Reservoir fracture characterization

Data processing: measuring – calibrating – or testing – Measurement system in a specific environment – Earth science

Reexamination Certificate

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C367S056000

Reexamination Certificate

active

06928367

ABSTRACT:
Systems and methods of reservoir fracture characterization are disclosed. In one embodiment, the method includes: 1) obtaining seismic traces; 2) combining seismic traces into gathers that reveal acoustic reflectivity as a function of offset, azimuth, and position; 3) measuring reflection anisotropy as a function of position; 4) determining a relationship between reflection anisotropy and fracture intensity measurements at specific positions; and 5) applying the relationship to reflection anisotropy measurements to create a set of fracture intensity measurements at regularly-spaced positions.

REFERENCES:
Zellou et al., Improved Fractured Reservoir Characterization Using Neural Networks, Geomechanics and 3-D Seismic, 1995, SPE 30722, pp. 205-215.
Ouenes et al., Fractured Reservoir Characterization and Performance Forecasting Using Geomechanics and Artificial Intelligence, 1995, SPE 30572, pp. 425-436.
Hall et al., Fractured reservoir Characterization Using P-Wave AVOA Analysis of 3D OBC Data, Aug. 2002, The Leading Edge, pp. 777-781.
Gray et al., Fractured Detection in Manderson Field: A 3-D AVAZ Case History, Nov. 2000, The Leading Edge, pp. 1214-1221.
Letter from co-inventor Abdel M. Zellou with attached claims having annotations by Abdel M. Zellou (7 p.).
Gauthier, B.D.M., et al., “Integrated Fractured Reservoir Characterization: A Case Study In A North Africa Field.” Society Of Petroleum Engineers, Paper No. SPE 65118, Oct. 24-24, 2000, 11 pages, Paris, France.
Ouenes, Ahmed et al., “Fractured Reservoir Characterization And Performance Forecasting Using Geomachinics And Artificial Intelligence,” Society Of Petroleum Engineers, Paper No. SPE 30572, Oct. 22-25, 1995, pp. 425-436, Dallas, Texas.
Ouenes, Ahmed, “Practical Application Of Fuzzy Logic And Neural Networks To Fractured Reservoir Characterization,” Computers & Geosciences, 26, 2000, pp. 953-962.
Ouenes, Ahmed et al., “Practical Use Of Neural Networks In Tight Gas Fractured Reservoirs: Application To The San Juan Basin,” Society Of Petroleum Engineers, Paper No. SPE 39965, Apr. 5-8, 1998, pp. 573-580, Denver, Colorado.
Zellou, Ahmed M. et al., “Improved Fractured Reservoir Characterization Using Neural Networks, Geomechanics And 3-D Seismic,” Society Of Petroleum Engineers, Paper No. SPE 30722, Oct. 22-25, 1995, pp. 205-215, Dallas, Texas.
Zellou, Ahmed M. et al., “Integrated Fractured Reservoir Characterization Using Neural Networks And Fuzzy Logic: Three Case Studies,” Journal Of Petroleum Geology, vol. 24, No. 4, Oct., 2001, pp. 459-476.
A.M. Zellou and A. Ouenes, Integrated Fractured Reservoir Characterization Using Neural Networks and Fuzzy Logic: Three Case Studies, Journal of Petroleum Geology, Oct. 2001, pp. 1-18, vol. 24(a), Scientific Press Ltd., UK.
A. Ouenes et al., Practical Use of Neural Networks in Tight Gas Fractured Reservoirs: Application to the San Juan Basin, 1998, pp. 1-8, SPE 39965, Society of Petroleum Engineers, Inc., Richardson, Texas.

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