Position/Fracture Uncertainty and Ranging During Drilling and Fracturing

摘要

Unconventional shale drilling has developed rapidly during the last decade. The development of substantial prospects, especially in shale gas plays, requires advanced technologies and prudent well monitoring. Many new operational challenges are associated because of simultaneous operations, such as drilling and fracturing nearby wells. While drilling, the interaction of the fractures from nearby wells results in pressure communication and unexpected well kicks. Because of the low porosity of those tight reservoirs, those uncommon well kicks have so far not been severe, but taking the time to control wellbore pressures adversely affects the efficiency of the drilling process and increases the non- productive time. Additionally, as fracturing becomes more efficient, and conductivity increases, possible future events might have much more dramatic consequences. This paper presents a method that uses the positional uncertainty of the well being drilled, positional uncertainty of the fractured/fracturing wells, and uncertainty of the fracture length and fracture orientation to avoid this problem. The paper discusses the reasons and challenges, techniques, and lessons learned to solve the simultaneous moving boundary conditions between the reference wells and multiple offset wells. The method of explicit solution avoids a trial-and-error procedure and provides excellent maneuverability of planning requirements. The modified model and methods provided the use of exact mathematical solutions and uncertainty ellipses estimates. This method can also be used to monitor the influence of other variables, such as temperature cycling, oil drainage distance, wellbore storage effect, and reservoir pressure transients. The method presented can be used in the planning stage to ensure optimal well placement; it can also be used for designing super fractures between wells. The study indicates that the use of real-time data from drilling and fracturing wells can predict the need for remedial action.