4.4 Article

A History Matching Framework to Characterize Fracture Network and Reservoir Properties in Tight Oil

Publisher

ASME
DOI: 10.1115/1.4044767

Keywords

oil/gas reservoirs; petroleum engineering; unconventional petroleum

Categories

Funding

  1. National Natural Science Foundation of China [51704312, U1762213]
  2. National Program for Fundamental Research and Development of China [2015CB250905]
  3. National Science and Technology Major Project [2017ZX05071]
  4. Program for Chang jiang Scholars and Innovative Research Team in University [IRT1294]
  5. National Postdoctoral Program for Innovative Talents [BX201600153]
  6. Natural Science Foundation of Shandong Province [ZR2017BEE009]
  7. China Postdoctoral Science Foundation [2016M600571]
  8. Fundamental Research Funds for the Central Universities [18CX07006A]
  9. China Scholarship Council (CSC) [201806450012]

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Accurately characterizing hydraulic fracture network and tight oil reservoir properties can lay the foundation for the production forecast and development design. In this work, we proposed a history matching framework for tight oil. We first use the Hough transform method to characterize complex fracture network from microseismic data. Then, we put the fracture network into an embedded discrete fracture model (EDFM) to build a tight oil reservoir simulation model. After that, we further couple whale optimization algorithm (WOA) and EDFM to match the field production data. In this way, we can accurately estimate reservoir properties, including matrix permeability and porosity, as well as fracture permeability. We apply the framework to two-field applications in China. One is fractured vertical well in the Songliao Basin of Daqing oilfield. The other one is multi-stage fractured horizontal well in the Jimsar Sag of the Xinjiang oilfield. Results show that if we do not consider tight oil characteristics, the estimated fracture permeability, matrix permeability, and matrix porosity will underestimate 73%, 20%, and 47%, respectively. Because we apply WOA to history matching for the first time, we compare the performance of WOA with ensemble-smoother with multiple data-assimilation (ES-MDA). When we fit six parameters, ES-MDA performs better than WOA. However, when we fit three parameters, WOA performs better than ES-MDA. In addition, for engineering problem, WOA performs well on both convergence speed and stability. Therefore, WOA is recommended in the future application of history matching.

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