Keywords
Sequential indicator simulation
Target-based simulation
Multi-Point geostatistics
Dawangzhuang oilfield
How to Cite
Abstract
As an important modeling parameter in multi-point geostatistics, training images determine the modeling effect to a great extent. It is necessary to evaluate and optimize the applicability of candidate training images before modeling by multi-point geostatistics. Conventional two-dimensional training images can’t describe the overlapping relation of sedimentary facies in space, and there is a deficiency in describing the event relation of single data. This paper puts forward a new training image optimization method. The basic idea is to arrange and analyze sand bodies filled with sedimentary sand bodies in point dams and river channels in different periods. The method of obtaining three-dimensional training images is to use sand thickness maps and sedimentary facies maps for spatial constraints. The simulation test shows that compared with the sedimentary facies model obtained from two-dimensional training images, the sedimentary facies model obtained from three-dimensional training images through multi-point geostatistics has high compatibility and is more in line with geological understanding. On the basis of fully understanding the development characteristics of the sedimentary system and quantitative geometry of sedimentary body in the study area, sedimentary microfacies models based on sequential indicator simulation method and target simulation method are established, respectively. By comparing and analyzing the sedimentary microfacies models established by three different methods, the results show that the multi-point geostatistics can stably present the planar distribution characteristics and overlapping spatial relationship of sedimentary microfacies and reproduce the complex spatial structure and geometric shape of fluvial facies. The model established by this method is more in line with the geological sedimentary model.
References
Haldorsen, H.; Lake L W. A new approach to shale management in field-scale models. SPE10976. 1984: 447- 457. https://doi.org/10.2118/10976-pa
Guo, K.; Shi, J. Study on variation of Donghe sand reservoir in Tazhong 4 oilfield. Xinjiang Oil & Gas. 2008, 4-8+104.
Lv, J.; Wang, X.; Qian, X.; Li, J. Sedimentary microfacies constraint in geological modeling. Fault-Block Oil & Gas Field. 2009, 16(03), 14-16.
Chen, H.; Ding, C.; Du, Y.; Wang, J. Advances of reservoir evaluation researches. Bulletin of Geological Science and Technology. 2015, 34(05), 66-74.
André, G. Journel.Geostatistics: Roadblocks and Challenges. Quantitative Geology and Geostatistics. 1993, (5), 213-224. https://doi.org/10.1007/978-94-011-1739-5_18
Chen, P.; Jiang, N.; Yang, H.; L, X. Reservoir stochastic modeling using geostatistics from two-point to multiple-point. Fault-Block Oil & Gas Field. 2012, 19(05), 596-599.
Turner, AK. Challenges and trends for geological modelling and visualization. Bulletin of Engineering Geology and the Environment. 2006, 65(2), 109-127. https://doi.org/10.1007/s10064-005-0015-0
Wu, S.; Li, Y. Present situation and prospect of reservoir geological modeling. Marine Origin Petroleum Geology. 2007, (03), 53-60.
Wu, S.; Li, W. Multiple-point geostatistics: theory, application and perspective. Journal of Palaeogeography (Chinese Edition). 2005, (01), 137-144.
Feng, G.; Chen,H.; Zhang,L. Stochastic simulation of lithofacies distribution using multi -point geostatistics. Journal of Xi’an Shiyou University( Natural Science Edition). 2005, 20(5), 9-11.
Bai, H.; Ge, Y,; Li, D. Multi- point statistics, algor ithm and simulation analysis. Geo- Information Science. 2006, 8(4), 117-121.
Luo, Y.; Zhao, Y. Application of Multiple-Point Geostatistics in Fluvial Reservoir Stochastic Modeling. Geological Science and Technology Information. 2008, 27(3), 68-71.
Li, S.; Zhang, C.; He,Y. Simulation on petrophysical property trends within channel sandbodies. Journal of Oil and Gas Technology. 2009, 31(1), 23-25.
Han, J.; Wang, X.; Sun, Z.; Li, Z. Simulation of fluvial sedimentary microfacies using multiple-point geostatistics. Special Oil & Gas Reservoirs. 2011, 18(06), 48-51+125.
Strebelle, S.; Journel A G. Reservoir modeling using multipoint statistics. SPE71324, 2001. https://doi.org/10.2118/71324-ms
Mao, F.; Gu, D.; Lian, Y.; Xu, X.; Qian, Z. Thin layer logging response characteristics of Sebei gas field. Qinghai Petroleum. 2013, 31(03), 39-45.
