SeTES: A self-teaching expert system for the analysis, design, and prediction of gas production from unconventional gas resources

SeTES is a self-teaching expert system that (a) can incorporate evolving databases involving any type and amount of relevant data (geological, geophysical, geomechanical, stimulation, petrophysical, reservoir, production, etc.) originating from unconventional gas reservoirs, i.e., tight sands, shale or coalbeds, (b) can continuously update its built-in public database and refine the its underlying decision-making metrics and process, (c) can make recommendations about well stimulation, well location, orientation, design, and operation, (d) offers predictions of the performance of proposed wells (and quantitative estimates of the corresponding uncertainty), and (e) permits the analysis of data from installed wells for parameter estimation and continuous expansion of its database. Thus, SeTES integrates and processes information from multiple and diverse sources to make recommendations and support decision making at multiple time-scales, while expanding its internal database and explicitly addressing uncertainty. It receives and manages data in three forms: public data, that have been made available by various contributors, semi-public data, which conceal some identifying aspects but are available to compute important statistics, and a userʹs private data, which can be protected and used for more targeted design and decision making. It is the first implementation of a novel architecture that allows previously independent analysis methods and tools to share data, integrate results, and intelligently and iteratively extract the most value from the dataset. SeTES also presents a new paradigm for communicating research and technology to the public and distributing scientific tools and methods. It is expected to result in a significant improvement in reserve estimates, and increases in production by increasing efficiency and reducing uncertainty.