Bayesian seismic AVO inversion for reservoir variables with bimodal spatial histograms

Geophysics ◽  
2021 ◽  
pp. 1-141
Author(s):  
Ole Bernhard Forberg ◽  
Øyvind Kjøsnes ◽  
Henning Omre
Keyword(s):  
Random Field ◽  
Linear Models ◽  
Water Saturation ◽  
Gaussian Random Field ◽  
Rock Physics ◽  
Seismic Inversion ◽  
Oil Reservoir ◽  
Initial State ◽  
Reservoir Properties ◽  
Avo Inversion

We consider seismic AVO inversion for prediction of the reservoir properties porosity and water saturation. An oil reservoir at initial state is studied; hence gravitational effects dominate and keep hydrocarbons from mixing with water. Histograms of observations of water saturation along wells are consequently clearly bimodal, which is challenging to model. The seismic AVO inversion is cast in a Bayesian framework. The prior spatial model for porosity and water saturation is specified to be a selection Gaussian random field, which is capable of representing spatial variables with multimodal histograms. By using linear models for the seismic and rock-physics likelihoods, the posterior model is also a selection Gaussian random field. Hence, the Bayesian seismic inversion can be solved analytically and the bimodal characteristics of the water saturations can be reproduced. The methodology is defined and demonstrated on two synthetic cases inspired by real data from an oil reservoir. Compared to standard spatial Gaussian models, the improvement of the inversion results is substantial. Inversion of the real seismic AVO data along a well trace reproduces the corresponding well observations fairly precisely, and is considered very encouraging.

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

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.

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 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.

Assessment of Reservoir Properties using Applied Rock Physics & Seismic Inversion for Near-Field Exploration in a Niger Delta Field

2019 ◽  
Author(s):  
Julius Adesun ◽  
Olanike Olajide ◽  
Chidi Ekesiobi ◽  
Abidoun Ogunjobi ◽  
Kehinde Ishola
Keyword(s):  
Niger Delta ◽  
Near Field ◽  
Rock Physics ◽  
Seismic Inversion ◽  
Reservoir Properties

Integrated reservoir characterization of a Utica Shale with focus on sweet spot discrimination

2020 ◽  
Vol 8 (3) ◽  
pp. SM1-SM14
Author(s):  
Jinming Zhu
Keyword(s):  
Reservoir Characterization ◽  
Water Saturation ◽  
Clay Content ◽  
Seismic Inversion ◽  
Reservoir Properties ◽  
Geomechanical Properties ◽  
Utica Shale ◽  
Characterization Study ◽  
Separate Point

Multiclient 3D seismic data were acquired in 2015 in eastern Ohio for reservoir characterization of the Utica Shale consisting of the Utica and Point Pleasant Formations. I attained accurate, high-fidelity acoustic impedance, shear impedance, density, and [Formula: see text], from elastic inversion. These accurate inversion results allow consistent calculation of reservoir and geomechanical properties of the Utica Shale. I found density critically important affecting the accuracy of other reservoir and geomechanical properties. More than a dozen properties in geologic, geomechanical, and reservoir categories were acquired from logs, cores, and seismic inversion, for this integrated reservoir characterization study. These properties include buried depth, formation thickness, mineralogy, density, Young’s modulus, Poisson’s ratio (PR), brittleness, total organic carbon (TOC), porosity, water saturation, permeability, clay content, and natural fractures. A ternary diagram of core samples from 18 wells demonstrates that the Point Pleasant is dominant with calcite, whereas the Utica mainly contains clay. Inverted density clearly divides Point Pleasant as low density from the overlying Utica. Calculated reservoir properties undoubtedly delineate the traditional Utica Shale as two distinctive formations. I calculated that the Utica Formation contains 1%–2% TOC, 3.5%–4.8% porosity, 10%–24% water saturation, and 40%–58% clay content, whereas Point Pleasant contains 3%–4.5% TOC, 5%–9% porosity, 2%–10% water saturation, and 15%–35% clay content. The PR and brittleness clearly separate Point Pleasant from the overlying Utica, with a lower PR and a higher brittleness index in Point Pleasant than in Utica. The higher brittleness in Point Pleasant makes it easier to frac, leading to enhanced permeability. Both formations exhibit spatial variations of reservoir and geomechanical properties. Nevertheless, the underlying Point Pleasant is obviously better than the Utica Shale with favorable reservoir and geomechanical properties for optimal development and production, although Utica is thicker and shallower. The central and southeastern portions of Point Pleasant have the sweetest reservoirs.

Seismic data conditioning and orthotropic rock-physics-based inversion of wide-azimuth P-wave seismic data for fracture and total organic carbon characterization in a north Kuwait unconventional reservoir

Geophysics ◽  
2018 ◽  
Vol 83 (4) ◽  
pp. B229-B240 ◽  
Author(s):  
Rajive Kumar ◽  
Prashant Bansal ◽  
Bader S. Al-Mal ◽  
Sagnik Dasgupta ◽  
Colin Sayers ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Total Organic Carbon ◽  
Seismic Data ◽  
Rock Physics ◽  
Seismic Inversion ◽  
P Wave ◽  
Natural Fracture ◽  
Reservoir Properties ◽  
Fluid Saturation ◽  
Fracture Intensity

Optimization of production from unconventional reservoirs requires estimates of reservoir properties such as porosity, total organic carbon (TOC) content, clay content, fluid saturation, and fracture intensity. The porosity and TOC content help to determine reservoir quality, and the natural fracture intensity provides information important for the completion strategy. Because shale reservoirs display intrinsic anisotropy due to layering and the partial alignment of clay minerals and kerogen with the bedding plane, the minimum acceptable representation of the anisotropy of naturally fractured shale-gas reservoirs is orthotropy, in which a set of vertical compliant fractures is embedded in a vertical transverse isotropic (VTI) background medium. Full-azimuth seismic data are required to characterize such reservoirs and to invert for the anisotropic elastic properties. Orthotropic inversion uses azimuthally sectored seismic data stacked according to the incident angle. Even for high-fold acquisition, this azimuth/angle grouping can result in low-fold angle stacks. Orthotropic amplitude-variation-with-offset-and-azimuth (AVOAz) inversion requires seismic preconditioning techniques that ensure proper primary amplitude preservation, noise attenuation, and data alignment, and a workflow implemented for the construction of an orthotropic rock-physics model. This model integrates well and core data to estimate reservoir properties using the results of the AVOAz inversion. The seismic inversion results include the P- and S-impedance and parameters quantifying the azimuthal anisotropy. The rock model assumes a VTI kerogen-rich layer, containing aligned vertical fractures, and it uses prestack orthotropic AVOAz inversion results to predict porosity, TOC, and fracture intensity.

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