@Article{cmes.2020.08993,
AUTHOR = {Zelong Wang, Xiangui Liu, Haifa Tang, Zhikai Lv, Qunming Liu},
TITLE = {Geophysical and Production Data History Matching Based on  Ensemble Smoother with Multiple Data Assimilation},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {123},
YEAR = {2020},
NUMBER = {2},
PAGES = {873--893},
URL = {http://www.techscience.com/CMES/v123n2/38703},
ISSN = {1526-1506},
ABSTRACT = {The Ensemble Kalman Filter (EnKF), as the most popular sequential data 
assimilation algorithm for history matching, has the intrinsic problem of high computational 
cost and the potential inconsistency of state variables updated at each loop of data 
assimilation and its corresponding reservoir simulated result. This problem forbids the 
reservoir engineers to make the best use of the 4D seismic data, which provides valuable 
information about the fluid change inside the reservoir. Moreover, only matching the 
production data in the past is not enough to accurately forecast the future, and the 
development plan based on the false forecast is very likely to be suboptimal. To solve this
problem, we developed a workflow for geophysical and production data history matching by 
modifying ensemble smoother with multiple data assimilation (ESMDA). In this work, we 
derived the mathematical expressions of ESMDA and discussed its scope of applications. 
The geophysical data we used is P-wave impedance, which is typically included in a basic 
seismic interpretation, and it directly reflects the saturation change in the reservoir. Full 
resolution of the seismic data is not necessary, we subsampled the P-wave impedance data 
to further reduce the computational cost. With our case studies on a benchmark synthetic 
reservoir model, we also showed the supremacy of matching both geophysical and 
production data, than the traditional reservoir history matching merely on the production data: 
the overall percentage error of the observed data is halved, and the variances of the updated 
forecasts are reduced by two orders of the magnitude.},
DOI = {10.32604/cmes.2020.08993}
}