Stochastic vs Deterministic Pre-stack Inversion Methods. Brian Russell
|
|
- Aubrey Davidson
- 6 years ago
- Views:
Transcription
1 Stochastic vs Deterministic Pre-stack Inversion Methods Brian Russell
2 Introduction Seismic reservoir analysis techniques utilize the fact that seismic amplitudes contain information about the geological properties of the reservoir. The mathematics behind this observation was developed in the early 1900s, but its application to exploration seismic data did not start until the 1970s. We classify these methods into two categories: methods that analyze only the amplitudes, and methods that invert the amplitudes to reservoir properties. Newer methods analyze pre-stack data, where the analysis of the amplitudes without inversion is called Amplitude versus Offset, or AVO. Pre-stack inversion has many forms, where the major division is between deterministic and stochastic, or geostatistical, methods. In this talk I will discuss these methods and look at their assumptions and limitations.
3 A suggested workflow Well Log Data Seismic Data V P, ρ V P, V S, ρ Modeling for V S Build rock physics model Post-stack only Post-stack inversion Gathers, only offsets AVO & Prestack inversion Gathers with azimuths AVAz / Fracture Identification Integrate using multivariate or Bayesian statistics
4 Seismic Inversion Methods Inversion methods Post-stack Pre-stack Model Based Recursive Sparse spike Colored Elastic Impedance LMR Simultaneous Joint PP/PS Inversion 4D Inversion Azimuthal Inversion Stochastic / Geostatistical Inversion
5 The basic model for inversion The zero offset, or stacked, seismic trace can be modeled as the convolution of the acoustic impedance (AI) reflectivity with the wavelet. As shown in the next slide, this is the basis for post-stack inversion. Acoustic Impedance AI = ρvp Reflectivity AI R AI = 2AI Wavelet W Seismic W S = W * R AI
6 Post-stack inversion Post-stack seismic Inversion, developed in the 1970s, reverses the forward modeling procedure, allowing us to derive the impedance from the reflectivity: Inverse Wavelet 6 Impedance Reflectivity Seismic
7 Qualitative AVO In the 1980s, geophysicists observed that the amplitudes in a seismic gather could be written in linearized form using the amplitude versus offset (AVO) equation, a reformulation of the Aki-Richards linearized solution to the Zoeppritz equations: Note that this has added two extra terms to the zero-offset case, a gradient term G and a curvature term C, often referred to as A, B and C, where the term A is called the intercept. This formed the basis to what I refer to as qualitative AVO. where :, sin tan sin ) ( θ θ θ θ C G R R AI P + + =. 2 and, 2 4 2, P P P S S S P S P P P P AI V V C V V V V V V V V G V V R = = + = ρ ρ ρ ρ
8 Intercept and gradient analysis The amplitudes are extracted at all times, two of which are shown here: Offset or Angle θ +R AI +G sin 2 θ Time The AVO equation predicts a linear relationship between these amplitudes and sin 2 θ. Regression curves are calculated to give R AI and G values for each time sample. -R AI - G
9 Using the angle gathers for inversion Fatti et al. (1994) re-formulated this equation to show that the pre-stack seismic data is a function of the acoustic impedance reflectivity (R AI ), shear impedance reflectivity (R SI ) and density reflectivity (R D ) term: R P( θ ) = arai + brsi + crd, where R AI AI VS ρ SI =, RSI = + =, SI = ρvs, RD 2AI 2V 2ρ 2SI S = ρ, 2ρ a = 1+ tan 2 θ, b V = 8 V S P 2 sin 2 θ, and c = V 4 V S P 2 sin 2 θ tan 2 θ.
10 Independent pre-stack inversion Independent pre-stack inversion is implemented by first extracting the reflectivity components, and then inverting them separately. To estimate the reflectivities, the amplitudes at each time t in an N- trace angle gather are picked as shown here, to give R P (θ 1 ) R P (θ N ): We can then solve for the reflectivities at each time sample using least-squares inversion. Finally, these estimates are inverted using a post-stack type scheme. Time (ms) t R R R AI SI D Reflectivities Angle 1 N Generalized inverse weight = matrix 1 R R P P ( θ 1 ) ( θ ) N Observations
11 Simultaneous Pre-stack Seismic inversion Pre-stack inversion is also based on an extension of the Fatti formulation of the Aki-Richards equation: S( θ ) = c1w ( θ ) DLP + c2w ( θ ) DLS + c3w ( θ ) DLD, where : S(θ) = seismic trace at angleθ, L, L, L = logarithms of Z, Z W(θ) = P S, and ρ, the extracted wavelet at angleθ, and D is the derivative operation. D P P As in our discussion of AVO and independent inversion, this can again be set up as a least-squares problem: model parameters = generalized inverse x observations As we discussed earlier, there are two main types of pre-stack inversion, deterministic and stochastic.
12 Deterministic vs Stochastic Inversion First of all, let us define the fundamental difference between deterministic and stochastic inversion: In deterministic inversion we produce what we consider to be a single best solution. In stochastic inversion we produce many possible solutions, all plausible, which average to the deterministic solution. The advantage of deterministic inversion is that we get the best leastsquares solution to our problem. The advantages of stochastic inversion are its higher frequency nature and the calculation of uncertainty.
13 Deterministic pre-stack inversion example On the next slide, I will show an example of deterministic pre-stack inversion. A Gulf Coast dataset (shown on the left of the slide) was inverted for P- impedance, S-impedance and density (which are shown on the right). The inverted volumes were transformed to Vshale, porosity and Sw (also shown on the right of the slide). Our assumption is that each inverted or transformed result is the correct answer. However, this will not allow us to obtain uncertainty estimates from of the rock properties.
