Unconventional resources derive their name based on the unconventional evaluation, extraction and production requirements associated with them. Unconventional reservoirs have moved from emerging resources a decade ago to major interests of many operating companies. The remarkable increase in the stakes of these reservoirs and the growth of associated risk has pushed the industry to update and upgrade evaluation techniques of such reservoirs. Current industry practice for characterization and assessment of unconventional reservoirs mostly utilizes empirical decline curve analysis or analytic rate and pressure transient analysis.
Analytical techniques are not capable to include the flow dynamics of a full field numerical simulation model for its capabilities to capture field geology, reservoir geometry and property distribution. Whereas, a reservoir simulation model requires detailed information input set and is computationally expensive. Fast marching method has been introduced as an intermediate approach to overcome the limitations of analytical techniques and as a complimentary tool to numerical simulation. This approach is based on a high frequency asymptotic solution of the diffusivity equation in heterogeneous reservoirs. This leads to Eikonal equation which can be solved for ‘diffusive time of flight’, governing pressure front propagation in the reservoir. The Eikonal equation is solved using fast marching method giving the speed to the solution. Diffusive time of flight can be a useful tool for drainage volume visualization and well performance prediction. It can further be used as spatial coordinates to reduce 3-D diffusivity equation into a 1-D equation thereby making it a comprehensive simulator. The speed and versatility of our proposed method makes it ideally suited for high resolution reservoir characterization through integration of static and dynamic data.
The major advantages of the approach are its simplicity, intuitive appeal and computational efficiency. This work demonstrates power and utility of our method using a field example that involves history matching, uncertainty analysis and performance assessment of a shale gas reservoir located in East Texas. A sensitivity study is first carried out to systematically identify the ‘heavy hitters’ impacting the well performance. This is followed by a history matching and uncertainty analysis to identify the fracture parameters and the stimulated reservoir volume. A comparison of model predictions with the actual well performance shows that our approach is able to reliably predict the pressure depletion and rate decline
