Sam Sun

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Sam Sun
Sun-Sam.jpg
BSc Geology
MSc Geophysics
PhD Geophysics
MSc university University of Calgary
PhD university University of Calgary

Sam Zandong Sun is professor and director of the Lab for Integration of Geology and Geophysics (LIGG) at China University of Petroleum (Beijing). Prior to joining China University of Petroleum, Sam held a variety of positions with functions of exploration, production, and research as a senoir research specialist with ExxonMobil Upstream Research, ExxonMobil Production Company, and Imperial Oil (ESSO Canada) for nearly 14 years. He also worked with Schlumberger, former Mobil Canada, and former Canadian Hunter Exploration Ltd.

Sam worked with CNPC headquarter research for about three years after graduating with a B.Sc. (1984) in geology. He earned both of his M.Sc. (1993) and Ph.D. (1999) in geophysics from University of Calgary, Canada. Sam has been focusing on reservoir monitoring, characterization, and prediction utilizing P-wave and 3D3C pre-stack data by integrating rock physics with amplitude processing including amplitude preserved migration into inversions for rservoir prediction and fluid mapping. Elastic information is very critical for reservoir and fluid prediction. To compromise high acquisition cost Sam strongly believes that prestack PP wave data should be worked harder and employed more often to extract elastic information for reservoir and fluid mapping. For that he leads the group to focus on integration of rock physics, amplitude processing, and inversion algorithms and new technology development to improve these integrations.

Recently, Sam has had a strong interest in unconventional resource development and published the book "Uncoventional Oil & Gas Exploration and Development". He also worked on 3C3D VSP technology development for a long time and published the book "3C3D VSP: Principle Technology and Applications."

Sam has been very active in serving the Society. He is a member of SEG Global Affairs, Research, D&P, and Reserve committees. Also, he serves as a member of SEG Beijing task force and is a member of SEG China advisory committee. He is an active member of SEG, AAPG, and a member of APPEGA. He served as chairman in 2011 of the SEG D&P Forum.

2012 SEG Honorary Lecturer, South and East Asia

The cheapest elastic information: How rock physics models and amplitude processing affect prestack PP inversion

Reservoir characterization and fluid prediction often require elastic information such as P- and S-wave impedance or Vp/Vs. This information can be obtained from 3C data but they are quite often not available. PP-wave prestack inversion is the cheapest way to obtain elastic information, and it is widely available. Because of high acquisition costs, prestack PP-wave data should be worked harder and employed more often to extract elastic information for reservoir and fluid mapping. But, implementing proper prestack inversion requires integration of a rock physics model with amplitude processing that includes amplitude-preserved migration into the prestack inversion. There are many different rock physics models. But, they must be employed properly. Often, people are not aware of the application conditions or prerequisites needed to use a certain model. Amplitude-preserved prestack migration is not critical for prestack inversion of flat reflectors. But today, amplitude-preserved prestack migration for CRP gather extraction has become routine. It is not only because almost all reflectors are structurally related but also because the CRP gather is better for determining the Fresnel zone.

This presentation will focus on three key factors of prestack inversion: rock physics, amplitude processing, and inversion algorithms. The talk will illustrate how to define a better or best rock physics model. It will cover existing and newly developed rock physics models, including the time-average equation, Gassmann equation, Kuster-Toksoz model, Xu-White model, AS-Xu-White model (3D Xu-White model), and DEM-Gassmann model. Berryman (1992) proposed a differential effective medium (DEM) model, in which pores are incrementally added into the matrix, satisfying the demand of "dilute pores," DEM-Gassmann model is a dispersion-corrected Kuster-Toksoz model and has wide application for characterizing reservoirs with sophisticated pore structure and geometry. The talk will also cover amplitude processing, including Kirchhoff and RTM algorithms. Different inversion algorithms will also be analyzed. The talk will include many field examples and case studies.

Interview

"Are you interested and good at pre-stack inversion?" You may confidently tell me "yes".

