Abstract: A methodology and system promote hydrocarbon production from a reservoir using steam assisted gravity drainage. The technique comprises deploying sensors in a subsurface environment containing the reservoir. The sensors are used to obtain data on properties related to a steam assisted gravity drainage region of the reservoir. Based on the data collected from the sensors, the amount of steam injected into areas of the reservoir may be adjusted to facilitate, e.g., optimize, production of hydrocarbons.

Abstract: A methodology and system promote hydrocarbon production from a reservoir using steam assisted gravity drainage. The technique comprises deploying sensors in a subsurface environment containing the reservoir. The sensors are used to obtain data on properties related to a steam assisted gravity drainage region of the reservoir. Based on the data collected from the sensors, the amount of steam injected into areas of the reservoir may be adjusted to facilitate, e.g., optimize, production of hydrocarbons.

Abstract: A multipoint geostatistics computer-implemented method for modeling of discrete properties, comprising acquiring by a computer software program a training image made from at least one dimensional array of discrete property values, the values depicting the spatial relationship and variability considered to be typical of a n-dimensional surface to be modeled; constructing a search tree, the tree representing the probability of occurrence of combinations of values of a discrete property value, the construction being performed by counting these occurrences in the training image. The non-branching sequences of the search tree are compressed to what essentially amounts to a single node, by keeping only the relevant information the sequences contain.

Abstract: A multipoint geostatistics computer-implemented method for modeling of discrete properties, comprising acquiring by a computer software program a training image made from at least one dimensional array of discrete property values, the values depicting the spatial relationship and variability considered to be typical of a n-dimensional surface to be modeled; constructing a search tree, the tree representing the probability of occurrence of combinations of values of a discrete property value, the construction being performed by counting these occurrences in the training image. The non-branching sequences of the search tree are compressed to what essentially amounts to a single node, by keeping only the relevant information the sequences contain.