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

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.

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Introduction to this special section: Rock physics

The Leading Edge ◽

10.1190/tle38050332.1 ◽

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.

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Integrated VTI model building with seismic data, geologic information, and rock-physics modeling — Part 2: Field data test

Geophysics ◽

10.1190/geo2015-0593.1 ◽

2016 ◽

Vol 81 (5) ◽

pp. C205-C218 ◽

Cited By ~ 6

Author(s):

Yunyue Li ◽

Biondo Biondi ◽

Robert Clapp ◽

Dave Nichols

Keyword(s):

Seismic Data ◽

Model Building ◽

Structural Information ◽

Rock Physics ◽

Velocity Model ◽

Seismic Inversion ◽

Well Logs ◽

Velocity Model Building ◽

Rock Physics Modeling ◽

Physics Modeling

Velocity model building is the first step of seismic inversion and the foundation of the subsequent processing and interpretation workflow. Velocity model building from surface seismic data only becomes severely underdetermined and nonunique when more than one parameter is needed to characterize the velocity anisotropy. The traditional seismic processing workflow sequentially performs seismic velocity model building, structural imaging/interpretation, and lithologic inversion, modifying the subsurface model in each step without verifications against the previously used data. We have developed an integrated model building scheme that uses all available information: seismic data, geologic structural information, well logs, and rock-physics knowledge. We have evaluated the accuracy of the anisotropic model in the image space, in which structural information is estimated. The lithologic inversion results from well logs and the dynamic seismic information (amplitude versus angle) are also fed back to the kinematic seismic inversion via a cross-parameter covariance matrix, which is a multivariate Gaussian approximation to the numerical distribution modeled from stochastic rock-physics modeling. The procedure of building the rock-physics prior information and the improvements using these extra constraints were tested on a Gulf of Mexico data set. The inverted vertical transverse isotropic model not only better focused the seismic image, but it also satisfied the geologic and rock-physics principles.

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Integrated VTI model building with seismic data, geologic information, and rock-physics modeling — Part 1: Theory and synthetic test

Geophysics ◽

10.1190/geo2015-0592.1 ◽

2016 ◽

Vol 81 (5) ◽

pp. C177-C191 ◽

Cited By ~ 11

Author(s):

Yunyue Li ◽

Biondo Biondi ◽

Robert Clapp ◽

Dave Nichols

Keyword(s):

Seismic Data ◽

Seismic Anisotropy ◽

Model Building ◽

Rock Physics ◽

Seismic Inversion ◽

Additional Information ◽

Synthetic Test ◽

Rock Physics Modeling ◽

Physics Modeling ◽

Geologic Information

Seismic anisotropy plays an important role in structural imaging and lithologic interpretation. However, anisotropic model building is a challenging underdetermined inverse problem. It is well-understood that single component pressure wave seismic data recorded on the upper surface are insufficient to resolve a unique solution for velocity and anisotropy parameters. To overcome the limitations of seismic data, we have developed an integrated model building scheme based on Bayesian inference to consider seismic data, geologic information, and rock-physics knowledge simultaneously. We have performed the prestack seismic inversion using wave-equation migration velocity analysis (WEMVA) for vertical transverse isotropic (VTI) models. This image-space method enabled automatic geologic interpretation. We have integrated the geologic information as spatial model correlations, applied on each parameter individually. We integrate the rock-physics information as lithologic model correlations, bringing additional information, so that the parameters weakly constrained by seismic are updated as well as the strongly constrained parameters. The constraints provided by the additional information help the inversion converge faster, mitigate the ambiguities among the parameters, and yield VTI models that were consistent with the underlying geologic and lithologic assumptions. We have developed the theoretical framework for the proposed integrated WEMVA for VTI models and determined the added information contained in the regularization terms, especially the rock-physics constraints.

