Method for validating a training image for the multipoint geostatistical modeling of the subsoil
According to the invention, a training image denotes, for each cell of a first three-dimensional grid, a respective type of geological facies assigned to said cell. For each cell of a second three-dimensional grid, the morphology of which is matched to estimated geological horizons, the well data comprise a respective type of geological facies estimated for said cell. Two-dimensional patterns, each of which is defined along a respective geological horizon and includes, in a cell containing the intersection of a well with said geological horizon, the type of geological facies estimated for each cell, are defined. The instances of the identified two-dimensional patterns are counted in the training image so as to verify the ability of the training image to account for correlations between wells.
1. A method for exploring a subsoil area, the method comprising:
drilling a plurality of wells in the subsoil area;
conducting observations in the plurality of wells by collecting measurements and samples therein;
obtaining a well data based on the observations conducted in the wells, wherein the well data refer to a second three-dimensional grid of cells having a morphology based on estimated geological horizons in the subsoil area, and the well data include types of geological facies respectively estimated from the observations for the cells of the second three-dimensional grid located along the wells;
constructing, by a processor a training image on a first three-dimensional grid of cells, the training image giving, for each cell of the first three-dimensional grid, a respective type of geological facies assigned to said cell;
verifying, by the processor, representativeness of the training image with respect to the well data to produce a validated training image if representativeness is verified; and
generating and displaying on a computer display a model representing the subsoil area using a multipoint geostatistical technique and the validated training image,
wherein verifying representativeness of the training image with respect to the well data includes an inter-well verification which comprises:
identifying, by the processor, two-dimensional patterns in the well data, each two-dimensional pattern being defined along a respective geological horizon and including, in a cell containing an intersection of a well with said geological horizon in the second three-dimensional grid, the type of geological facies estimated for said cell; and
counting, by the processor, in the training image, occurrences of the two-dimensional patterns identified.
2. The method as claimed in claim 1 , wherein the inter-well verification further comprises:
validating, by the processor, the training image when a number of occurrences of the identified two-dimensional patterns counted in the training image exceeds a threshold.
3. The method as claimed in claim 2 , wherein said threshold is expressed in proportion to a number of possible positions of the two-dimensional pattern in the training image.
4. The method as claimed in claim 1 , wherein each two-dimensional pattern defined for wells intersecting a geological horizon is located via a position of a cell of the second three-dimensional grid containing a centroid of said wells along said geologic horizon.
5. The method as claimed in claim 4 , wherein the counting, in the training image, of the occurrences of the two-dimensional pattern located via the position of the cell of the second three dimensional grid containing the centroid of said wells along said geologic horizon comprises:
scanning, by the processor, the training image, each scanning step placing the two-dimensional pattern on the first three-dimensional grid by aligning the cell containing the centroid of the wells over a reference cell of the first three-dimensional grid and the cells of the two-dimensional pattern respectively containing the intersections between the wells and the geological horizon over target cells of the first three-dimensional grid; and
incrementing, by the processor, a counter each time a scanning step results in the types of facies estimated for the cells of the two-dimensional pattern coinciding with the types of facies respectively assigned to the target cells in the training image.
6. The method as claimed in claim 1 , wherein verifying representativeness of the training image with respect to the well data further comprises an intra-well verification comprising:
estimating, by the processor, a first multipoint statistical measurement along each well in the well data;
estimating, by the processor, a second multipoint statistical measurement along vertical columns of the first three-dimensional grid of cells; and
comparing, by the processor, the two estimated multipoint statistical measurements.
7. The method as claimed in claim 6 , wherein each estimated multipoint statistical measurement is a multipoint density function (MPDF) or a distribution of runs.