Log interpretation of carbonate rocks based on petrophysical facies constraints

The complex pore structure of carbonate reservoirs hinders the correlation between porosity and permeability.In view of the sedimentation,diagenesis,testing,and production characteristics of carbonate reservoirs in the study area,combined with the current trends and advances in well log interpretation techniques for carbonate reservoirs,a log interpretation technology route of“geological information constraint+deep learning”was developed.The principal component analysis(PCA)was employed to establish lithology identification criteria with an accuracy of 91%.The Bayesian stepwise discriminant method was used to construct a sedimentary microfacies identification method with an accuracy of 90.5%.Based on production data,the main lithologies and sedimentary microfacies of effective reservoirs were determined,and 10 petrophysical facies with effective reservoir characteristics were identified.Constrained by petrophysical facies,the mean interpretation error of porosity compared to core analysis results is 2.7%,and the ratio of interpreted permeability to core analysis is within one order of magnitude,averaging 3.6.The research results demonstrate that deep learning algorithms can uncover the correlation in carbonate reservoir well logging data.Integrating geological and production data and selecting appropriate machine learning algorithms can significantly improve the accuracy of well log interpretation for carbonate reservoirs.

A new methodology for identification of potential pay zones from well logs: Intelligent system establishment and application in the Eastern Junggar Basin, China

In recent years, as the exploration practices extend into more complicated formations, conventional well log interpretation has often shown its inaccuracy and limitations in identifying hydrocarbons. The Permian Wutonggou Formation hosts typical clastic reservoirs in the Eastern Junggar Basin. The sophisticated lithology characteristics cause complex pore structures and fluid properties. These all finally cause low well testing agreement rate using conventional methods. Eleven years＇ recent statistics show that 12 out of 15 water layers have been incorrectly identified as being oil or oil/water layers by conventional well log interpretation. This paper proposes a methodology called intelligent prediction and identification system （IPIS）. Firstly, parameters reflecting lithological, petrophysical and electrical responses which are greatly related to reservoir fluids have been selected carefully. They are shale content （Vsh）, numbered rock type （RN）, porosity （φ）, permeability （K）, true resistivity （RT） and spontaneous-potential （SP）. Secondly, Vsh, φ and K are predicted from well logs through artificial neural networks （ANNs）. Finally, all the six parameters are input into a neuro-fuzzy inference machine （NFIM） to get fluids identification results. Eighteen new layers of 145.3 m effective thickness were examined by IPIS. There is full agreement with well testing results. This shows the system＇s accuracy and effectiveness.

Calculation of oil saturation in clay-rich shale reservoirs:A case study of Qing 1 Member of Cretaceous Qingshankou Formation in Gulong Sag,Songliao Basin,NE China

The targeted reservoir,which is referred as the first member of Cretaceous Qingshankou Formation in Gulong Sag,Songliao Basin,NE China,is characterized by the enrichment of clay and lamellation fractures.Aiming at the technical challenge of determining oil saturation of such reservoir,nano-pores were accurately described and located through focused ion beam scanning electron microscopy and quantitative evaluation of minerals by scanning electron microscopy based on Simandoux model,to construct a 4D digital core frame.Electrical parameters of the shale reservoir were determined by finite element simulation,and the oil saturation calculation method suitable for shale was proposed.Comparison between the results from this method with that from real core test and 2D nuclear magnetic log shows that the absolute errors meet the requirements of the current reserve specification in China for clay-rich shale reservoir.Comparison analysis of multiple wells shows that the oil saturation values calculated by this method of several points vertically in single wells and multiple wells on the plane are in agreement with the test results of core samples and the regional deposition pattern,proving the accuracy and applicability of the method model.