Rock physics based facies classification from seismic ... Center...Gouveia, W., 1996, Bayesian seismic waveform data inversion: Parameter estimation and uncertainty analysis: Ph.D.
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Rock physics based facies classification from seismic inversion results in unconventional
reservoirs Zakir Hossain* and Stefano Volterrani, ION
Summary
The objective of this study is to demonstrate the power of
integrating rock physics theory, measurement and
simulation to improve facies prediction in an
unconventional limestone and shale reservoir. Reliable
facies prediction is a challenge in unconventional reservoir
characterization because of complex geological
heterogeneities. Both deterministic and probabilistic
approaches are commonly used in facies classifications that
use well and seismic data. Bayes’ theory with
uninformative priors is often used for probabilistic facies
classification. We provide a case study that uses Bayes’
theory with informative priors for facies classification from
pre-stack simultaneous elastic inversion results in an
unconventional reservoir. In the proposed methodology, we
integrate rock physics based theory, measurements and
simulation with Bayesian statistical techniques where the
prior probability represents our knowledge about rock
properties, and is consistent with our geological
knowledge, rock physics theory and measured data. We
evaluate four facies classification methodologies:
deterministic method, probabilistic method with
uninformative priors, probabilistic method with
uninformative priors and training facies defined from
simulation, and probabilistic method with informative
priors and training facies defined from simulation. This
study indicates that, in probabilistic facies classification
(Method 2), if uninformative priors are used, results are
sub-optimal compared to deterministic methods involving a
Rock Physics Template (RPT) workflow (Method 1).
Additionally, probabilistic facies classification can be
further improved if we use uninformative priors and
training facies defined from Monte Carlo simulation
(Method 3). Probabilistic facies prediction improves if we
use informative priors and training facies defined from
Monte Carlo simulation (Method 4).
Introduction
Reliable facies prediction is an essential problem in
reservoir characterization. Predicted facies properties are
important engineering inputs for drilling and production.
For reservoir facies characterization, two different methods
are commonly used: deterministic approach (Doyen, 1988;
Loertzer and Berkhout, 1992; Avseth et al., 2005; Hossain
et al. 2015) and probabilistic approach (Gastaldi, et al.
1998; Gouveia, 1996; Takashashi, 2000; Mukerji et al.,
2001; Hossain and Mukerji, 2011; Bachrach et al., 2004;
Bachrach, and Dutta, 2004; Grana et al., 2012; Hossain et
al. 2015). For deterministic facies classification we use an
RPT workflow, while for probabilistic facies prediction, we
can use Bayes’ theory:
n
i
ii
iii
cpcxp
cpcxpxcp
1
|
|| (1)
where, p(ci) is the prior probability,
p(ci|x) is the posterior probability of our observation,
p(x|ci) is the likelihood of obtaining our particular
observation ci, under the supposition that any of the
possible states of the variable x was actually the case.
For seismic based facies prediction, the above expression
can be written as:
priorfaciestrainingseismicp
seismicfaciespredictedp
)_|(
)|_( (2)
From this expression, we observe that the training facies
influence the predicted facies, but the prior probabilities are
more heavily influenced by the predicted facies. Hossain et
al. (2015) demonstrated the role of prior belief of Bayesian
statistics by using three types of priors: uninformative
priors, informative priors, and continuous priors and found
that for uninformative prior, the posterior remains
unchanged, while for informative priors, the posterior is
increased.
For uninformative priors, equation (1) becomes:
Figure 1: The relationship between rock physics theory, rock
physics measurement and rock physics based simulation for facies
EDITED REFERENCES Note: This reference list is a copyedited version of the reference list submitted by the author. Reference lists for the 2016
SEG Technical Program Expanded Abstracts have been copyedited so that references provided with the online metadata for each paper will achieve a high degree of linking to cited sources that appear on the Web.
REFERENCES Avseth, P., T. Mukerji, and G. Mavko, 2005, Quantitative seismic interpretation: Applying rock physics
tools to reduce interpretation risk: Cambridge University, http://dx.doi.org/10.1017/CBO9780511600074.
Bachrach, R., and N. Dutta, 2004, Joint estimation of porosity and saturation using stochastic rock physics modeling: 66th Annual International Conference and Exhibition, EAGE, Extended Abstracts, http://dx.doi.org/10.2118/89991-MS.
Bachrach, R., M. Beller, C. Liu, J. Perdomo, D. Shelander, and N. Dutta, 2004, Combining rock physics analysis, full wave form prestack inversion and high resolution seismic interpretation to map lithology units in deep water: A Gulf of Mexico case study: The Leading Edge, 23, 378–383, http://dx.doi.org/10.1190/1.1729224.
Doyen, P. M., 1988, Porosity from seismic data: A geostatistical approach: Geophysics, 53, 1263–1275, http://dx.doi.org/10.1190/1.1442404.
Gastaldi, C., D. Roy, P. Doyen, and L. Den Boer, 1998, Using Bayesian simulations to predict reservoir thickness under tuning conditions: The Leading Edge, 17, 539–543, http://dx.doi.org/10.1190/1.1438008.
Gouveia, W., 1996, Bayesian seismic waveform data inversion: Parameter estimation and uncertainty analysis: Ph.D. thesis, Colorado School of Mines.
Grana, D., M. Pirrone, and T. Mukerji, 2012, Quantitative log interpretation and uncertainty propagation of petrophysical properties and facies classification from rock physics modeling and formation evaluation analysis: Geophysics, 77, no. 3, WA45–WA63, http://dx.doi.org/10.1190/geo2011-0272.1.
Hossain, Z., and T. Mukerji, 2011, Statistical rock physics and Monte Carlo Simulation of seismic attributes for greensand: 73rd Annual International Conference and Exhibition, EAGE, Extended Abstracts, http://dx.doi.org/10.3997/2214-4609.20149674.
Hossain, Z., S. Volterrani, and F. Diaz, 2015, Integration of rock physics template to improve Bayes’ facies classification: 85th Annual International Meeting, SEG, Expanded Abstracts, 2760–2764, http://dx.doi.org/10.1190/segam2015-5900545.1.
Landau, D. P., and K. Binder, 2000, A Guide to Monte Carlo Simulations in Statistical Physics: Cambridge University Press.
Lörtzer, G. J. M., and A. J. Berkhout, 1992, An integrated approach to lithologic inversion: Part 1, Theory: Geophysics, 57, 233–244, http://dx.doi.org/10.1190/1.1443236.
Takahashi, I., 2000, Quantifying information and uncertainty of rock property estimation from seismic data: Ph.D. thesis, Stanford University.