Abstract
The interlayer structure of braid river reservoirs is complex and the interwell prediction is difficult in offshore oil field. Taking CFD11-1 oilfield of NgⅢ sand as an example, based on layer contrasting, the interlayer is divided into muddy interlayer, clay boulder interlayer, and physical interlayer according to lithology. Under the guidance of sedimentary model, we use the geology statistical inversion method to predict the clay boulder interlayer, consistent with the dynamic characteristics of oil production which is used for the prior quality control. The results of this study can objectively reveal the characteristics of interlayer space distribution. Compared with the traditional multi-well comparison and stochastic simulation model, this method is applied to the offshore oil field which is character with wide well space, sparse well network, which has very high application value in predicting the interlayer and deploying of inter-well encryption in the similar oilfield.
Highlights
The braided river reservoir is one of the most important reservoir types in clastic rock reservoir
The interlayer structure of braid river reservoirs is complex and the interwell prediction is difficult in offshore oil field
Taking CFD11-1 oilfield of NgIII sand as an example, based on layer contrasting, the interlayer is divided into muddy interlayer, clay boulder interlayer, and physical interlayer according to lithology
Summary
The braided river reservoir is one of the most important reservoir types in clastic rock reservoir. Many different types of interbeds are often developed in sandy braided river reservoirs, and their shapes, stages and spatial distribution are different. For offshore oilfields with a sparse well pattern, large well spacing and well spacing beyond the width of interbeds [25] [26], how to quantitatively characterize the distribution pattern and mechanism of interbeds between wells in sandy Braided River reservoirs have not been studied in depth. Taking the layered conglomerate intercalation in the braided river reservoir as an example, under the guidance of geological model, the conglomerate distribution is identified by using the inversion model algorithm of geostatistical and the blind well data for testing, avoiding the matching, interference and determination of influencing factors of model section, and expecting to achieve breakthrough knowledge. Geostatistical inversion method is expected to quantitatively characterize sandy braided river reservoir interbeds and provide effective guidance for comprehensive adjustment projects of oilfields. This study provides a good method for oilfields with similar reservoir structure
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