Ultra-low permeability shale gas reservoirs have emerged as a significant source of natural gas in North America. Improved drilling, completion and stimulation techniques combined with declining conventional gas reserves have made shale gas a desirable commodity with significant long-term production potential.
Despite extensive development and production (particularly in North America), minimal work has been done to develop tools and methodologies for shale gas prospect analysis. Due to the complexity and large extent of unconventional natural gas resources, it is crucial to be able to investigate potential prospects in a methodical manner to determine whether a given prospect has commercial potential and to compare it to other potential prospects. Experience has shown that conventional exploration techniques using deterministic solutions are not suitable for unconventional prospects due to the unique nature of each prospect and the complexity of each reservoir.
The most common method for exploiting shale gas reservoirs is through the use of multi-fractured horizontal wells; the resulting well performance, influenced by both the stimulation treatment and complex reservoir attributes, precludes the use of traditional techniques for production data analysis and forecasting. Several new techniques have been developed to improve the quality and efficiency of analysis while accounting for properties that are unique to shale gas (i.e. adsorbed gas in self-sourced reservoirs and nanodarcy level permeability). Also, due to the complexity of shale gas reservoirs, many authors have suggested that deterministic analysis is unsuitable and that probabilistic analysis should be used to quantify the risk and uncertainty associated with shale gas prospects and the associated data.
This paper discusses a new tool that was developed specifically for shale gas prospect screening. This tool combines the latest production data analysis and forecasting techniques with a simple, yet rigorous method for stochastically comparing shale gas prospects. The paper discusses the production analysis and rate forecasting techniques used in the tool, as well as the tool development and application. A sample case using simulated data is presented for proof of concept and a discussion is given for extension of the tool for comparison of several potential prospects.