A stochastic modeling study of H56 block in Daqingzijing oilfield
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ORIGINAL PAPER
A stochastic modeling study of H56 block in Daqingzijing oilfield Jianpeng Hou 1 & Fang Ding 2 & Weilu Li 3
Received: 4 November 2014 / Accepted: 23 June 2015 / Published online: 2 August 2015 # Saudi Society for Geosciences 2015
Abstract After years of water injection, the underground movement of oil and water in Daqingzijing oilfield is becoming more and more complex, and the distribution of reservoir parameters between wells and remaining oil is difficult to predict. The reservoir geological model of H56 block in Daqingzijing oilfield is established with the support of numerous data by applying stochastic modeling techniques; it effectively reproduces the reservoir heterogeneity and distributes parameters in uncertain area between wells. A sequential indicator simulation algorithm is used to simulate the sedimentary facies, and facies-controlled reservoir characteristics have been performed including the reservoir petrophysical parameters and variability in the wells. In order to verify model accuracy, a comparison of porosity and permeability between model data and actual data is essentially made, the fitting of reserves and the original reserves were compared, and the whole field and the single well between the model and the history data were compared; the model shows consistency that gives confidence in using the model for numerical simulation purposes.
Keywords Stochastic modeling . Sedimentary facies . Facies-controlled modeling . Model checking
* Jianpeng Hou [email protected]
Introduction During the late-life stage, with the enhancement of Daqingzijing reservoir development, some drainage problems have been exposed: the contradictions had taken on such as defective well pattern in some areas, unclear understanding of the advantages of water seepage into the direction and influencing factors, and not well targeted measures in potential adjustment. These factors have restrained the efficiency of oil field development, especially, owing to complex fault growth and complex reservoir heterogeneity, the requirements of reservoir modeling become more quantitative and sophisticated. Thus, it is necessary to build a fine-scale reservoir model. Establishing a geological model is the most important part of reservoir description (Zhang, et al. 1997; Qiu and Chen 1996). Stochastic modeling technology is a new technology developed since the middle 1980s (Wang and Zhang 2001; Wang and Shi 1999), and it essentially aids 3D visualization and quantitative prediction on reservoirs and is thus suitable to describe heterogeneity and uncertainty of reservoirs (Zhang et al 1995). Proper use of stochastic modeling methods can enhance the reliability and accuracy of reservoir properties and better predict the distribution of the remaining oil accurately by establishing the 3D geological model and quantitatively describing and modeling geological facies together with petrophysical properties in 3D. Results show that by combining the available subsurface data and performing stochastic modeling, the water flooding r
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