define litho facies, rock physics modeling was carried out through well log analysis separately for each facies. Next, the available subsurface information is incorporated in a Bayesian engine which outputs several simulations of elastic reservoir properties, as well as their probabilities that were used for post-inversion analysis. Vast areal coverage of seismic and sparse vertical well log data was integrated by geostatistical inversion to produce acoustic impedance realizations of high-resolution. Porosity models were built later using the 3D impedance model. Lastly, reservoir bodies were identified and cross plot analysis discriminated the lithology and fluid within the bodies successfully.

Seismic petrophysics focused case study for AVA modelling and pre-stack seismic inversion

2016 ◽  
Vol 56 (1) ◽  
pp. 341
Author(s):  
Jahan Zeb ◽  
Sanjeev Rajput ◽  
Jimmy Ting
Keyword(s):  
Model Building ◽  
Rock Physics ◽  
Seismic Inversion ◽  
Oil Field ◽  
Well Logs ◽  
Real Field ◽  
Seismic Responses ◽  
Reservoir Properties ◽  
Rock Types ◽  
Amplitude Versus

Hydrocarbon reservoirs are characterised by integrating seismic, well-log and petrophysical information, which are dissimilar in spatial distribution, scale and relationship to reservoir properties. Well logs are essential for amplitude versus offset (AVO) modelling and seismic inversion. The usability of well logs can be determined during wavelet estimation, seismic-to-well ties, background model building, property distribution for inversion, deriving probability density functions and variograms, offset-to-angle conversion of seismic data, and many other processes. For the implementation of seismic inversion workflows, accurate and geologically corrected compressional-sonic, shear-sonic and density logs are necessary. Preparing the logs for quantitative interpretation becomes challenging in a real-field environment because of bad borehole conditions including washouts, uncalibrated and variability of logging tools, invasion effects, missing shear logs and change of borehole size. Conventional petrophysical analysis is usually restricted to the reservoir interval, the calculation of reservoir versus non-reservoir (including sands or shales), and log corrections for smaller intervals; in contrast, seismic petrophysics encompasses the entire geological interval, calculates the volume of multi-minerals, incorporates boundaries between non-reservoir and reservoir, and often includes the prediction of missing compressional and shear-sonic for AVO analysis. A detailed seismic petrophysics analysis was performed for amplitude versus angle (AVA) modelling and attributes analysis. To perform the AVA modelling, a series of forward models in association with rock physics modelled fluid-substituted logs were developed, and associated seismic responses for various pore fluids and rock types studied. The results reveal that synthetic seismic responses together with the AVA analysis show changes for various lithologies. AVA attributes analysis show trends in generated synthetic seismic responses for various fluid-substituted and porosity logs. Reservoir modelling and fluid substitution increases understanding of the observed seismic response. This paper describes detailed data analysis using various techniques to confirm the rock model for petrophysical evaluation, rock physics modelling, AVA analysis, pre-stack seismic inversion, and the scenario modelling applied to the study of an oil field in Australia.

Seismic Quantitative Interpretation for Uncertainty Reduction of Subsurface Modeling of the Deep Visean Reservoirs

2021 ◽  
Author(s):  
Pavlo Kuzmenko ◽  
Rustem Valiakhmetov ◽  
Francesco Gerecitano ◽  
Viktor Maliar ◽  
Grigori Kashuba ◽  
...  
Keyword(s):  
Elastic Properties ◽  
Seismic Data ◽  
Seismic Anisotropy ◽  
Rock Physics ◽  
Seismic Inversion ◽  
Well Logs ◽  
Quantitative Interpretation ◽  
Structural Interpretation ◽  
Elastic Inversion ◽  
Subsurface Modeling

