Characteristics and key parameter calculation of diamictic carbonate reservoirs:An example from the Cambrian Canglangpu Formation,Penglai gas area,Sichuan Basin
The first member of the Lower Cambrian Canglangpu Formation,Penglai gas area,north slope of Leshan-Longlvsi paleou-plift,central Sichuan Basin,is developed with some deposits of fresh shallow-water shelf succeeded by diamictic shallow-water shelf.Among which,siliciclastic and carbonate present the depostional pattern of vertical alternation or horizontal succession.In this mem-ber,there extended a variety of minerals,such as pelite,silicalite,calcite,dolomite and pyrite,which form various rock types.More-over,exhibiting great vertical and horizontal variation in lithology,the reservoirs are characterized by low porosity and permeability.Distinct lithology makes the relation of porosity to permeability vastly different.Most traditional dual-mineral calculation models are not available for the complex mineral composition in diamictic rocks.As a result,the porosity calculation accuracy cannot be guaran-teed.So,calculating the key parameters in diamictic carbonate reservoirs was discussed,namely mineral content and porosity.Based on a host of experimental data on XRD,physical property,and mercury injection together with conventional logging data calibrated by petrochemical analysis and LithoScanner logging,four methods,containing dimensionality reduction,neural network analysis,element content to mineral content inversion,and optimal processing,were established for the Canglangpu Formation to calculate its mineral content.Results show that(i)findings from these four methods are consistent with those from whole rock analysis and lithological scanning.Specifically,the optimal processing achieves the best results,while the dimensionality reduction with the best operability;(ii)taken tri-porosity curves into account and dependent on accurate mineral content,another model to calculate porosity of variable matrix parameters was built for logging evaluation on the diamictic carbonate reservoirs.
Diamictic carbonateDimensionality reductionNeural network analysisElement content to mineral content inversion methodOptimal methodVariable matrix parameter
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