**Slide 1**

**Acknowledgements**

**Quote**

**Overview**

**Part I**

**Limitations of traditional
geostatistics**

**Stochastic sequential
simulation**

**Practice of sequential
simulation**

**Multiple-point Geostatistics**

**Extended Normal Equations**

**Single Normal Equation**

**The training image module**

**The SNESIM algorithm**

**Probabilities from a Search
Tree**

**Example**

**Where do we get a 3D TI ?**

**Modular training image**

**Properties of training image**

**Part II**

**Simple question, difficult
problem…**

**Combining sources of
information**

**Conditional independence**

**Correcting conditional
independence**

**Permanence of ratios
hypothesis**

**Advantages of using ratios**

**Simple problem…**

**Example reservoir**

**P(A|C), A = single-point !**

**Concept of MODULAR training
image**

**Local rotation angle from
seismic**

**Results**

**Constrain to local “channel
features”**

**Part III**

**Production data does not
inform geological heterogeneity**

**Approach**

**Methodology: two facies**

**Define a Markov chain**

**Transition matrix**

**Parameter r**_{D}

**Determine r**_{D}

**r**_{D} determines a
“perturbation”

**Complete algorithm**

**Examples**

**Single model**

**r**_{D} values,

single 1D optimization

**Different geology**

**More wells**

**Hierarchical matching**

**Example**

**Results**

**Results**

**More realistic**

**Conclusions**

**More on conditional
independence**