Applying multi-point statistical methods to build the facies model for Oligocene formation, X oil field, Cuu Long basin

Abstract

Abstract Multi-point statistic method (MPS) can overcome the inherent disadvantages of traditional method based on variogram and object modeling, simultaneously allow the modeling progress, and become more flexible and rational. The algorithms based on variogram and gridding geological model are able to control the final result under the collection of samples’ data (well data) and another corresponding data (seismic). Though, these methods have trouble in modeling the shape of geological features. Then, object-modeling method can generate digitized geological features with responsible shapes; conversely, a final result in accordance with an input data is difficult to achieve. Combining the advantages of two mentioned methods, MPS describes the relationship of data in space based on the group of adjacent points or has a certain relationship, it allows the generation of digitized geological features corresponding with responsible shapes, and moreover, it is able to control the final result under a collection of input data (whose nature is still the pixel-based). The Oligocene reservoir, X field, was formed in fluvial/lacustrine and sedimentary mainly deposited in Northwest–Southeast, which is primarily affected by latitude—sub-latitude faults’ system. An Oligocene facies model of X field is built based on MPS, and it will show the geological features more clearly than the existing one. It also shows the remarkable ability on controlling the final result. MPS allows to combine a lot of different data (geology, seismic, outcrop, etc.) with the geological viewpoints which are shown by training image and itself proves the superiority over traditional methods. Duration of each model simulation is approximately 3000 s and huge size (over 15 million cells), and it is better while compared with 1717.8750 s in case of sequential simulation by SISIM method and default properties.

Keywords

• Multi-point statistic
• Facies model
• Training image
• Sedimental environment
• Sequential indicator simulation