Li, Y.; Yang, C. Strategy of reservoir geological modeling and application of technical methed. Journal of Oil and Gas Technology. 2009, 31(03), 30-35+12.
Ma, X. Study on the application of multi-point statistical method in mature oil field modeling. Xi’an University of Petroleum, 2012.
Yu, J.; Wang, X.; Li, Z.; Zhao, Q. Research on training image based on multi-point geostatistics. China Petroleum and Chemical Standard and Quality. 2013, 33 (23), 116.
Zhang, W.; Lin, C.; Dong, C. Application of multi-point geostatistics in geological modeling of D oilfield in Peru. Journal of China University of Petroleum (Edition of Natural Science).2008, (04), 24-28.
Wang, J.; Ma, X. The application of multi-point geostatistics in reservoir stochastic modeling. Computer Applications of Petroleum. 2012, (02), 15-1603.
Yang, L. Research and application of training image in reservoir multi-point statistical modeling. Xi’an University of Petroleum, 2014.
Chen, T. Study on multi-point geostatistical modeling method of braided river reservoir. Xi’an University of Petroleum, 2014.
Chen, G.; Zhao, F.; Wang, J. Regionalized multiple-point stochastic geological modeling:A case from braided delta sedimentary reservoirs in Qaidam Basin, NW China. Petroleum Exploration and Development. 2015, 42 (05), 638- 645. https://doi.org/10.1016/s1876-3804(15)30065-3
Li, S.; Zhang, C.; Yin, Y. Analysis of reservoir modeling algorithm. Petroleum Industry Press. 2012, 139-142.
Luo, Y.; Zhao, Y. Application of multiple-point geostatistics in fluvial reservoir stochastic modeling. Bulletin of Geological Science and Technology. 2008, (03), 68-72.
Zhang, Q.; Guo F.; Xie, J. Sedimentary microfacies of Ed3 in Dawangzhuang area of Raoyang Sag. Geology and Resources. 2020, 29(03), 252-259.
Yang, L. Research and application of training image in reservoir multi-point statistical modeling. Xi’an Shiyou University. 2014.
L, Y.; Li, J.; Chen, Z.; Du, K. Application of multiple-point geostatistics lithofacies modeling base on 3D training image. Petrochemical Industry Application. 2015, 34(09), 94-100.
Li, S.; Zhang, C.; Yin. Y. Several stochastic modeling methods for fluvial reservoirs. Journal of Xi’an Shiyou University (Natural Science Edition). 2003, (05), 10-16+97.
He, X.; Yang, Y.; Li, Y. Application of two-dimensional trend surface to geological modeling of Chang 8 oil reservoir set in Fuxian area. Lithologic Reservoirs. 2012, 24(01), 100-103.
Lv, Z.; Zhao, C.; Huo, C. Application of fine facies-constrained geological modeling technology in adjustment and tapping oil potential of old oilfields:An example from Suizhong 36-1 oilfield. Lithologic Reservoirs. 2010, 22(03), 100-105.
Song, Z.; Yi, J.; Pang, Z. 3D reservoir geologic modeling and potential tapping in glutenite reservoir: A case study from conglomerate reservoirs in Karamay oilfield. Lithologic Reservoirs. 2007, (04), 99-105.
Wang, J.; Xing, Y. Establishment of three-dimensional geological model by sequential indicator simulation method: a case study of Panguliang Reservoir in Jingan oilfield. Yunnan Chemical Technology. 2019, 46(09), 146-149.
Wang, D.; Yang, Y.; Gong, W.; L, S. The method and application study about stochastic modeling with sedimentary microfacies. Bulletin of Science and Technology. 2004, (02), 121-126.
Zhang, C.; Li, S. A series of techniques for stochastic reservoir modeling. Petroleum Science and Technology Forum. 2007, (03), 37-42.
Li, S.; Zhang, C.; Yin, Y. Several stochastic modeling methods for fluvial reservoirs. Journal of Xi’an Shiyou University (Natural Science Edition). 2003, (05), 10-16+97.
Yin, N.; Zhang, J.; Li, C. Application of improved target based high-precision sedimentary microfacies modeling method in su-14 infill experimental area. Journal of Chengdu University of Technology (Science & Technology Edition). 2017, 44(01), 76-85.
Chen, L. Meandering river reservoir sedimentary characterization and geological modelling at the high water cut oilfield. China University of Geosciences. Beijing. 2011.
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.