Controlled Hydraulic Fracturing of Naturally Fractured Shales - A ase tudy in the Marcellus Shale xamining ow to dentify and xploit atural ractures

摘要

This case study will illustrate how natural fractures were identified in real time through analysis of surface pumping parameters and confirmed with microseismic data and multiple production logs. Furthermore, subsurface natural fracture distribution will be examined in an approximately 1 square mile area using microseismic and production data. Additionally, gas shows from mud logs will be examined and correlated to production. Finally, methods for real-time fracture optimization will be explored based on natural fracture identification through analysis of fracturing parameters along the horizontal wellbore. It is well known in the oil and gas industry that natural fractures, either open or prone to opening during hydraulic fracturing, greatly improve hydrocarbon production in tight sand and shale reservoirs. There are many techniques available to identify natural fractures; including image logs, mud logs, G-function analysis leading to pressure dependent leakoff (PDL), core analysis, and many more. However, these techniques have many limitations when used for optimizing the interaction of hydraulic fractures with natural fractures to create a complex fracture network. The limitations result from small sample size when using core analysis, inaccurate or unpredictable measurements from mud logs; or in the case of horizontal image logs, they are extremely costly and uneconomical to run regularly. Recent research related to subcritical index testing has shown that subterranean natural fractures manifest themselves in clusters that are not uniformly distributed along a horizontal wellbore. Therefore, it would be advantageous to gain insight into natural fracture occurrence and distribution before designing the fracturing program and then positively identify natural fracture clusters along the horizontal wellbore in real time and optimize hydraulic fracturing parameters on-the-fly.