14 Deterministic pre-stack inversion example Seismic Amplitude Map Inverted Acoustic Impedance
15 Deterministic pre-stack inversion example Seismic Amplitude Map Inverted Inverted Acoustic Shear Impedance Impedance
16 Deterministic pre-stack inversion example Seismic Amplitude Map Inverted Inverted Acoustic Inverted Shear Impedance Density Impedance
17 Deterministic pre-stack inversion example Seismic Amplitude Map Derived V shale Map Inverted Inverted Acoustic Inverted Derived Shear Impedance Density Vshale Impedance
18 Deterministic pre-stack inversion example Seismic Amplitude Map Derived V shale Map Inverted Inverted Acoustic Inverted Derived Shear Impedance Density Porosity Vshale Impedance
19 Deterministic pre-stack inversion example Seismic Amplitude Map Derived V shale Map Inverted Inverted Acoustic Inverted Derived Shear Impedance Density Porosity Vshale Sw Impedance
20 Stochastic inversion In stochastic inversion, the least-squares inversion method is extended by formulating the problem using a Gaussian or Log Gaussian posterior probability density function, or pdf (Tarantola, 1987). This allows us to sample various scenarios from the pdf using the Monte Carlo (MC) or Markov Chain Monte Carlo (MCMC) approach. The earliest approach to stochastic inversion was by Haas and Dubrule, 1994, in which Sequential Gaussian Simulation (SGS) is used. Buland and Omre (2003) developed a fast approach to stochastic linearized inversion which utilized a Gaussian pdf. The GeoSI method that I will discuss today combines both a Gaussian pdf and the SGS approach (Doyen, Williamson et al., 2007) My colleague Ali Tehrani discussed the Jason StatMod approach yesterday.
21 Geostatistical inversion (Haas and Dubrulle) Actual seismic trace Adapted from Dubrule, 2003 (x,y) Best simulated synthetic trace wavelet AI simulations Populate model with AI data at wells Define a random path through all (x,y) trace locations At each trace location perform a local optimization Generate a large number of trial AI sequences using SGS with spatial and vertical variograms. Compute reflectivity series and convolve with extracted wavelet. Compute misfit against observed seismic. Retain best matching AI (ρ >0.8). Go to next trace
22 Variogram models Vertical (temporal) variogram Horizontal variogram map showing anisotropy Anisotropic variograms in principal directions Here are the variograms computed by Haas and Dubrulle (1994), showing the vertical, or temporal change, and the horizontal change including anisotropy.
23 Bayesian stochastic inversion Although geostatistical stochastic inversion produces reasonable results, it has two limitations: It is quite slow. It has difficulty in converging to an answer. Buland and Omre (2003) introduced a new type of stochastic inversion which was based on multivariate Bayesian statistics. To illustrate the concept of Bayesian statistics, I will first consider the standard least-squares regression problem. We will then look at the general theory proposed by Buland and Omre. We will then extend this method by combining it with SGS.
24 Least-squares regression Consider a regression fit to 16 measured porosity values (φ i ) plotted against seismic impedance (z i ), shown by the red line in the plot. This can be written: φi = a + bz i The regression line is the least-squares fit between porosity and impedance and is considered the right answer, even though only one point falls on it.
25 Statistical interpretation In the statistical interpretation of this crossplot, each variable (porosity and impedance) is given as a Gaussian probability distribution function (pdf) defined by its mean (µ) and variance (σ). p(φ) µ φ σ φ p(z) σ z Joint pdf p(φ,z) µ z The joint pdf p(φ,z) is the probability of φ and z occurring, and is defined by the variances and means, as well as the covariance between φ and z.
26 Bayesian regression Bayesian statistics tells us that the conditional probability of φ given z, or the posterior, equals the joint probability divided by the probability of z, or the prior. p( φ z) = p( φ, z) p( z) σ φ µ φ z conditional pdf p(φ z) σ φ z The conditional mean µ φ z is the least-squares fit, and the conditional variance σ φ z gives us the scatter in this fit. Note it is narrower than p(φ).
27 Bayesian stochastic inversion Generalizing the previous example to inversion, Buland and Omre (2003) showed that: where: µ C m d m and : µ = model T 1 1 = C ( G C d + C µ ) m d m d d m m = conditional mean, C C m d = ( G covariance, T C 1 d G + C 1 m = conditional This equation reduces to the least-squares solution if we assume that µ m = 0, and C d = σ d2 I: T 2 1 mˆ = µ m d = ( G G + σ dcm ) d µ ) = data m 1 = model covariance mean (prior), 1 G covariance. T, d
28 GeoSI The GeoSI method, as implemented by CGG and ported to the Hampson- Russell suite of software, involves the following steps: Build a stratigraphic grid using horizons, well logs and layer-based kriging. Bring in partial angle stacks and wavelets. Compute the Bayesian posterior distribution by combining the model, seismic data and well logs. Create multiple P and S-impedance realizations using the SGS technique. Compute the mean and standard deviations from the impedance realizations. These steps are shown diagrammatically on the next two slides.
29 Building the stratigraphic grid Stratigraphic grid Ip Horizons in time Layer-based Kriging Is Stratigraphic layering style Low-pass filtering Well logs in time (Vp, Vs, Density) Low-frequency prior model in stratigraphic grid R. Moyen and J. Frelet
30 Stochastic Inversion Workflow Partial angle stack seismic cubes n Ip-Is realisations Well logs (Vp, Vs, Density) Well uncertainty Bayesian stochastic inversion time AI Ip-Is prior mean & standard deviation in stratigraphic grid Horizontal & vertical variograms R. Moyen and J. Frelet Posterior mean & standard deviation Ip-Is
31 Bandwidth components For all inversion methods, the prior model is constructed by interpolating filtered logs, and controls low frequencies. For both deterministic and stochastic inversion, the seismic amplitudes control intermediate frequencies within the seismic bandwidth. In stochastic inversion, the vertical variogram model controls the high frequencies. Prior model Seismic Variogram model Power Spectrum Frequency (Hz) Adapted from Moyen and Frelet
32 Offshore West Africa example Elastic inversion (Ip-Is) 3 seismic angle stacks ,000 traces Time window of 200 ms 132 layers in grid 500 realisations (59 Gb total) 3 wells with Vp, Vs and density logs Computations on standard workstation Courtesy of R. Moyen and J. Frelet
33 Ip-Is Prior Model P Impedance 200 ms m/s x g/cm 3 S Impedance Courtesy of R. Moyen and J. Frelet m/s x g/cm 3
34 Ip-Is Realisations P Impedance 200 ms 5200 m/s x g/cm S Impedance Courtesy of R. Moyen and J. Frelet 2200 m/s x g/cm3 4200
35 Ip Posterior Mean and Standard Deviation P Impedance mean 200 ms m/s x g/cm 3 P Impedance std. dev. Courtesy of R. Moyen and J. Frelet σ Ip (km/s. g/cm 3 )
36 Vp/Vs Mean vs Realisations 1.5 Vp/Vs 2.5 Mean Realisation Posterior mean Sand/shale Cutoff Realisation
37 Inversion Results Vp/Vs Ratio Mean of 500 realisations Vp/Vs Courtesy of R. Moyen and J. Frelet
38 Using the realizations One of the key questions about stochastic inversion is: what do we do with all the realizations? In other words, wouldn t a single answer (i.e. the deterministic solution) be better? The answer is that with multiple realizations we can generate a number of new results, such as: Seismic lithology prediction. Facies classification. Volumetric uncertainty analysis. Petrophysical property analysis. These concepts are illustrated in the next few slides.
