Automatic scheduling techniques are becoming a crucial tool for the efficient planning of large astronomical surveys. A specific scheduling method is being designed and developed for the Atmospheric Remote-sensing Infrared Exoplanet Large-survey (Ariel) mission planning based on a hybrid meta-heuristic algorithm with global optimization capability to ensure obtaining satisfying results fulfilling all mission constraints. We used this method to simulate the Ariel mission plan, to assess the feasibility of its scientific goals, and to study the outcome of different science scenarios. We conclude that Ariel will be able to fulfill the scientific objectives, i.e. characterizing $$\sim$$
∼
1000 exoplanet atmospheres, with a total exposure time representing about 75–80% of the mission lifetime. We demonstrate that it is possible to include phase curve observations for a sample of targets or to increase the number of studied exoplanets within the mission lifetime. Finally, around 12–15% of the time can still be used for non-time constrained observations.
Unconventional hydrocarbon resources are going to play an important role in the US energy strategy. Conventional tools and techniques that are used for analysis of unconventional resources include decline curve analysis, type curve matching and sometimes (in the case of prolific assets) reservoir simulation. These methods have not been completely successful due to the fact that fluid flow in unconventional reservoirs does not follow the same physical principles that supports mentioned analytical and numerical methods. Application of an innovative technology, Top-Down Modeling (TDM), is proposed for the analyses of unconventional resources. This technology is completely data-driven, incorporating field measurements (drilling data, well logs, cores, well tests, production history, etc.) to build comprehensive full field reservoir models. In this study, a Top-Down Model (TDM) was developed for a field in Weld County, Colorado, producing from Niobrara. The TDM was built using data from more than 145 wells. Well logs, production history, well design parameters and dynamic production constrains are the main data attributes that were used to perform data driven analysis. The workflow for Top-Down Modeling included generating a high-level geological model followed by reservoir delineation based on regional productivity, reserve and recovery estimation, field wide pattern recognition (based on fuzzy set theory), Key Performance Indicator (KPI) analysis (which estimates the degree of influence of each parameter on the field production), and finally history matching the production data from individual wells and production forecasting. The results of production analysis by Top-Down Modeling can provide insightful guidelines for better planning and decision making.
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