Abstract The seismic data have historically been utilized to perform structural interpretation of the geological subsurface. Modern approaches of Quantitative Interpretation are intended to extract geologically valuable information from the seismic data. This work demonstrates how rock physics enables optimal prediction of reservoir properties from seismic derived attributes. Using a seismic-driven approach with incorporated prior geological knowledge into a probabilistic subsurface model allowed capturing uncertainty and quantifying the risk for targeting new wells in the unexplored areas. Elastic properties estimated from the acquired seismic data are influenced by the depositional environment, fluid content, and local geological trends. By applying the rock physics model, we were able to predict the elastic properties of a potential lithology away from the well control points in the subsurface whether or not it has been penetrated. Seismic amplitude variation with incident angle (AVO) and azimuth (AVAZ) jointly with rock-derived petrophysical interpretations were used for stochastical modeling to capture the reservoir distribution over the deep Visean formation. The seismic inversion was calibrated by available well log data and by traditional structural interpretation. Seismic elastic inversion results in a deep Lower Carboniferous target in the central part of the DDB are described. The fluid has minimal effect on the density and Vp. Well logs with cross-dipole acoustics are used together with wide-azimuth seismic data, processed with amplitude control. It is determined that seismic anisotropy increases in carbonate deposits. The result covers a set of lithoclasses and related probabilities: clay minerals, tight sandstones, porous sandstones, and carbonates. We analyzed the influence of maximum angles determination for elastic inversion that varied from 32.5 to 38.5 degrees. The greatest influence of the far angles selection is on the density. AI does not change significantly. Probably the 38,5 degrees provides a superior response above the carbonates. It does not seem to damage the overall AVA behavior, which result in a good density outcome, as higher angles of incidence are included. It gives a better tie to the wells for the high density layers over the interval of interest. Sand probability cube must always considered in the interpretation of the lithological classification that in many cases may be misleading (i.e. when sand and shale probabilities are very close to each other, because of small changes in elastic parameters). The authors provide an integrated holistic approach for quantitative interpretation, subsurface modeling, uncertainty evaluation, and characterization of reservoir distribution using pre-existing well logs and recently acquired seismic data. This paper underpins the previous efforts and encourages the work yet to be fulfilled on this subject. We will describe how quantitative interpretation was used for describing the reservoir, highlight values and uncertainties, and point a way forward for further improvement of the process for effective subsurface modeling.

Seismic screening of rock stiffness and fluid softening using rock-physics attributes

2015 ◽  
Vol 3 (4) ◽  
pp. SAE85-SAE93 ◽  
Author(s):  
Per Avseth ◽  
Tor Veggeland
Keyword(s):  
Rock Physics ◽  
Seismic Inversion ◽  
Well Log ◽  
Reservoir Properties ◽  
Empirical Relationships ◽  
Norwegian Sea ◽  
Reservoir Rocks ◽  
Fluid Saturation ◽  
Reservoir Sandstones ◽  
Elastic Impedance

We have developed a methodology to create easy-to-implement rock-physics attributes that can be used to screen for reservoir sandstones and hydrocarbon pore fill from seismic inversion data. Most seismic attributes are based on the empirical relationships between reservoir properties and seismic observables. We have honored the physical properties of the rocks by defining attributes that complied with calibrated rock-physics models. These attributes included the fluid saturation sensitive curved pseudo-elastic impedance (CPEI) and the rock stiffness/lithology attribute pseudo-elastic impedance for lithology (PEIL). We found that the CPEI attribute correlated nicely with saturation and resistivity, whereas the PEIL attribute in practice was a scaled version of the shear modulus and correlated nicely with porosity. We determined the use of these attributes on well log and seismic inversion data from the Norwegian Sea, and we successfully screened out reservoir rocks filled with either water or hydrocarbons.

