Identification of reservoir types in deep carbonates based on mixedkernel machine learning using geophysical logging data

Identification of reservoir types in deep carbonates has always been a great challenge due to complex logging responses caused by the heterogeneous scale and distribution of storage spaces.Traditional cross-plot analysis and empirical formula methods for identifying reservoir types using geophysical logging data have high uncertainty and low efficiency,which cannot accurately reflect the nonlinear relationship between reservoir types and logging data.Recently,the kernel Fisher discriminant analysis(KFD),a kernel-based machine learning technique,attracts attention in many fields because of its strong nonlinear processing ability.However,the overall performance of KFD model may be limited as a single kernel function cannot simultaneously extrapolate and interpolate well,especially for highly complex data cases.To address this issue,in this study,a mixed kernel Fisher discriminant analysis(MKFD)model was established and applied to identify reservoir types of the deep Sinian carbonates in central Sichuan Basin,China.The MKFD model was trained and tested with 453 datasets from 7 coring wells,utilizing GR,CAL,DEN,AC,CNL and RT logs as input variables.The particle swarm optimization(PSO)was adopted for hyper-parameter optimization of MKFD model.To evaluate the model performance,prediction results of MKFD were compared with those of basic-kernel based KFD,RF and SVM models.Subsequently,the built MKFD model was applied in a blind well test,and a variable importance analysis was conducted.The comparison and blind test results demonstrated that MKFD outperformed traditional KFD,RF and SVM in the identification of reservoir types,which provided higher accuracy and stronger generalization.The MKFD can therefore be a reliable method for identifying reservoir types of deep carbonates.

Evaluation on the anisotropic brittleness index of shale rock using geophysical logging

The brittleness index plays a significant role in the hydraulic fracturing design and wellbore stability analysis of shale reservoirs.Various brittleness indices have been proposed to characterize the brittleness of shale rocks,but almost all of them ignored the anisotropy of the brittleness index.Therefore,uniaxial compression testing integrated with geophysical logging was used to provide insights into the anisotropy of the brittleness index for Longmaxi shale,the presented method was utilized to assess brittleness index of Longmaxi shale formation for the interval of 3155e3175 m in CW-1 well.The results indicated that the brittleness index of Longmaxi shale showed a distinct anisotropy,and it achieved the minimum value at β=45°-60°.As the bedding angle increased,the observed brittleness index(BI_(2_β))decreased firstly and increased then,it achieved the lowest value at β=40°-60°,and it is consistent with the uniaxial compression testing results.Compared to the isotropic brittleness index(β=0°),the deviation of the anisotropic brittleness index ranged from 10%to 66.7%,in other words,the anisotropy of brittleness index cannot be ignored for Longmaxi shale.Organic matter content is one of the main intrinsic causes of shale anisotropy,and the anisotropy degree of the brittleness index generally increases with the increase in organic matter content.The present work is valuable for the assessment of anisotropic brittleness for hydraulic fracturing design and wellbore stability analysis.

Evaluation of coal bed methane content using BET adsorption isotherm equation

Coal bed methane is unconventional raw natural gas stored in coal seam with considerable reserves in China.In recent years,as the coal bed methane production,the safety and the use of resources have been paid more attentions.Evaluating coal bed methane content is an urgent problem.A BET adsorption isotherm equation is used to process the experimental data.The various parameters of BET equation under different temperatures are obtained;a theoretical gas content correction factor is proposed,and an evaluation method of actual coal bed methane is established.