39 Stochastic Lithology Prediction N sand / shale simulations Is Ip Sand probability cube N realisations of Ip, Is Histogram of sand volume Courtesy of R. Moyen and J. Frelet
40 Facies Discrimination 0.5 Poisson s ratio Ip/Is Courtesy of R. Moyen and J. Frelet VSH P Impedance
41 Individual Realisations Courtesy of R. Moyen and J. Frelet
42 Histogram of sand volume For each realization, we can compute the sand volume from the number of cells with sand. This can then be arranged in histogram format, and the probability percentiles can be computed. A percentile is computed from the total area under the probability curve. Note that the percentile maps do not indicate a higher probability of sand, only where the map falls within the distribution. As shown by an earlier slide, the percentile values will in general be larger than mean computation. These concepts are illustrated in the next few slides.
43 Histogram of Sand Volume from Realizations 40 P50 Number of realisations P10 P90 0 Sand volume P10 P50 P90 Ranked lithology simulations
44 Sand Volume from Realisations and Mean 40 P50 Number of realisations Sand volume from inversion mean P10 P90 0 Sand volume
45 Connected Sand Geo-bodies Geobodies connected to at least one well Courtesy of R. Moyen and J. Frelet Color-code: geobody volume (only largest are displayed)
46 Facies Probability from Stochastic Inversion Sand probability Large volume but small probability Courtesy of R. Moyen and J. Frelet Smaller volume but high probability
47 Stochastic Petrophysical Modelling Multiple I p & I s models Multiple V sh and Φ models Φ I p Statistical petro-elastic calibration Courtesy of R. Moyen and J. Frelet
48 Geostatistical inversion vs other modelling techniques Geostatistical reservoir modeling Interpolate between the wells Plausible details Accurate near wells Not elsewhere Deterministic seismic inversion Optimize P-Impedance to minimize synthetic-to-seismic misfit Accurate within seismic bandwidth Unrealistically smooth Only one possibility StatMod/GeoSI geostatistical seismic inversion Subsumes geostatistical modeling and deterministic inversion Does both, simultaneously and in a statistically rigorous way Multiple plausible realizations at high detail (e.g. 1ms 25m) Yet also coherent interpretations of the seismic up to the km scale
49 Conclusions Stochastic inversion is a natural extension of deterministic inversion (mean of realizations deterministic inversion) But it can provide extra information, such as: Lithology probability Facies distribution Volumetrics Petrophysical parameters Our case study focussed on a channel sand play from West Africa.
50 Thank You
Reservoir connectivity uncertainty from stochastic seismic inversion Rémi Moyen* and Philippe M. Doyen (CGGVeritas)
Rémi Moyen* and Philippe M. Doyen (CGGVeritas) Summary Static reservoir connectivity analysis is sometimes based on 3D facies or geobody models defined by combining well data and inverted seismic impedances.
More informationQUANTITATIVE INTERPRETATION
QUANTITATIVE INTERPRETATION THE AIM OF QUANTITATIVE INTERPRETATION (QI) IS, THROUGH THE USE OF AMPLITUDE ANALYSIS, TO PREDICT LITHOLOGY AND FLUID CONTENT AWAY FROM THE WELL BORE This process should make
More informationFifteenth International Congress of the Brazilian Geophysical Society. Copyright 2017, SBGf - Sociedade Brasileira de Geofísica
Geostatistical Reservoir Characterization in Barracuda Field, Campos Basin: A case study Frank Pereira (CGG)*, Ted Holden (CGG), Mohammed Ibrahim (CGG) and Eduardo Porto (CGG). Copyright 2017, SBGf - Sociedade
More informationAn overview of AVO and inversion
P-486 An overview of AVO and inversion Brian Russell, Hampson-Russell, CGGVeritas Company Summary The Amplitude Variations with Offset (AVO) technique has grown to include a multitude of sub-techniques,
More informationDownloaded 10/02/18 to Redistribution subject to SEG license or copyright; see Terms of Use at
Multi-scenario, multi-realization seismic inversion for probabilistic seismic reservoir characterization Kester Waters* and Michael Kemper, Ikon Science Ltd. Summary We propose a two tiered inversion strategy
More informationStatistical Rock Physics
Statistical - Introduction Book review 3.1-3.3 Min Sun March. 13, 2009 Outline. What is Statistical. Why we need Statistical. How Statistical works Statistical Rock physics Information theory Statistics
More information3D geostatistical porosity modelling: A case study at the Saint-Flavien CO 2 storage project
