Abstract
Multi-Point Geostatistics (MPS) is a type of geostatistical method used to estimate the value of an unsampled location by utilizing several data points around it simultaneously. The MPS method estimates it by defining a model based on initial data in the form of a training image, which is a collection of data in the form of a geological conceptual model in the research area with the integration of geological and geophysical knowledge. The MPS method is currently starting to develop because it differs from conventional covariance-based geostatistical methods such as simple kriging and ordinary kriging, which only use a variogram based on the relationship between two points rapidly. In this study, we evaluated the use of the MPS method by using a direct sampling algorithm with Python that will directly sample the training image and then retrieve the data based on the sample data. A braided channel training image is used as the initial model to estimate the distribution of reservoir properties in lithology with sand and shale types. This study shows that MPS could reconstruct geological features better than kriging.
Highlights
In the oil & gas exploration and production phase, geostatistical methods are required to see the distribution of reservoir property data in the area of interest
We choose indicator kriging because it produces probability value like Multi-Point Geostatistics (MPS) result
We cannot input the interpretation of geological features, represented by training image
Summary
In the oil & gas exploration and production phase, geostatistical methods are required to see the distribution of reservoir property data in the area of interest. The training image acts as a spatial relationship in a certain area, so different areas may have unique spatial relations In this project, the MPS technique is performed by using the Direct Sampling (DS) algorithm (Mariethoz, 2009) that requires a training image that represents the geological conditions of the interest zone. 2.2 Direct Sampling Algorithm One of the methods used by MPS in retrieving data is a direct sampling (Figure 1), from Mariethoz (2009) with an illustration as followed: (i) Take the point to estimate, called an unsampled location, on the simulation grid. After the estimated value at an unsampled location is obtained, the searching window will stop, and the algorithm will continue to estimate the unsampled location This simulation is carried out by paying attention to several parameters that must be adjusted, including:. DS will take the lowest distance along with the iterations even though it is higher than the threshold
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