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.

The research on method of interlayer modeling based on seismic inversion and petrophysical facies

Currently,the three-dimensional distribution of interlayer is realized by stochastic modeling.Traditionally,the three-dimensional geological modeling controlled by sedimentary facies models is built on the basis of logging interpretation parameters and geophysical information.Because of shallow gas-cap,the quality of three-dimensional seismic data vertical resolution in research area cannot meet the interlayer research that is below ten meters.Moreover,sedimentary facies cannot commendably reveal interlayer distribution and the well density is very sparse in research area.So,it is difficult for conventional technology to finely describe interlayers.In this document,it uses L1-L2 combined norm constrained inversion to enhance the recognition capability of interlayer in seismic profile and improve the signal to noise ratio,the wave group characteristics and the vertical resolution of three-dimensional data and classifies petrophysical facies of interlayer based on core,sedimentary facies and logging interpretation.The interlayer model which is based on seismic inversion model and petrophysical facies can precisely simulate the distribution of reservoir and interlayer.The results show that the simulation results of this new methodology are consistent with the dynamic production perfectly which provide a better basis for producing and mining remaining oil and a new interlayer modeling method for sparse well density.