3D geostatistical porosity modelling: A case study at the Saint-Flavien CO 2 storage project Maxime Claprood Institut national de la recherche scientifique, Québec, Canada Earth Modelling 2013 October
More informationQuantitative Interpretation
Quantitative Interpretation The aim of quantitative interpretation (QI) is, through the use of amplitude analysis, to predict lithology and fluid content away from the well bore. This process should make
More informationHampsonRussell. A comprehensive suite of reservoir characterization tools. cgg.com/geosoftware
HampsonRussell A comprehensive suite of reservoir characterization tools cgg.com/geosoftware HampsonRussell Software World-class geophysical interpretation HampsonRussell Software is a comprehensive suite
More informationSEISMIC INVERSION OVERVIEW
DHI CONSORTIUM SEISMIC INVERSION OVERVIEW Rocky Roden September 2011 NOTE: Terminology for inversion varies, depending on the different contractors and service providers, emphasis on certain approaches,
More information23855 Rock Physics Constraints on Seismic Inversion
23855 Rock Physics Constraints on Seismic Inversion M. Sams* (Ikon Science Ltd) & D. Saussus (Ikon Science) SUMMARY Seismic data are bandlimited, offset limited and noisy. Consequently interpretation of
More informationReservoir Characterization using AVO and Seismic Inversion Techniques
P-205 Reservoir Characterization using AVO and Summary *Abhinav Kumar Dubey, IIT Kharagpur Reservoir characterization is one of the most important components of seismic data interpretation. Conventional
More informationAFI (AVO Fluid Inversion)
AFI (AVO Fluid Inversion) Uncertainty in AVO: How can we measure it? Dan Hampson, Brian Russell Hampson-Russell Software, Calgary Last Updated: April 2005 Authors: Dan Hampson, Brian Russell 1 Overview
More informationWe LHR3 04 Realistic Uncertainty Quantification in Geostatistical Seismic Reservoir Characterization
We LHR3 04 Realistic Uncertainty Quantification in Geostatistical Seismic Reservoir Characterization A. Moradi Tehrani* (CGG), A. Stallone (Roma Tre University), R. Bornard (CGG) & S. Boudon (CGG) SUMMARY
More informationIntegrating rock physics modeling, prestack inversion and Bayesian classification. Brian Russell
Integrating rock physics modeling, prestack inversion and Bayesian classification Brian Russell Introduction Today, most geoscientists have an array of tools available to perform seismic reservoir characterization.
More informationSensitivity Analysis of Pre stack Seismic Inversion on Facies Classification using Statistical Rock Physics
Sensitivity Analysis of Pre stack Seismic Inversion on Facies Classification using Statistical Rock Physics Peipei Li 1 and Tapan Mukerji 1,2 1 Department of Energy Resources Engineering 2 Department of
More informationComparative Study of AVO attributes for Reservoir Facies Discrimination and Porosity Prediction
5th Conference & Exposition on Petroleum Geophysics, Hyderabad-004, India PP 498-50 Comparative Study of AVO attributes for Reservoir Facies Discrimination and Porosity Prediction Y. Hanumantha Rao & A.K.
More informationReliability of Seismic Data for Hydrocarbon Reservoir Characterization
Reliability of Seismic Data for Hydrocarbon Reservoir Characterization Geetartha Dutta (gdutta@stanford.edu) December 10, 2015 Abstract Seismic data helps in better characterization of hydrocarbon reservoirs.
More informationAccurate reservoir modelling through optimized integration of geostatistical inversion and flow simulation. A North Sea case study.
Accurate reservoir modelling through optimized integration of geostatistical inversion and flow simulation. A North Sea case study. A. Castoro 1, L de Groot 2, D. Forsyth 3 & R. Maguire 1 1 Fugro-Jason
More informationP191 Bayesian Linearized AVAZ Inversion in HTI Fractured Media
P9 Bayesian Linearized AAZ Inversion in HI Fractured Media L. Zhao* (University of Houston), J. Geng (ongji University), D. Han (University of Houston) & M. Nasser (Maersk Oil Houston Inc.) SUMMARY A new
More informationMultiple Scenario Inversion of Reflection Seismic Prestack Data
Downloaded from orbit.dtu.dk on: Nov 28, 2018 Multiple Scenario Inversion of Reflection Seismic Prestack Data Hansen, Thomas Mejer; Cordua, Knud Skou; Mosegaard, Klaus Publication date: 2013 Document Version
More informationSeismic reservoir characterization in offshore Nile Delta.
