Relationship between well pattern density and variation function of stochastic modelling and database establishment
College of Earth Science and Engineering, Shandong University of Science and Technology, 266590 Qingdao, PR China
2 Research Institute of Petroleum Exploration and Development, 100083 Beijing, PR China
3 Laboratory for Marine Mineral Resources, Qingdao National Laboratory for Marine Science and Technology, 266237 Qingdao, PR China
4 The Fourth Gas Production Plant, Changqing Oilfield Branch, PetroChina, 710021 Xi’an, PR China
* Corresponding author: email@example.com
Accepted: 15 September 2020
For 3D geological modelling of oil and gas reservoirs, well pattern density is directly related to the number of samples involved in the calculation, which determines the variation function of stochastic modelling and has great impacts on the results of reservoir modelling. This paper focuses on the relationship between well pattern density and the variogram of stochastic modelling, selects the large Sulige gas field with many well pattern types as the research object, and establishes a variogram database of stochastic models for different well pattern densities. First, the well pattern in the study area is divided into three different types (well patterns A, B, and C) according to well and row space. Several different small blocks (model samples) are selected from each type of well pattern to establish the model, and their reasonable variogram values (major range, minor range and vertical range) are obtained. Then, the variogram values of all model samples with similar well pattern densities are analysed and counted, and the variogram database corresponding to each type of well pattern is established. Finally, the statistical results are applied to the modelling process of other blocks with similar well pattern density to test their accuracy. The results show that the reservoir model established by using the variation function provided in this paper agrees well with the actual geological conditions and that the random model has a high degree of convergence. This database has high adaptability, and the model established is reliable.
© J. Wang et al., published by IFP Energies nouvelles, 2020
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