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Denis Allard

Pierre Biver

Dimitri D'Or

Vincent Henrion

Alexandre Walgenwitz

The present invention relates to a method for determining a truncation diagram for a pluri-Gaussin geological parameter estimation associated with a zone of a real subsoil. The method comprises receiving an auxiliary variable describing the zone, computing a normalized variable based on the auxiliary variable and determining a weighted probability map for the normalized variable, each point of the weighted probability map being in correspondence with a point, of the truncation diagram. Moreover for each point of the truncation diagram, the method further comprises determining an associated geological parameter based on the probability value of each point of the weighted probability map. The method is also used to adapt truncation diagram to global or local target, proportions of the facies model to built.

Truncation diagram determination for a pluri-Gaussian estimation

The invention relates to a method for estimating a set of variables associated with sites of an area, the method comprising the following steps: a) a set of observation data is supplied, b) a variable associated with a site of a first sub-area is estimated by krigeing, using the precision matrix of the first sub-area, c) a second adjacent sub-area is determined, d) a variable associated with a site of the second sub-area is estimated by krigeing, on the basis of the first sub-space, the second sub-space, and the precision matrix determined for the first sub-space.

Computer estimation method, and method for oil exploration and development using such a method

A method of estimating lithologic properties of a geological zone represented by a grid, in which method facies are allocated to the nodes of the grid by sequential stochastic simulation. For a target node (x) from a set of target nodes for scanning iteratively, and for each possible facies (c), a conditional probability (p) of observing said facies is estimated knowing that respective facies have already been allocated to other nodes in the neighborhood. A random draw weighted by these conditional probabilities is then performed. The estimation of the conditional probabilities makes use of univariate probabilities (p) and of bivariate probabilities (p) of observing two facies at two respective nodes of the grid. Each bivariate probability used in estimating the conditional probability relates to the target node (x) and to a node (x) neighboring the target node.

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Avignon, France

Denis Allard