TY  - JOUR
EP  - B108
KW  - Carbonation; Gas permeability; Gaussian distribution; Geophysical prospecting; Lime; Limestone; Offshore gas fields; Offshore oil well production; Petroleum reservoir engineering; Petroleum reservoirs; Probability density function; Seismic response; Seismic waves; Shear waves
KW  -  Argillaceous limestone; Gaussian Mixture Model; Geologic interpretation; Porosity distributions; Posterior distributions; Probabilistic algorithm; Probabilistic prediction; Reservoir characterization
KW  -  Porosity
KW  -  AVO method; Bayesian analysis; carbonate platform; data inversion; dolomitization; facies analysis; gas field; hydrocarbon reservoir; limestone; lithofacies; lithology; porosity; probability density function; reservoir characterization; S-wave; seismic data; seismic velocity; structural geology
KW  -  East Malaysia; Malaysia; Sarawak
N2  - We have developed a case study of geophysical reservoir characterization in which we use elastic inversion and probabilistic prediction to estimate nine carbonate lithofacies and the associated porosity distribution. The study focuses on an isolated carbonate platform of middle Miocene age, offshore Sarawak in Malaysia that has been partly dolomitized- A process that increased the porosity and permeability of the prolific gas reservoir. The nine lithofacies are defined from one reference core and include a range of lithologies and pore types, covering limestone and dolomitized limestone, each with vuggy varieties, as well as sucrosic and crystalline dolomites with intercrystalline porosity, and argillaceous limestones and shales. To predict the lithofacies and porosity from geophysical data, we adopt a probabilistic algorithm that uses Bayesian theory with an analytical solution for conditional means and covariances of posterior probabilities, assuming a Gaussian mixture model. The inversion is a two-step process, first solving for P- A nd S-wave velocities and density from two partial seismic stacks. Subsequently, the lithofacies and porosity are predicted from the elastic parameters in the borehole and across a 2D inline. The final result is a model that consists of the pointwise posterior distributions of the facies and porosity at each location where seismic data are available. The facies posterior distribution represents the facies proportions estimated from seismic data, whereas the porosity distribution represents the probability density function at each location. These distributions provide the most likely model and its associated uncertainty for geologic interpretations of lithofacies associated with distinct stages of carbonate platform growth. © 2021 Society of Exploration Geophysicists.
VL  - 86
JF  - Geophysics
AV  - none
TI  - Bayesian facies inversion on a partially dolomitized isolated carbonate platform: A case study from Central Luconia Province, Malaysia
SN  - 00168033
ID  - scholars15119
A1  - Ghon, G.
A1  - Grana, D.
A1  - Rankey, E.C.
A1  - Baechle, G.T.
A1  - Bleibinhaus, F.
A1  - Lang, X.
A1  - De Figueiredo, L.P.
A1  - Poppelreiter, M.C.
Y1  - 2021///
N1  - cited By 6
SP  - B97
IS  - 2
PB  - Society of Exploration Geophysicists
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105162592&doi=10.1190%2fgeo2020-0351.1&partnerID=40&md5=92679885e1c457ddd01c9ccff0e6577d
ER  -