Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Geostatistical algorithms that consider multiple-point statistics are becoming increasingly popular. These methods allow for the reproduction of complicated features beyond the commonly implemented variogram. In practice, it is not possible to infer many multiple-point statistics directly from the available data; therefore, it is common to borrow statistics from training images. A library of training images is developed for fluvial and deepwater depositional settings. These training images are based on object-based models, surface-based models and pseudo-genetic process mimicking (event-based) models. The training images represent a range of net-to-gross fractions and depositional styles. Associated code provides the ability to modify, format and tailor the training images and to extract multiple-point statistics. The training image library provides a source for multiple-point statistics, can be used in comparative flow studies and as an aid in scenario-based uncertainty studies.
['Michael J. Pyrcz', 'Jeff B. Boisvert', 'Clayton V. Deutsch']
I am writing a research paper on a idea A library of training images for fluvial and deepwater reservoirs and associated code
[{"from": "human", "value": "I am writing a research paper on a idea A library of training images for fluvial and deepwater reservoirs and associated code"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Geostatistical algorithms that consider multiple-point statistics are becoming increasingly popular. These methods allow for the reproduction of complicated features beyond the commonly implemented variogram. In practice, it is not possible to infer many multiple-point statistics directly from the available data; therefore, it is common to borrow statistics from training images. A library of training images is developed for fluvial and deepwater depositional settings. These training images are based on object-based models, surface-based models and pseudo-genetic process mimicking (event-based) models. The training images represent a range of net-to-gross fractions and depositional styles. Associated code provides the ability to modify, format and tailor the training images and to extract multiple-point statistics. The training image library provides a source for multiple-point statistics, can be used in comparative flow studies and as an aid in scenario-based uncertainty studies."}]