Seismic reservoir characterization in offshore Nile Delta. Part II: Probabilistic petrophysical-seismic inversion M. Aleardi 1, F. Ciabarri 2, B. Garcea 2, A. Mazzotti 1 1 Earth Sciences Department, University
More informationGeostatistics for Seismic Data Integration in Earth Models
2003 Distinguished Instructor Short Course Distinguished Instructor Series, No. 6 sponsored by the Society of Exploration Geophysicists European Association of Geoscientists & Engineers SUB Gottingen 7
More informationQuantitative Seismic Interpretation An Earth Modeling Perspective
Quantitative Seismic Interpretation An Earth Modeling Perspective Damien Thenin*, RPS, Calgary, AB, Canada TheninD@rpsgroup.com Ron Larson, RPS, Calgary, AB, Canada LarsonR@rpsgroup.com Summary Earth models
More informationLithology prediction and fluid discrimination in Block A6 offshore Myanmar
10 th Biennial International Conference & Exposition P 141 Lithology prediction and fluid discrimination in Block A6 offshore Myanmar Hanumantha Rao. Y *, Loic Michel, Hampson-Russell, Kyaw Myint, Ko Ko,
More informationThe GIG consortium Geophysical Inversion to Geology Per Røe, Ragnar Hauge, Petter Abrahamsen FORCE, Stavanger
www.nr.no The GIG consortium Geophysical Inversion to Geology Per Røe, Ragnar Hauge, Petter Abrahamsen FORCE, Stavanger 17. November 2016 Consortium goals Better estimation of reservoir parameters from
More informationSeismic inversion for reservoir properties combining statistical rock physics and geostatistics: A review
GEOPHYSICS, VOL. 75, NO. 5 SEPTEMBER-OCTOBER 2010 ; P. 75A165 75A176, 8 FIGS. 10.1190/1.3478209 Seismic inversion for reservoir properties combining statistical rock physics and geostatistics: A review
More informationPorosity prediction using cokriging with multiple secondary datasets
Cokriging with Multiple Attributes Porosity prediction using cokriging with multiple secondary datasets Hong Xu, Jian Sun, Brian Russell, Kris Innanen ABSTRACT The prediction of porosity is essential for
More informationURTeC: Summary
URTeC: 2665754 Using Seismic Inversion to Predict Geomechanical Well Behavior: a Case Study From the Permian Basin Simon S. Payne*, Ikon Science; Jeremy Meyer*, Ikon Science Copyright 2017, Unconventional
More informationIJMGE Int. J. Min. & Geo-Eng. Vol.49, No.1, June 2015, pp
IJMGE Int. J. Min. & Geo-Eng. Vol.49, No.1, June 2015, pp.131-142 Joint Bayesian Stochastic Inversion of Well Logs and Seismic Data for Volumetric Uncertainty Analysis Moslem Moradi 1, Omid Asghari 1,
More informationAVAZ and VVAZ practical analysis to estimate anisotropic properties
AVAZ and VVAZ practical analysis to estimate anisotropic properties Yexin Liu*, SoftMirrors Ltd., Calgary, Alberta, Canada yexinliu@softmirrors.com Summary Seismic anisotropic properties, such as orientation
More informationA031 Porosity and Shale Volume Estimation for the Ardmore Field Using Extended Elastic Impedance
A31 Porosity and Shale Volume Estimation for the Ardmore Field Using Extended Elastic Impedance A.M. Francis* (Earthworks Environment & Resources Ltd) & G.J. Hicks (Earthworks Environment & Resources Ltd)
More informationReservoir properties inversion from AVO attributes
Reservoir properties inversion from AVO attributes Xin-gang Chi* and De-hua Han, University of Houston Summary A new rock physics model based inversion method is put forward where the shaly-sand mixture
More informationSimultaneous Inversion of Pre-Stack Seismic Data
6 th International Conference & Exposition on Petroleum Geophysics Kolkata 006 Summary Simultaneous Inversion of Pre-Stack Seismic Data Brian H. Russell, Daniel P. Hampson, Brad Bankhead Hampson-Russell
More informationLinearized AVO and Poroelasticity for HRS9. Brian Russell, Dan Hampson and David Gray 2011
Linearized AO and oroelasticity for HR9 Brian Russell, Dan Hampson and David Gray 0 Introduction In this talk, we combine the linearized Amplitude ariations with Offset (AO) technique with the Biot-Gassmann
More informationIntegration of Rock Physics Models in a Geostatistical Seismic Inversion for Reservoir Rock Properties
Integration of Rock Physics Models in a Geostatistical Seismic Inversion for Reservoir Rock Properties Amaro C. 1 Abstract: The main goal of reservoir modeling and characterization is the inference of
More informationThe reason why acoustic and shear impedances inverted
SPECIAL The Rocky SECTION: Mountain The Rocky region Mountain region Comparison of shear impedances inverted from stacked PS and SS data: Example from Rulison Field, Colorado ELDAR GULIYEV, Occidental
More informationThomas Bayes versus the wedge model: An example inference using a geostatistical prior function
Thomas Bayes versus the wedge model: An example inference using a geostatistical prior function Jason M. McCrank, Gary F. Margrave, and Don C. Lawton ABSTRACT The Bayesian inference is used to estimate
More informationRC 2.7. Main Menu. SEG/Houston 2005 Annual Meeting 1355
Thierry Coléou, Fabien Allo and Raphaël Bornard, CGG; Jeff Hamman and Don Caldwell, Marathon Oil Summary We present a seismic inversion method driven by a petroelastic model, providing fine-scale geological
More informationPetrophysical seismic inversion conditioned to well-log data: Methods and application to a gas reservoir
GEOPHYSICS, VOL. 4, NO. MARCH-APRIL 009 ; P. O1 O1, 11 FIGS. 10.1190/1.30439 Petrophysical seismic inversion conditioned to well-log data: Methods and application to a gas reservoir Miguel Bosch 1, Carla
More informationFred Mayer 1; Graham Cain 1; Carmen Dumitrescu 2; (1) Devon Canada; (2) Terra-IQ Ltd. Summary
2401377 Statistically Improved Resistivity and Density Estimation From Multicomponent Seismic Data: Case Study from the Lower Cretaceous McMurray Formation, Athabasca Oil Sands Fred Mayer 1; Graham Cain
More informationNew Frontier Advanced Multiclient Data Offshore Uruguay. Advanced data interpretation to empower your decision making in the upcoming bid round
New Frontier Advanced Multiclient Data Offshore Uruguay Advanced data interpretation to empower your decision making in the upcoming bid round Multiclient data interpretation provides key deliverables
More informationWe apply a rock physics analysis to well log data from the North-East Gulf of Mexico
Rock Physics for Fluid and Porosity Mapping in NE GoM JACK DVORKIN, Stanford University and Rock Solid Images TIM FASNACHT, Anadarko Petroleum Corporation RICHARD UDEN, MAGGIE SMITH, NAUM DERZHI, AND JOEL
More informationBayesian Lithology-Fluid Prediction and Simulation based. on a Markov Chain Prior Model
Bayesian Lithology-Fluid Prediction and Simulation based on a Markov Chain Prior Model Anne Louise Larsen Formerly Norwegian University of Science and Technology, N-7491 Trondheim, Norway; presently Schlumberger
More informationEstimation of density from seismic data without long offsets a novel approach.
