Xuri Huang

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Xuri Huang
BSc Applied Geophysics
MSc Reservoir Engineering
PhD Reservoir Engineering
BSc university University of Petroleum, China
MSc university University of Tulsa
PhD university University of Tulsa

Xuri Huang obtained his PhD (1996) and MS (1994) in reservoir engineering from the University of Tulsa. He graduated from the University of Petroleum (PU), China with a BS degree in applied geophysics in 1985 and worked in an integrated research team of PU for seven years. Xuri worked for WesternGeco from 1996 to 2001 in 4D and reservoir characterization. Currently, he is the president of SunRise PetroSolutions Tech, where he is developing tools to close the loop between reservoir engineering and geophysics. He was an SPE distinguished lecturer in 2009–2010. He has published or presented almost 80 papers in SPE and SEG conferences and journals and served on the organizing committees of several intersociety meetings and workshops.

2014 SEG Honorary Lecturer, South and East Asia

Bridging the chasm between geophysics and reservoir engineering

Reservoir engineering data could play a role in seismic interpretation, model building or updating, and even imaging, but effectively combining engineering and geophysics is a challenge. This lecture will highlight three areas in which the fusion of these disciplines has been successful in recent years.

First, the seismic data and reservoir engineering data are “bridged together” in the attribute domain. The workflow starts with a “data fusion” between seismic attributes and production data. This gives more dynamic understanding of the seismic information, especially for data acquired after production has started. The empirical relationships between the two data types are correlated, which gives more insight into the dynamics of the reservoir.

Secondly, the reservoir or geologic model is updated using the seismic and reservoir engineering data. This approach is integrated into the quality control of the geologic model, production data analysis, and history matching. The geologic model from geostatistical modeling normally has no mechanism to reproduce the seismic response, even when seismic attributes constrain the modeling process. This approach allows QC and fine-tuning of the geologic model according to the seismic response. For history matching, reservoir engineers normally adjust the model to match the production history. This mainly relies on their insight into the reservoir. Geologic or geophysical data are barely revisited in this phase. This causes much uncertainty in prediction. Very frequently, the model after history matching by reservoir engineers and by geophysicists is not consistent. The solution is include a step in our production data analysis and history matching and model updating to constrain the analysis with seismic information.

Thirdly, updating reservoir model properties can be integrated into seismic inversion. The sensitive reservoir properties can be chosen for perturbation. With an iterative process, the reservoir model can be inverted to make model updating more consistent and automatic.

For the second and third areas, the rock model is used to generate synthetic seismic attributes from the reservoir model to compare with observed seismic attributes. This leads to an integration of petrophysics, seismic and production analysis. By iterating between model updating, matching of production history, and seismic attributes, the model becomes more realistic. Using different types of seismic data as constraints (including 3D, 4D, prestack, and multicomponent), the influence of each type can be determined. Another benefit is that this process allows further understanding of the seismic attributes. Finally, with the model constrained by seismic and production data, the optimization also can be guided using G&G data by defining the spatial pattern. The workflow should be a standard way to improve the consistency between reservoir engineers and geoscientists.

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Xuri Huang
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