Big data paradox and modeling strategies in geological modeling based on horizontal wells data

• Published in: Petroleum exploration and development Vol. 44; no. 6; pp. 993 - 1002
• Main Authors: HUANG, Wensong, WANG, Jiahua, CHEN, Heping, XU, Fang, MENG, Zheng, LI, Yonghao
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.12.2017; KeAi Communications Co., Ltd
• Subjects: big data analysis; big data paradox; geological modeling; horizontal well; variogram; horizontal well; variogram; big data paradox; geological modeling; big data analysis
• ISSN: 1876-3804; 1876-3804
• DOI: 10.1016/S1876-3804(17)30111-8

Based on analysis of horizontal well data characteristics, the differences of data distribution and variogram between vertical and horizontal wells in MPE3 oil field of Orinoco heavy oil belt were compared, and modeling strategies were proposed to cope with the big data paradox when data of horizontal wells was used directly into geologic modeling. The study shows the horizontal wells in the study area contain a large quantity of information, strong directionality of well trajectories and high drilling ratio of sandstone, causing variogram analysis result unconformable to the geologic understanding, and in turn making errors in the modeling of sedimentary microfacies and reservoir physical properties and prediction of probabilistic reserves. Firstly, the distributary channel distribution variogram was analyzed with data of vertical wells, and then the lithofacies framework was established under the control of the sedimentary facies and seismic data. After that, the horizontal wells data revealing high heterogeneity accuracy of reservoir, was combined with the vertical wells data to analyze argillaceous interlayer variograms and the corresponding reservoir lithofacies models were constructed. Finally, reservoir physical property models were generated and the geological reserves were calculated by wellblocks. This reservoir modeling method does not only reflect the geologic features underground, but also improve the accuracy of inter-well sand body prediction, and enhance the reliability of reservoir geologic model ultimately.