Lithologic Reservoirs ›› 2013, Vol. 25 ›› Issue (6): 35-39.doi: 10.3969/j.issn.1673-8926.2013.06.007

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New methods for the exploration, evaluation and development of unconventional reservoirs

WANG Tuo1， ZHU Rukai 1，2， BAI Bin 1，2， WU Songtao 1，2   

  1. 1. PetroChina Research Institute of Petroleum Exploration and Development， Beijing 100083， China；2. State Key Laboratory of Enhanced Oil Recovery， Beijing 100083， China
  • Online:2013-11-26 Published:2013-11-26

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Abstract:

 Unconventional petroleum especially shale oil and gas have great geological reserves and large exploitation potentiality, and petroleum in shale reservoir has achieved industrialized production in North America. Because of the properties of low permeability and low porosity compared with conventional reservoir, new geological methods and advanced technologies should be employed for unconventional reservoir research. Based on the studyofshale reservoir in Eagle Ford, Marcellus and Hawkville, this paper proposed a series ofnewapproaches for exploration, evaluation and development ofunconventional reservoir. Firstly, 2Dand 3Dseismic alongwith pilot well used toidentifyconfiguration and the boundary of the target basin, as well as the thickness and area of reservoirs. And then, core data were used to carryout logcalibration soas toacquire comprehensive loggingcurve interpretation includingloggingdata, petrophysical analysis data and seven properties of shale reservoirs. Combined with seismic identification technology, “sweet spot” and favorable reservoirs distribution were effectivelypredicted. Finally, geosteeringwas applied toallowthe well tostay within the target windowin reservoir, and combined with 3Dseismic technology, designed scheme ofhydraulic fracture stimulation for different geologic reservoirs. The successfullyexploration ofthese newmethods can provide reference for the exploration, evaluation and development oftight oil/gas, shale oil/gas and coalbed methane in China.

 

Key words: seismic attributes, correlation analysis, grey correlation degree, neural network, carbonate reservoir, seismic sedimentology

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