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Petrophysical Properties of Nahr Umar Formation in Nasiriya Oil Field

Iraqi Journal of Chemical and Petroleum Engineering ◽

10.31699/ijcpe.2020.3.2 ◽

2020 ◽

Vol 21 (3) ◽

pp. 9-18

Author(s):

Ahmed Abdulwahhab Suhail ◽

Mohammed H. Hafiz ◽

Fadhil S. Kadhim

Keyword(s):

Gamma Ray ◽

Water Saturation ◽

Oil Field ◽

Well Logs ◽

High Porosity ◽

Reservoir Properties ◽

Petrophysical Parameters ◽

Resistivity Logs ◽

Lithology Prediction ◽

Porosity And Permeability

Petrophysical characterization is the most important stage in reservoir management. The main purpose of this study is to evaluate reservoir properties and lithological identification of Nahr Umar Formation in Nasiriya oil field. The available well logs are (sonic, density, neutron, gamma-ray, SP, and resistivity logs). The petrophysical parameters such as the volume of clay, porosity, permeability, water saturation, were computed and interpreted using IP4.4 software. The lithology prediction of Nahr Umar formation was carried out by sonic -density cross plot technique. Nahr Umar Formation was divided into five units based on well logs interpretation and petrophysical Analysis: Nu-1 to Nu-5. The formation lithology is mainly composed of sandstone interlaminated with shale according to the interpretation of density, sonic, and gamma-ray logs. Interpretation of formation lithology and petrophysical parameters shows that Nu-1 is characterized by low shale content with high porosity and low water saturation whereas Nu-2 and Nu-4 consist mainly of high laminated shale with low porosity and permeability. Nu-3 is high porosity and water saturation and Nu-5 consists mainly of limestone layer that represents the water zone.

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

10.1190/1.2147927 ◽

2005 ◽

Author(s):

Ron McWhorter ◽

Duane Pierce ◽

Niranjan Banik ◽

Haibin Xu ◽

George Bunge ◽

...

Keyword(s):

Rock Physics ◽

Seismic Inversion ◽

East Texas ◽

Reservoir Properties

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

Geophysics ◽

10.1190/1.3478209 ◽

2010 ◽

Vol 75 (5) ◽

pp. 75A165-75A176 ◽

Cited By ~ 203

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.

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

Geophysics ◽

10.1190/1.3386676 ◽

2010 ◽

Vol 75 (3) ◽

pp. O21-O37 ◽

Cited By ~ 194

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.

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

Bulletin of the Geological Society of Malaysia ◽

10.7186/bgsm70202016 ◽

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.

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Hartha Formation divisions Based on Well Logs Analysis in Majnoon Oil Field, Southern Iraq

Iraqi Journal of Science ◽

10.24996/ijs.2020.61.6.14 ◽

2020 ◽

pp. 1362-1369

Author(s):

Gheed Chaseb ◽

Thamer A. Mahdi

Keyword(s):

Water Saturation ◽

Effective Porosity ◽

Oil Field ◽

Well Logs ◽

Petrophysical Properties ◽

Reservoir Properties ◽

Thin Sections ◽

Facies Associations ◽

Log Interpretation ◽

Well Logs Interpretation

This study aims to evaluate reservoir characteristics of Hartha Formation in Majnoon oil field based on well logs data for three wells (Mj-1, Mj-3 and Mj-11). Log interpretation was carried out by using a full set of logs to calculate main petrophysical properties such as effective porosity and water saturation, as well as to find the volume of shale. The evaluation of the formation included computer processes interpretation (CPI) using Interactive Petrophysics (IP) software. Based on the results of CPI, Hartha Formation is divided into five reservoir units (A1, A2, A3, B1, B2), deposited in a ramp setting. Facies associations is added to well logs interpretation of Hartha Formation, and was inferred by a microfacies analysis of thin sections from core and cutting samples. The CPI shows that the A2 is the main oil- bearing unit, which is characterized by good reservoir properties, as indicated by high effective porosity, low water saturation, and low shale volume. Less important units include A1 and A3, because they have low petrophysical properties compared to the unit A2.

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