Stochastic geological modeling constrained by well test data

被引：0

作者：

Feng G. ^{[1
]}

He Y. ^{[1
]}

Liu H. ^{[2
]}

Chen Y. ^{[3
]}

Zhang P. ^{[1
,4
]}

Xue F. ^{[5
]}

机构：

[1] State Key Laboratory of Reservoir Geology and Development Engineering, Southwest Petroleum University, Chengdu, 610500, Sichuan

[2] The Second Oil Production Plant, Xinjiang Oilfield Company, CNPC, Karamay, 834008, Xinjiang

[3] The Second Oil Production Plant, Henan Oilfield Company, SINOPEC, Nanyang, 473100, Henan

[4] Chongqing Fuling Shale Gas Exploration and Development Company, SINOPEC, Chongqing

[5] Oil and Gas Technology Research Institute, Changqing Oilfield Company, CNPC, Xi'an, 710018, Shaanxi

来源：

Shiyou Diqiu Wuli Kantan/Oil Geophysical Prospecting | 2020年 / 55卷 / 02期

关键词：

Geological modeling; Objective function; Simulated annealing; Stochastic simulation; Well data constraint;

D O I：

10.13810/j.cnki.issn.1000-7210.2020.02.023

中图分类号：

学科分类号：

摘要：

Production performance data have seldom been involved in stochastic simulation; thus, it is difficult to describe such production performance as pressure and yield using the geologic model. We present a method for well test data constrained stochastic modeling. The initial model, which is built through stochastic simulation, is updated using simulated annealing algorithm until the model matches well test data. In this process, bottom hole pressure is calculated using analytic solution combined with numerical solution; this may save more than 90% of computation time. A case study shows that the final model has been improved and matches well test data. © 2020, Editorial Department OIL GEOPHYSICAL PROSPECTING. All right reserved.

引用

页码：435 / 441

页数：6

共 28 条

[1] Huo C., Liu S., Gu L., Et al., A quantitative method for appraising reservoir geological model uncertainty, Petroleum Exploration and Development, 34, 5, pp. 574-579, (2007)
[2] Maschio C., de Carvalho C.P.V., Schiozer D.J., A new methodology to reduce uncertainties in reservoir simulation models using observed data and sampling techniques, Journal of Petroleum Science and Engineering, 72, 1, pp. 110-119, (2010)
[3] Zafari M., Reynolds A.C., Assessing the uncertainty in reservoir description and performance predictions with the ensemble Kalman filter, SPE Annual Technical Conference and Exhibition, (2005)
[4] Yang M., Liu J., Wang Y., Et al., Application of well-seismic joint geology modeling technique in reservoir characterization in Fuyang reservoir, Oil Geophysical Prospecting, 45, pp. 130-133, (2010)
[5] Ye D., Cheng Z., Zheng W., Et al., Complex-structure modeling in seismic inversion, Oil Geophysical Prospecting, 53, pp. 270-275, (2018)
[6] Song J., Liu L., Zhou T., Buildup of complex geologic model of steep slope zone, Oil Geophysical Prospecting, 43, 4, pp. 473-478, (2008)
[7] Song L., Song H., Some thoughts on improving the accuracy of reservoir geological modeling, Petrochemical Technology, 22, 2, pp. 160-161, (2015)
[8] Wu S., Zhang Y., Li S., Et al., Geological constraint principle for improving the accuracy of stochastic reservoir modeling, Journal of China University of Petroleum (Natural Science Edition), 25, 1, pp. 55-58, (2001)
[9] Feitosa G.S., Chu L., Thompson L.G., Et al., Determination of reservoir permeability distributions from pressure buildup data, SPE Annual Technical Conference and Exhibition, (1993)
[10] Li H., Liao X., Chen Q., Determination of permeability distribution by simulated annealing method and well test data, Journal of the University of Petroleum(Natural Science Edition), 25, 2, pp. 34-37, (2001)

← 1 2 3 →