Figure 1.  Comparison of modeling and inversion results by different rock physics models.

"Do you still excel at seismic rock physics?" Your answer may be uncertain.

"And, how about the seismic amplitude processing?" This time,you may shake your head.

Figure 2.  Cave image with amplitude-preserved processing (a) and without amplitude-preserved processing.

It's difficult to find individuals with skills and experiences in all these three areas mentioned above. However, in order to conduct precise reservoir prediction and fluid mapping, rock physics and amplitude processing should be fully integrated with pre-stack inversion algorithms, but it is not always the case. Therefore, in my lecture, I will try my effort to illustrate how to incorporate rock physics, amplitude processing into pre-stack inversion and reveal their connections.

Why does PP-wave pre-stack inversion become so appealing and fascinating? The reason is quite simple: profit! Currently, PP-wave pre-stack inversion may be the most economic way in obtaining the required elastic information (such as P- and S- wave impedance or Vp/Vs) for reservoir prediction. Although these information can also be obtained by 3C seismic data, they are quite often not available. Because of high acquisition costs, pre-stack inversion should be handled more properly and extensively to extract elastic information for reservoir characterization. Without any doubts, inversion algorithm is the key step. So what kinds of inversion methods should be developed and focused on? Basically AVO and AVAZ inversion techniques are needed. AVO (amplitude versus offset) inversion is a common tool for extracting density and velocity information of the rocks in lithology and fluid prediction. And, fractures will also be our interests, which could act as both the storage spaces and connection channel when the primary pores are compacted. The azimuthal anisotropy caused by fractures can be well considered and tackled by AVAZ inversion to detect fracture density and trend. All these techniques will be discussed in details in the lecture.

Figure 3.  Comparison of pre-stack elastic inversion for complex carbonate reservoir: (a) cave with oil and gas filled; (b) cave with mud filled; (c) weak impedance within bed (representing honeycomb type of storage space).

What effects does amplitude processing have on pre-stack inversion? Given that the phenomenon of amplitude versus offset is the foundation for PP-wave pre-stack inversion, amplitude processing is of course closely connected with inversion. Amplitude-preserved data processing especially for amplitude-preserved migration should be highlighted. Although it is not critical for pre-stack inversion of flat reflectors, today amplitude-preserved migration for Common Reflection Point (CRP) gather extraction has become routine. Whether the amplitude characters of CRP gathers are true or not will directly influence the inversion results. An integrated amplitude-preserved data processing centred on the true amplitude pre-stack depth migration (PSDM) is much more promising. Compared with PSTM, PSDM is more amplitude-preserved. Compared with Kirchhoff PSDM, two-way wave equation migration, known as reverse time migration (RTM) can generate more amplitude-preserved angle-domain gathers and is more preferable for pre-stack inversion.

Why is rock physics essential for PP-wave pre-stack inversion? The reason is that an initial Vp/Vs model should be provided before inversion. An inaccurate model may probably result in invalid or even wrong inversion results. Also, rock physics model is essential for fluid replacement study of reservoir. There are many different rock physics models, but they must be employed properly. Often, people are not aware of the application conditions or prerequisites to use a certain model. Some existing and newly developed rock physics models, including time-average equation, Gassmann equation, Kuster-Toksoz model, Xu-white model, As Xu-white model and DEM-Gassmann model. Berryman (1992) proposed a differential effective medium (DEM) model, in which pores are incrementally added into the matrix satisfying the demand of "dilute pores", DEM-Gaussmann model is a dispersion-corrected Kuster-Toksoz model developed here and has been widely applied for charactering reservoirs with sophisticated pore structure and geometry.

Besides all the techniques I talk about above, many field examples and cases studies will also be presented to illustrate the significance of integration of pre-stack inversion, rock physics and true amplitude processing for the success of reservoir characterization and fluid prediction, without which our efforts will be in vain.

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Sam Sun
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