Estimation of density from seismic data without long offsets a novel approach. Ritesh Kumar Sharma* and Satinder Chopra Arcis seismic solutions, TGS, Calgary Summary Estimation of density plays an important
More informationIntroduction: Simultaneous AVO Inversion:
Implementation of AVO, AVOAz Inversion and Ant Tracking Techniques in Wembley Valhalla Integrated Merge 3D Seismic Survey, Alberta Homayoun Gerami, Patty Evans WesternGeco Introduction: The Wembley Valhalla
More informationQuantitative interpretation using inverse rock-physics modeling on AVO data
Quantitative interpretation using inverse rock-physics modeling on AVO data Erling Hugo Jensen 1, Tor Arne Johansen 2, 3, 4, Per Avseth 5, 6, and Kenneth Bredesen 2, 7 Downloaded 08/16/16 to 129.177.32.62.
More informationA Petroleum Geologist's Guide to Seismic Reflection
A Petroleum Geologist's Guide to Seismic Reflection William Ashcroft WILEY-BLACKWELL A John Wiley & Sons, Ltd., Publication Contents Preface Acknowledgements xi xiii Part I Basic topics and 2D interpretation
More informationA seismic reservoir characterization and porosity estimation workflow to support geological model update: pre-salt reservoir case study, Brazil
A seismic reservoir characterization and porosity estimation workflow to support geological model update: pre-salt reservoir case study, Brazil Laryssa Oliveira 1*, Francis Pimentel 2, Manuel Peiro 1,
More informationUse of Seismic Inversion Attributes In Field Development Planning
IOSR Journal of Applied Geology and Geophysics (IOSR-JAGG) e-issn: 2321 0990, p-issn: 2321 0982.Volume 6, Issue 2 Ver. II (Mar. Apr. 2018), PP 86-92 www.iosrjournals.org Use of Seismic Inversion Attributes
More informationRock Physics and Quantitative Wavelet Estimation. for Seismic Interpretation: Tertiary North Sea. R.W.Simm 1, S.Xu 2 and R.E.
Rock Physics and Quantitative Wavelet Estimation for Seismic Interpretation: Tertiary North Sea R.W.Simm 1, S.Xu 2 and R.E.White 2 1. Enterprise Oil plc, Grand Buildings, Trafalgar Square, London WC2N
More informationThe progressive role of Quantitative Seismic Interpretation Unlocking subsurface opportunities From qualitative to quantitative
The progressive role of Quantitative Seismic Interpretation Unlocking subsurface opportunities From qualitative to quantitative Bruno de Ribet, Technology Global Director Peter Wang, Technical Sales Advisor
More informationPetrophysical Study of Shale Properties in Alaska North Slope
Petrophysical Study of Shale Properties in Alaska North Slope Minh Tran Tapan Mukerji Energy Resources Engineering Department Stanford University, CA, USA Region of Interest 1.5 miles 20 miles Stratigraphic
More informationRC 1.3. SEG/Houston 2005 Annual Meeting 1307
from seismic AVO Xin-Gong Li,University of Houston and IntSeis Inc, De-Hua Han, and Jiajin Liu, University of Houston Donn McGuire, Anadarko Petroleum Corp Summary A new inversion method is tested to directly
More informationSubsurface Consultancy Services
Subsurface Consultancy Services Porosity from Reservoir Modeling Perspective Arnout Everts with contributions by Peter Friedinger and Laurent Alessio FESM June 2011 LEAP Energy Main Office: G-Tower, level
More informationIntegrating reservoir flow simulation with time-lapse seismic inversion in a heavy oil case study
Integrating reservoir flow simulation with time-lapse seismic inversion in a heavy oil case study Naimeh Riazi*, Larry Lines*, and Brian Russell** Department of Geoscience, University of Calgary **Hampson-Russell
More informationDownloaded 10/25/16 to Redistribution subject to SEG license or copyright; see Terms of Use at
Facies modeling in unconventional reservoirs using seismic derived facies probabilities Reinaldo J. Michelena*, Omar G. Angola, and Kevin S. Godbey, ireservoir.com, Inc. Summary We present in this paper
More informationA010 MULTISCALE RESERVOIR CHARACTERIZATION USING
1 A010 MULTISCALE RESERVOIR CHARACTERIZATION USING RODUCTION AND TIME LASE SEISMIC DATA Mokhles MEZGHANI, Alexandre FORNEL, Valérie LANGLAIS, Nathalie LUCET IF, 1 & 4 av de Bois réau, 92852 RUEIL-MALMAISON
More informationPost-stack inversion of the Hussar low frequency seismic data
Inversion of the Hussar low frequency seismic data Post-stack inversion of the Hussar low frequency seismic data Patricia E. Gavotti, Don C. Lawton, Gary F. Margrave and J. Helen Isaac ABSTRACT The Hussar
More informationApplication of advance tools for reservoir characterization- EEI & Poisson s impedance: A Case Study
P-272 Application of advance tools for reservoir characterization- EEI & Poisson s impedance: A Case Study Summary Puja Prakash*, S.K.Singh, Binode Chetia, P.K.Chaudhuri, Shyam Mohan, S.K.Das, ONGC Pore
More informationDelineating a sandstone reservoir at Pikes Peak, Saskatchewan using 3C seismic data and well logs
Delineating a sandston reservoir at Pikes Peak Delineating a sandstone reservoir at Pikes Peak, Saskatchewan using 3C seismic data and well logs Natalia L. Soubotcheva and Robert R. Stewart ABSTRACT To
More informationMultiple horizons mapping: A better approach for maximizing the value of seismic data
Multiple horizons mapping: A better approach for maximizing the value of seismic data Das Ujjal Kumar *, SG(S) ONGC Ltd., New Delhi, Deputed in Ministry of Petroleum and Natural Gas, Govt. of India Email:
More informationEarth models for early exploration stages
ANNUAL MEETING MASTER OF PETROLEUM ENGINEERING Earth models for early exploration stages Ângela Pereira PhD student angela.pereira@tecnico.ulisboa.pt 3/May/2016 Instituto Superior Técnico 1 Outline Motivation
