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Research Papers: Oil/Gas Reservoirs

Quantitative Characterization of Dynamic Heterogeneity in Reservoirs With Stratified Noncommunicating Layers

[+] Author and Article Information
Yongge Liu

School of Petroleum Engineering,
China University of Petroleum (East China),
Qingdao, Shandong 266580, China
e-mail: yg.leo@foxmail.com

Huiqing Liu

School of Petroleum Engineering,
China University of Petroleum (Beijing),
Beijing 102249, China
e-mail: lengyuexiaohan@yeah.net

Jian Hou

School of Petroleum Engineering,
China University of Petroleum (East China),
Qingdao, Shandong 266580, China
e-mail: houjian@upc.edu.cn

Qing Wang

School of Petroleum Engineering,
China University of Petroleum (Beijing),
Beijing 102249, China
e-mail: tianmiugo@foxmail.com

Kai Dong

Reservoir Development Services,
Baker Hughes, Inc.,
Houston, TX 77073
e-mail: yg198706@163.com

1Corresponding author.

Contributed by the Petroleum Division of ASME for publication in the JOURNAL OF ENERGY RESOURCES TECHNOLOGY. Manuscript received October 30, 2015; final manuscript received April 9, 2016; published online June 21, 2016. Assoc. Editor: Daoyong (Tony) Yang.

J. Energy Resour. Technol 139(1), 012901 (Jun 21, 2016) (11 pages) Paper No: JERT-15-1412; doi: 10.1115/1.4033624 History: Received October 30, 2015; Revised April 09, 2016

Due to the difference of permeability in reservoir and viscosities between oil and water, oil displacement efficiencies at different locations differ significantly. Also, along with the water flooding process, the differences of oil displacement efficiencies change in time and manifest dynamic characteristics, which is called dynamic heterogeneity in this paper. A new parameter called “conductivity index” (IC) is defined, and the Gini coefficient of IC (GCIC) is selected to quantitatively characterize the dynamic heterogeneity in reservoirs with stratified noncommunicating layers. Then, the changing laws and influential factors of GCIC are investigated by physical experiments and numerical simulation methods. Finally, the application of dynamic heterogeneity in individual-layer water injection technique is studied. Based on the theory of seepage flow mechanics, the formula of IC is derived. IC not only contains static parameters including permeability, water, and oil viscosity but also contains dynamic parameters including water and oil relative permeabilities, which are both function of water saturation and also function of rock type. Therefore, IC can reflect the dynamic heterogeneity caused by water flooding process. A five parallel sandpacks' water flooding experiment is conducted to investigate the changes of dynamic heterogeneity. Results show that the value of GCIC increases rapidly before the water breakthrough of the sandpack with highest permeability. Then, after water breakthrough, GCIC decreases slowly. A new parameter GCI is defined to represent the average increase of GCIC during the water flooding process. By numerical simulation method, the influences of Gini coefficient of permeability (GCP) and oil viscosity on GCI are studied. Results show that GCI increases along with the increase of oil viscosity. And GCI first increases and then decreases along with the increase of GCP. When GCP equals 0.6, GCI gets its maximum value. Taking block P of Shengli Oilfield in China, for example, the changes of dynamic heterogeneity along the water flooding process are studied. Results show that the dynamic heterogeneity of each well group varies greatly before and after water flooding. For some well groups, the relative sizes of GCIC even reverse. The performances of different cases in individual-layer water injection technique are investigated by numerical simulation method. Results show that the case both considering dynamic heterogeneity and the remaining oil volume gets the best performance.

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Figures

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Fig. 1

Experimental setup

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Fig. 2

Relationship between shunt rates and injected PV of water

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Fig. 3

Comparison of Lorenz curves and GCIC: (a) Lorenz curves and (b) relationship between GCIC and PV

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Fig. 4

History matching result

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Fig. 5

Distribution of IC at different times

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Fig. 6

Schematic plot of GCIC

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Fig. 7

Lorenz curve of permeability

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Fig. 8

Influence of GCP on dynamic heterogeneity: (a) GCP = 0.2, (b) GCP = 0.4, (c) GCP = 0.6, and (d) GCP = 0.8

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Fig. 9

Influence of oil viscosity on dynamic heterogeneity: (a) curves of GCIC and (b) GCD and GCI

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Fig. 10

Well patterns of block P

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Fig. 11

Permeability and initial ICD distributions of block P: (a) permeability distribution and (b) initial ICD distribution

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Fig. 12

Three-dimensional distribution of ICD: (a) before water flooding and (b) when watercut reaches 96%

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Fig. 13

Comparison of the oil recoveries

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