## Abstract

Stochastic modeling of the geology in petroleum reservoirs has become an important tool in order to investigate flow properties in the reservoir. The stochastic models used contain parameters which must be estimated based on observations and geological knowledge. The amount of data available is however quite limited due to high drilling costs etc., and the lack of data prevents the use of many of the standard data driven approaches to the parameter estimation problem. Modern simulation based methods using Markov chain Monte Carlo simulation, can however be used to do fully Bayesian analysis with respect to parameters in the reservoir model, with the drawback of relatively high computational costs. In this paper, we propose a simple, relatively fast approximate method for fully Bayesian analysis of the parameters. We illustrate the method on both simulated and real data using a two-dimensional marked point model for reservoir characterization.

Original language | English (US) |
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Pages (from-to) | 1147-1162 |

Number of pages | 16 |

Journal | Communications in Statistics Part B: Simulation and Computation |

Volume | 26 |

Issue number | 3 |

State | Published - Dec 1 1997 |

## Keywords

- Marked point process
- Markov chain Monte Carlo
- Parameter estimation
- Reservoir characterization

## ASJC Scopus subject areas

- Statistics and Probability
- Modeling and Simulation