More informationPre-Stack Seismic Inversion and Amplitude Versus Angle Modeling Reduces the Risk in Hydrocarbon Prospect Evaluation
Advances in Petroleum Exploration and Development Vol. 7, No. 2, 2014, pp. 30-39 DOI:10.3968/5170 ISSN 1925-542X [Print] ISSN 1925-5438 [Online] www.cscanada.net www.cscanada.org Pre-Stack Seismic Inversion
More informationPre-stack (AVO) and post-stack inversion of the Hussar low frequency seismic data
Pre-stack (AVO) and post-stack inversion of the Hussar low frequency seismic data A.Nassir Saeed, Gary F. Margrave and Laurence R. Lines ABSTRACT Post-stack and pre-stack (AVO) inversion were performed
More informationPorosity. Downloaded 09/22/16 to Redistribution subject to SEG license or copyright; see Terms of Use at
Geostatistical Reservoir Characterization of Deepwater Channel, Offshore Malaysia Trisakti Kurniawan* and Jahan Zeb, Petronas Carigali Sdn Bhd, Jimmy Ting and Lee Chung Shen, CGG Summary A quantitative
More informationProbabilistic seismic inversion using pseudo-wells
Seismic Rock Physics Seminar Probabilistic seismic inversion using pseudo-wells Patrick Connolly*, PCA Ltd Patrick Connolly Associates Ltd. geophysics for integration Outline ODiSI: probabilistic inversion
More informationSimultaneous Inversion of Clastic Zubair Reservoir: Case Study from Sabiriyah Field, North Kuwait
Simultaneous Inversion of Clastic Zubair Reservoir: Case Study from Sabiriyah Field, North Kuwait Osman Khaled, Yousef Al-Zuabi, Hameed Shereef Summary The zone under study is Zubair formation of Cretaceous
More informationMaximize the potential of seismic data in shale exploration and production Examples from the Barnett shale and the Eagle Ford shale
Maximize the potential of seismic data in shale exploration and production Examples from the Barnett shale and the Eagle Ford shale Joanne Wang, Paradigm Duane Dopkin, Paradigm Summary To improve the success
More informationTowards Interactive QI Workflows Laurie Weston Bellman*
Laurie Weston Bellman* Summary Quantitative interpretation (QI) is an analysis approach that is widely applied (Aki and Richards, 1980, Verm and Hilterman, 1995, Avseth et al., 2005, Weston Bellman and
More informationAssessing uncertainty on Net-to-gross at the Appraisal Stage: Application to a West Africa Deep-Water Reservoir
Assessing uncertainty on Net-to-gross at the Appraisal Stage: Application to a West Africa Deep-Water Reservoir Amisha Maharaja April 25, 2006 Abstract A large data set is available from a deep-water reservoir
More informationSequential Simulations of Mixed Discrete-Continuous Properties: Sequential Gaussian Mixture Simulation
Sequential Simulations of Mixed Discrete-Continuous Properties: Sequential Gaussian Mixture Simulation Dario Grana, Tapan Mukerji, Laura Dovera, and Ernesto Della Rossa Abstract We present here a method
More informationBayesian lithology/fluid inversion comparison of two algorithms
Comput Geosci () 14:357 367 DOI.07/s596-009-9155-9 REVIEW PAPER Bayesian lithology/fluid inversion comparison of two algorithms Marit Ulvmoen Hugo Hammer Received: 2 April 09 / Accepted: 17 August 09 /
More informationSEG Houston 2009 International Exposition and Annual Meeting. that the project results can correctly interpreted.
Calibration of Pre-Stack Simultaneous Impedance Inversion using Rock Physics Scott Singleton and Rob Keirstead, Rock Solid Images Log Conditioning and Rock Physics Modeling Summary Geophysical Well Log
More informationNet-to-gross from Seismic P and S Impedances: Estimation and Uncertainty Analysis using Bayesian Statistics
Net-to-gross from Seismic P and S Impedances: Estimation and Uncertainty Analysis using Bayesian Statistics Summary Madhumita Sengupta*, Ran Bachrach, Niranjan Banik, esterngeco. Net-to-gross (N/G ) is
More informationA021 Petrophysical Seismic Inversion for Porosity and 4D Calibration on the Troll Field
A021 Petrophysical Seismic Inversion for Porosity and 4D Calibration on the Troll Field T. Coleou* (CGG), A.J. van Wijngaarden (Hydro), A. Norenes Haaland (Hydro), P. Moliere (Hydro), R. Ona (Hydro) &
More informationDeterministic and stochastic inversion techniques used to predict porosity: A case study from F3-Block
Michigan Technological University Digital Commons @ Michigan Tech Dissertations, Master's Theses and Master's Reports 2015 Deterministic and stochastic inversion techniques used to predict porosity: A
More informationWorkflows for Sweet Spots Identification in Shale Plays Using Seismic Inversion and Well Logs
Workflows for Sweet Spots Identification in Shale Plays Using Seismic Inversion and Well Logs Yexin Liu*, SoftMirrors Ltd., Calgary, Alberta, Canada yexinliu@softmirrors.com Summary Worldwide interest
More informationAn empirical study of hydrocarbon indicators
An empirical study of hydrocarbon indicators Brian Russell 1, Hong Feng, and John Bancroft An empirical study of hydrocarbon indicators 1 Hampson-Russell, A CGGVeritas Company, Calgary, Alberta, brian.russell@cggveritas.com
More informationPluto 1.5 2D ELASTIC MODEL FOR WAVEFIELD INVESTIGATIONS OF SUBSALT OBJECTIVES, DEEP WATER GULF OF MEXICO*
Pluto 1.5 2D ELASTIC MODEL FOR WAVEFIELD INVESTIGATIONS OF SUBSALT OBJECTIVES, DEEP WATER GULF OF MEXICO* *This paper has been submitted to the EAGE for presentation at the June 2001 EAGE meeting. SUMMARY
More informationAn Integrated Workflow for Seismic Data Conditioning and Modern Prestack Inversion Applied to the Odin Field. P.E.Harris, O.I.Frette, W.T.
