A Better Understanding of Finite Element Simulation for Shale Gas Reservoirs through a Series of Different Case Histories

Abstract

Production from the extremely low permeability reservoirs found in shale gas plays results in very large pressure gradients at and around the sand face. Finite-element modeling technology, in which a large number of closely-spaced nodes are placed near hydraulic fractures, is the best approach for replicating the reservoir and completion complexity required to model horizontal shale gas wells. The primary objective of this work is to demonstrate how the performance of three Haynesville Shale gas wells were modeled using this technology. Each of these three wells exhibits the large early production decline typically observed in the Haynesville. Case histories for these wells show how the finite-element software can be used to simulate the mechanisms thought to contribute to the observed production decline. The workflow used to model performance demonstrates how petrophysical, stimulation, and flow modeling are utilized to understand the key reservoir and completion mechanisms that affect production behavior of shale gas wells. IntroductionUnconventional reservoirs have gained prominence because they hold the supply potential necessary to achieve energy independence for many countries. It has always been a challenge to model the reservoir and completion aspects of unconventional reservoirs accurately. In his recent (2010) review, King pointed to the variability in shales, even within the same lateral, and to the need to collect sufficient data to optimize the design of shale completions and stimulations. Thompson (2010) analyzed well performance data from Haynesville Shale wells and determined that the phenomenon of pressuredependent permeability is responsible for performance behavior. Taylor (2010) used reservoir and fracture stimulation data in a shale oil field to determine the optimum fracture spacing.

Shut-in based production optimization of shale-gas systems

“…This phenomenon has been observed in production data with isolated shut-ins, e.g. King (2010); Valkó (2009) ;Jayakumar et al (2011);Jayakumar and Rai (2014), although, as mentioned in the previous section, most frequently related to an initial shut-in period (Cheng, 2012;Makhanov et al, 2013). Shut-ins of shale-gas wells have also been performed and studied related to the plunger-lift technique, however, with durations of minutes or hours.…”

Lagrangian relaxation based decomposition for well scheduling in shale-gas systems

“…The last category is pressure build-up transients observable during re-openings of wells after shut-ins. Modeling all of these transients accurately is recognized as inherently difficult [23], even for very complex and detailed numerical models. Consequently, there are fundamental trade-offs when constructing shale-gas models, and clearly a proxy modeling scheme must take into account which type of transients the proxy should replicate.…”

“…for the Lagrangian Dual, where n is the number of iterations in the primal-dual loop solving (19) and (23). At each step of the loop, solving (23) provides an upper and lower bound on Z D [46],…”

The RealGas and RealGasH2O options of the TOUGH+ code for the simulation of coupled fluid and heat flow in tight/shale gas systems

“…Miller et al, (2010) and Jayakumar et al, (2011) used numerical simulation to history-match and forecast production from two different shale-gas fields. Cipolla et al, (2009), Freeman (2010, 5 Moridis et al, (2010) and Freeman et al, (2009; and conducted numerical sensitivity studies to identify the most important mechanisms and factors that affect shale-gas reservoir performance.…”