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Reservoir Heterogeneity And Favorable Target Area Prediction Of Guantao Formation In Shu21 Well Area Of Shulu Sag

Posted on:2024-02-14Degree:MasterType:Thesis
Country:ChinaCandidate:Z Y RenFull Text:PDF
GTID:2531307055973679Subject:Earth Exploration and Information Technology
Abstract/Summary:PDF Full Text Request
The reservoir in Shu21 well area of Shulu sag is a stratigraphic-lithologic reservoir with reservoir heterogeneity.The heterogeneity of the carrier layer directly affects the migration path and accumulation mode of oil and gas in the carrier layer.The conducting layer acts as a channel connecting oil source and trap.Studying the heterogeneity of the carrier layer and the migration path of oil and gas is of great significance for mastering the law of oil and gas distribution and predicting favorable targets.In the study,the reservoir anatomy of the Shu 21 well area was first carried out.Analyze the characteristics of oil and water distribution,and clarify the accumulation mode of stratigraphic lithologic reservoirs in the Shu 21 well area.Then the sedimentary facies map is drawn by well seismic combination.Analysis of sedimentary facies types and distribution characteristics.At the same time,BP neural network technology is used to predict the sand body in the transport layer.Filling probability model and sand ratio distribution model are provided for reservoir heterogeneity geological modeling.Then,based on the principle of intrusive infiltration,the dominant path of oil and gas migration is simulated.Combined with the actual drilling data to verify the accuracy of oil and gas migration simulation.Finally,the favorable target area is predicted by oil and gas migration simulation combined with favorable accumulation conditions.The results show that the oil-water distribution in the reservoir of Shu 21 well area is controlled by the heterogeneity of the reservoir.Hydrocarbons mainly accumulate in effective reservoir rocks.But not all effective reservoir rocks are oil-bearing.Some effective reservoir rocks are characterized by water content.Therefore,the Guantao Formation reservoir in the Shu 21 well area is a lithologic reservoir with reservoir heterogeneity supplied by the Shahejie Formation reservoir.During the deposition period of Guantao Formation.It mainly accepts the supply from the northwest source,and the sedimentary microfacies are mainly braided channels,which are distributed in the north-south direction.The braided channel composite sand bodies with lateral and vertical superposition are developed at the bottom of Guantao Formation,which have the characteristics of structural heterogeneity.BP neural network has strong learning ability for nonlinear relationship between data.Its neurons can be trained by learning seismic attributes and sand ratio at well points.The hidden functional relationship between data is obtained and the model is established.The model is used to predict the sand-land ratio of the reservoir.The absolute error of the sand-land ratio predicted by the subsequent well test is basically less than 0.15,the average relative error is 17 %,and the curve trend of the absolute error is gentle.From Ng32 to Ng21,the sand body distribution and sand body range of Shu21 well area are consistent with sedimentary facies and actual drilling data.Therefore,the braided river sedimentary system is taken as the probability model.The prediction results of the sand ratio of the subdivision layer of the Guantao Formation are taken as the constraint.The reservoir heterogeneity geological model of Guantao formation in Shu21 well area is established.According to the principle of invasion and infiltration,the oil and gas migration and accumulation simulation is carried out,and combined with the favorable accumulation conditions,three potential evaluation target areas of Shu21 well area in the northern part of the western slope of Shulu sag are divided.They are located in the southeast of Jin14 well,the east of Jin74-2X well and the north of Jingu 14-110 x well,which provides suggestions for further tapping the potential of the oilfield.
Keywords/Search Tags:bp neural network, reservoir heterogeneity, geological modeling, hydrocarbon migration simulation, Sand ratio prediction
PDF Full Text Request
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