Mapping lithofacies and pore‐fluid probabilities in a North Sea reservoir: Seismic inversions and statistical rock physics

Geophysics ◽  
2001 ◽  
Vol 66 (4) ◽  
pp. 988-1001 ◽  
Author(s):  
T. Mukerji ◽  
A. Jørstad ◽  
P. Avseth ◽  
G. Mavko ◽  
J. R. Granli
Keyword(s):  
North Sea ◽  
Seismic Data ◽  
Acoustic Impedance ◽  
Probability Distributions ◽  
Rock Physics ◽  
Normal Incidence ◽  
Seismic Inversion ◽  
Training Data ◽  
Well Log ◽  
Statistical Rock Physics

Reliably predicting lithologic and saturation heterogeneities is one of the key problems in reservoir characterization. In this study, we show how statistical rock physics techniques combined with seismic information can be used to classify reservoir lithologies and pore fluids. One of the innovations was to use a seismic impedance attribute (related to the [Formula: see text] ratio) that incorporates far‐offset data, but at the same time can be practically obtained using normal incidence inversion algorithms. The methods were applied to a North Sea turbidite system. We incorporated well log measurements with calibration from core data to estimate the near‐offset and far‐offset reflectivity and impedance attributes. Multivariate probability distributions were estimated from the data to identify the attribute clusters and their separability for different facies and fluid saturations. A training data was set up using Monte Carlo simulations based on the well log—derived probability distributions. Fluid substitution by Gassmann’s equation was used to extend the training data, thus accounting for pore fluid conditions not encountered in the well. Seismic inversion of near‐offset and far‐offset stacks gave us two 3‐D cubes of impedance attributes in the interwell region. The near‐offset stack approximates a zero‐offset section, giving an estimate of the normal incidence acoustic impedance. The far offset stack gives an estimate of a [Formula: see text]‐related elastic impedance attribute that is equivalent to the acoustic impedance for non‐normal incidence. These impedance attributes obtained from seismic inversion were then used with the training probability distribution functions to predict the probability of occurrence of the different lithofacies in the interwell region. Statistical classification techniques, as well as geostatistical indicator simulations were applied on the 3‐D seismic data cube. A Markov‐Bayes technique was used to update the probabilities obtained from the seismic data by taking into account the spatial correlation as estimated from the facies indicator variograms. The final results are spatial 3‐D maps of not only the most likely facies and pore fluids, but also their occurrence probabilities. A key ingredient in this study was the exploitation of physically based seismic‐to‐reservoir property transforms optimally combined with statistical techniques.

scholarly journals Quantitative seismic interpretation of rock brittleness based on statistical rock physics

Geophysics ◽  
2019 ◽  
Vol 84 (4) ◽  
pp. IM63-IM75 ◽  
Author(s):  
Lin Wang ◽  
Feng Zhang ◽  
Xiang-Yang Li ◽  
Bang-Rang Di ◽  
Lian-Bo Zeng
Keyword(s):  
Random Field ◽  
Markov Random Field ◽  
Rock Physics ◽  
Seismic Interpretation ◽  
Brittleness Index ◽  
Simple Relationship ◽  
Elastic Parameters ◽  
Markov Random ◽  
Rock Brittleness ◽  
Statistical Rock Physics

Rock brittleness is one of the important properties for fracability evaluation, and it can be represented by different physical properties. The mineralogy-based brittleness index (BIM) builds a simple relationship between mineralogy and brittleness, but it may be ambiguous for rocks with a complex microstructure; whereas the elastic moduli-based brittleness index (BIE) is applicable in the field, but BIE interpretation needs to be constrained by lithofacies information. We have developed a new workflow for quantitative seismic interpretation of rock brittleness: Lithofacies are defined by a criterion combining BIM and BIE for comprehensive brittleness evaluation; statistical rock-physics methods are applied for quantitative interpretation by using inverted elastic parameters; acoustic impedance and elastic impedance are selected as the optimized pair of attributes for lithofacies classification. To improve the continuity and accuracy of the interpreted results, a Markov random field is applied in the Bayesian rule as the spatial constraint. A 2D synthetic test demonstrates the feasibility of the Bayesian classification with a Markov random field. This new interpretation framework is also applied to a shale reservoir formation from China. Comparison analysis indicates that brittle shale sections can be efficiently discriminated from ductile shale sections and tight sand sections by using the inverted elastic parameters.

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