TY - JOUR

T1 - Bayesian facies inversion on a partially dolomitized isolated carbonate platform

T2 - A case study from Central Luconia Province, Malaysia

AU - Ghon, Georg

AU - Grana, Dario

AU - Rankey, Eugene C.

AU - Baechle, Gregor T

AU - Bleibinhaus, Florian

AU - Lang, Xiaozheng

AU - Passos de Figueiredo, Leandro

AU - Poppoelreiter, Michael C

PY - 2021/2/23

Y1 - 2021/2/23

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 forconditional means and covariances of posterior probabilities, assuming a Gaussian mixture model. The inversion is a two-step process, first solving for P- and 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.

AB - 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 forconditional means and covariances of posterior probabilities, assuming a Gaussian mixture model. The inversion is a two-step process, first solving for P- and 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.

U2 - 10.1190/GEO2020-0351.1

DO - 10.1190/GEO2020-0351.1

M3 - Article

VL - 86.2021

SP - B97-B108

JO - Geophysics

JF - Geophysics

SN - 0016-8033

IS - 2

ER -