SeTES: A self-teaching expert system for the analysis, design, and prediction of gas production from unconventional gas resources
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abstract
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 any available information from multiple and diverse sources on a continuous basis to make recommendations and support decision making at multiple time-scales, while expanding its internal database and explicitly addressing uncertainty. It uses three types of data: 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 user's private data, which can be protected and used for more targeted design and decision making. SeTES can be a vital and easy-to-use tool in gas production from unconventional gas resources. It is expected to result in a significant increase in both reserve estimates and production by providing a technology that will increase efficiency and reduce the uncertainties associated with such gas reservoirs. Copyright 2011, Society of Petroleum Engineers.
name of conference
Society of Petroleum Engineers - Canadian Unconventional Resources Conference 2011, CURC 2011