An Integrated Workflow for Seismic Data Conditioning and Modern Prestack Inversion Applied to the Odin Field P.E.Harris, O.I.Frette, W.T.Shea Talk Outline Introduction Motivation Introducing Pcube+ Gather
More informationReducing Uncertainty through Multi-Measurement Integration: from Regional to Reservoir scale
Reducing Uncertainty through Multi-Measurement Integration: from Regional to Reservoir scale Efthimios Tartaras Data Processing & Modeling Manager, Integrated Electromagnetics CoE, Schlumberger Geosolutions
More informationRobust one-step (deconvolution + integration) seismic inversion in the frequency domain Ivan Priezzhev* and Aaron Scollard, Schlumberger
Robust one-step (deconvolution + integration) seismic inversion in the frequency domain Ivan Priezzhev and Aaron Scollard, Schlumberger Summary Seismic inversion requires two main operations relative to
More informationPrinciples of 3-D Seismic Interpretation and Applications
Principles of 3-D Seismic Interpretation and Applications Instructor: Dominique AMILHON Duration: 5 days Level: Intermediate-Advanced Course Description This course delivers techniques related to practical
More informationDownloaded 11/02/16 to Redistribution subject to SEG license or copyright; see Terms of Use at Summary.
in thin sand reservoirs William Marin* and Paola Vera de Newton, Rock Solid Images, and Mario Di Luca, Pacific Exploración y Producción. Summary Rock Physics Templates (RPTs) are useful tools for well
More informationDATA ANALYSIS AND INTERPRETATION
III. DATA ANALYSIS AND INTERPRETATION 3.1. Rift Geometry Identification Based on recent analysis of modern and ancient rifts, many previous workers concluded that the basic structural unit of continental
More informationIntegration of broadband seismic data into reservoir characterization workflows: A case study from the Campos Basin, Brazil
t Technical papers Integration of broadband seismic data into reservoir characterization workflows: A case study from the Campos Basin, Brazil Ekaterina Kneller 1 and Manuel Peiro 1 Abstract Towed-streamer
More informationFracture characterization from scattered energy: A case study
Fracture characterization from scattered energy: A case study Samantha Grandi K., Sung Yuh, Mark E. Willis, and M. Nafi Toksöz Earth Resources Laboratory, MIT. Cambridge, MA. Total Exploration & Production.
More informationWe Challenges in shale-reservoir characterization by means of AVA and AVAZ
We-06-15 Challenges in shale-reservoir characterization by means of AVA and AVAZ N.C. Banik* (WesternGeco), M. Egan (WesternGeco), A. Koesoemadinata (WesternGeco) & A. Padhi (WesternGeco) SUMMARY In most
More informationThe Marrying of Petrophysics with Geophysics Results in a Powerful Tool for Independents Roger A. Young, eseis, Inc.
The Marrying of Petrophysics with Geophysics Results in a Powerful Tool for Independents Roger A. Young, eseis, Inc. While the application of new geophysical and petrophysical technology separately can
More informationThe SPE Foundation through member donations and a contribution from Offshore Europe
Primary funding is provided by The SPE Foundation through member donations and a contribution from Offshore Europe The Society is grateful to those companies that allow their professionals to serve as
More informationTime-lapse filtering and improved repeatability with automatic factorial co-kriging. Thierry Coléou CGG Reservoir Services Massy
Time-lapse filtering and improved repeatability with automatic factorial co-kriging. Thierry Coléou CGG Reservoir Services Massy 1 Outline Introduction Variogram and Autocorrelation Factorial Kriging Factorial
More informationRock physics and AVO analysis for lithofacies and pore fluid prediction in a North Sea oil field
Rock physics and AVO analysis for lithofacies and pore fluid prediction in a North Sea oil field Downloaded 09/12/14 to 84.215.159.82. Redistribution subject to SEG license or copyright; see Terms of Use
More informationStochastic inversion by matching to pseudo-wells
Pre-stack seismic data for interpretation and analysis Stochastic inversion by matching to pseudo-wells Patrick Connolly Shakespeare 884,647 words ~6,000,000 characters, 30 values each 10 9,000,000 realisations
More informationGermán D. Merletti* and Carlos Torres-Verdín, The University of Texas at Austin
Detection and Spatial Delineation of Thin-Sand Sedimentary Sequences With Joint Stochastic Inversion of Well Logs and 3D Prestack Seismic Amplitude Data Germán D. Merletti* and Carlos Torres-Verdín, The
More informationConsistent Downscaling of Seismic Inversions to Cornerpoint Flow Models SPE
Consistent Downscaling of Seismic Inversions to Cornerpoint Flow Models SPE 103268 Subhash Kalla LSU Christopher D. White LSU James S. Gunning CSIRO Michael E. Glinsky BHP-Billiton Contents Method overview
More informationP235 Modelling Anisotropy for Improved Velocities, Synthetics and Well Ties
P235 Modelling Anisotropy for Improved Velocities, Synthetics and Well Ties P.W. Wild* (Ikon Science Ltd), M. Kemper (Ikon Science Ltd), L. Lu (Ikon Science Ltd) & C.D. MacBeth (Heriot Watt University)
More informationToward an Integrated and Realistic Interpretation of Continuous 4D Seismic Data for a CO 2 EOR and Sequestration Project
SPE-183789-MS Toward an Integrated and Realistic Interpretation of Continuous 4D Seismic Data for a CO 2 EOR and Sequestration Project Philippe Nivlet, Robert Smith, Michael A. Jervis, and Andrey Bakulin,
More informationSEG/San Antonio 2007 Annual Meeting. Summary
A comparison of porosity estimates obtained using post-, partial-, and prestack seismic inversion methods: Marco Polo Field, Gulf of Mexico. G. Russell Young* and Mrinal K. Sen, The Institute for Geophysics
More informationRESERVOIR SEISMIC CHARACTERISATION OF THIN SANDS IN WEST SYBERIA
www.senergyltd.com RESERVOIR SEISMIC CHARACTERISATION OF THIN SANDS IN WEST SYBERIA Erick Alvarez, Jaume Hernandez, Bolkhotivin E.A., Belov A.V., Hakima Ben Meradi,Jonathan Hall, Olivier Siccardi, Phil
More information