Intelligent fracture creation for shale gas development


Craig C. Douglas (a), Guan Qin (a), Nathan Collier (b), and Bin Gong (c)

(a) University of Wyoming School of Energy Resources, Laramie, WY 82071, USA

(b) King Abdullah University of Science and Technology, Thuwal, Saudi Arabia

(c) Peking University Department of Energy and Resource Engineering, Beijing 100871, China


Abstract


Shale gas represents a major fraction of the proven reserves of natural gas in the United States and a collection of other countries. Higher gas prices and the need for cleaner fuels provides motivation for commercializing shale gas deposits even though the cost is substantially higher than traditional gas deposits. Recent advances in horizontal drilling and multistage hydraulic fracturing, which dramatically lower costs of developing shale gas fields, are key to renewed interest in shale gas deposits.


Hydraulically induced fractures are quite complex in shale gas reservoirs. Massive, multistage, multiple cluster treatments lead to fractures that interact with existing fractures (whether natural or induced earlier). A dynamic approach to the fracturing process so that the resulting network of reservoirs is known during the drilling and fracturing process is economically enticing. The process needs to be automatic and done in faster than real-time in order to be useful to the drilling crews.


Keywords


sensor-model feedback, dynamic data-driven application system, DDDAS,

multiscale methods, reservoir simulation