A training image optimization method in multiple-point geostatistics and its application in geological modeling

被引：0
|
作者：
Wang L. [1 ]
Yin Y. [1 ]
Feng W. [1 ]
Duan T. [2 ]
Zhao L. [2 ]
Zhang W. [2 ]
机构：
[1] School of Geosciences, Yangtze University, Wuhan
[2] Sinopec Exploration & Production Research Institute, Beijing
来源：
Shiyou Kantan Yu Kaifa/Petroleum Exploration and Development | 2019年 / 46卷 / 04期
关键词：
Angola; Data event; Multiple-point geostatistics; Plutonio oilfield; Repetition probability; Training image; Turbidite channel;
D O I：
10.11698/PED.2019.04.08
中图分类号：
学科分类号：
摘要：
Based on the analysis of the high-order compatibility optimization method proposed by predecessors, a new training image optimization method based on data event repetition probability is proposed. The basic idea is to extract the data event contained in the condition data and calculate the number of repetitions of the extracted data events and their repetition probability in the training image to obtain two statistical indicators, unmatched ratio and repeated probability variance of data events. The two statistical indicators are used to characterize the diversity and stability of the sedimentary model in the training image and evaluate the matching of the geological volume spatial structure contained in data of the well block to be modeled. The unmatched ratio reflects the completeness of geological model in training image, which is the first choice index. The repeated probability variance reflects the stationarity index of geological model of each training image, and is an auxiliary index. Then, we can integrate the above two indexes to achieve the optimization of training image. Multiple sets of theoretical model tests show that the training image with small variance and low no-matching ratio is the optimal training image. The method is used to optimize the training image of turbidite channel in Plutonio oilfield in Angola. The geological model established by this method is in good agreement with the seismic attributes and can better reproduce the morphological characteristics of the channels and distribution pattern of sands. © 2019, The Editorial Board of Petroleum Exploration and Development. All right reserved.
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页码：703 / 709
页数：6
相关论文
共 25 条
  • [1] Guardiano F.B., Srivastava R.M., Multivariate Geostatistics: Beyond Bivariate Moments, (1993)
  • [2] Wu S., Li W., Multiple-point geostatistics: Theory, application and perspective, Journal of Palaeogeography, 7, 1, pp. 137-144, (2005)
  • [3] Huang J., Mu L., Chen H., Et al., Preliminary study on a depositional interface-based reservoir modeling method, Petroleum Exploration and Development, 40, 5, pp. 591-594, (2013)
  • [4] Yin Y., Zhang C., Li S., Et al., A pattern-based multiple point geostatistics method, Geological Review, 60, 1, pp. 216-221, (2014)
  • [5] Li Y., Hou J., Li Y., Features and hierarchical modeling of carbonate fracture-cavity reservoirs, Petroleum Exploration and Development, 43, 4, pp. 600-606, (2016)
  • [6] Huang W., Wang J., Chen H., Et al., Big data paradox and modeling strategies in geological modeling based on horizontal wells data, Petroleum Exploration and Development, 44, 6, pp. 939-947, (2017)
  • [7] Arpat G.B., Caers J., Conditional simulation with patterns, Mathematical Geology, 39, 2, pp. 177-203, (2007)
  • [8] Boisvert J.B., Pyrcz M.J., Deutsch C.V., Multiple-point statistics for training image selection, Natural Resources Research, 16, 4, pp. 313-321, (2007)
  • [9] Perez C., Mariethoz G., Ortiz J.M., Verifying the high-order consistency of training images with data for multiple-point geostatistics, Computers & Geosciences, 70, pp. 190-205, (2014)
  • [10] Li Y., Wu S., Hou J., Et al., Progress and prospects of reservoir development geology, Petroleum Exploration and Development, 44, 4, pp. 569-579, (2017)
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