### Abstract

In this paper, we present a novel method of data integration based on the permanence of ratio hypothesis. In order to model the conditional probability, it would be convenient if the information from each data source can be assessed independently in order to find P(A|B) and P(A|C), and then these joint probabilities are merged to calculate P(A|B,C) accounting for the redundancy between different data sources. We propose a methodology for calculating the redundancy between different sources of information. Our formulation is based on the information from each data modeled using a mixture of Gaussian assumption indicative of the multiple facies or categories of rock properties observed in the reservoir. We implemented the proposed methodology to characterize a carbonate reservior in the Gulf of Mexico. The available data sets were drill cutting data, core data, well log measurements and 3D seismic volume. We used core data to calibrate log measurements to lithofacies. Then, we merged the probability maps of lithofacies using permanence of ratio hypothesis and generated multiple realization by Monte-Carlo sampling from the probability maps. The modeling resulted in identification of reservoir regions that have higher proportion of dolomitized grainstones that might be suitable drilling targets.

Original language | English (US) |
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Title of host publication | Petroleum Geostatistics 2015 |

Publisher | European Association of Geoscientists and Engineers, EAGE |

Pages | 243-247 |

Number of pages | 5 |

ISBN (Electronic) | 9781510814110 |

State | Published - Jan 1 2015 |

Event | Petroleum Geostatistics 2015 - Biarritz, France Duration: Sep 7 2015 → Sep 11 2015 |

### Publication series

Name | Petroleum Geostatistics 2015 |
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### Other

Other | Petroleum Geostatistics 2015 |
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Country | France |

City | Biarritz |

Period | 9/7/15 → 9/11/15 |

### Fingerprint

### All Science Journal Classification (ASJC) codes

- Geotechnical Engineering and Engineering Geology
- Geophysics
- Statistics, Probability and Uncertainty
- Geology

### Cite this

*Petroleum Geostatistics 2015*(pp. 243-247). (Petroleum Geostatistics 2015). European Association of Geoscientists and Engineers, EAGE.

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*Petroleum Geostatistics 2015.*Petroleum Geostatistics 2015, European Association of Geoscientists and Engineers, EAGE, pp. 243-247, Petroleum Geostatistics 2015, Biarritz, France, 9/7/15.

**Integration of seismic and well data to characterize facies variation in a carbonate reservoir-the tau model revisited.** / Elahi Naraghi, M.; Srinivasan, Sanjay.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Integration of seismic and well data to characterize facies variation in a carbonate reservoir-the tau model revisited

AU - Elahi Naraghi, M.

AU - Srinivasan, Sanjay

PY - 2015/1/1

Y1 - 2015/1/1

N2 - In this paper, we present a novel method of data integration based on the permanence of ratio hypothesis. In order to model the conditional probability, it would be convenient if the information from each data source can be assessed independently in order to find P(A|B) and P(A|C), and then these joint probabilities are merged to calculate P(A|B,C) accounting for the redundancy between different data sources. We propose a methodology for calculating the redundancy between different sources of information. Our formulation is based on the information from each data modeled using a mixture of Gaussian assumption indicative of the multiple facies or categories of rock properties observed in the reservoir. We implemented the proposed methodology to characterize a carbonate reservior in the Gulf of Mexico. The available data sets were drill cutting data, core data, well log measurements and 3D seismic volume. We used core data to calibrate log measurements to lithofacies. Then, we merged the probability maps of lithofacies using permanence of ratio hypothesis and generated multiple realization by Monte-Carlo sampling from the probability maps. The modeling resulted in identification of reservoir regions that have higher proportion of dolomitized grainstones that might be suitable drilling targets.

AB - In this paper, we present a novel method of data integration based on the permanence of ratio hypothesis. In order to model the conditional probability, it would be convenient if the information from each data source can be assessed independently in order to find P(A|B) and P(A|C), and then these joint probabilities are merged to calculate P(A|B,C) accounting for the redundancy between different data sources. We propose a methodology for calculating the redundancy between different sources of information. Our formulation is based on the information from each data modeled using a mixture of Gaussian assumption indicative of the multiple facies or categories of rock properties observed in the reservoir. We implemented the proposed methodology to characterize a carbonate reservior in the Gulf of Mexico. The available data sets were drill cutting data, core data, well log measurements and 3D seismic volume. We used core data to calibrate log measurements to lithofacies. Then, we merged the probability maps of lithofacies using permanence of ratio hypothesis and generated multiple realization by Monte-Carlo sampling from the probability maps. The modeling resulted in identification of reservoir regions that have higher proportion of dolomitized grainstones that might be suitable drilling targets.

UR - http://www.scopus.com/inward/record.url?scp=84959212480&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84959212480&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:84959212480

T3 - Petroleum Geostatistics 2015

SP - 243

EP - 247

BT - Petroleum Geostatistics 2015

PB - European Association of Geoscientists and Engineers, EAGE

ER -