Unilateral Alignment: An interpretable machine learning method for geophysical logs calibration

Most of the existing machine learning studies in logs interpretation do not consider the data distribution discrepancy issue,so the trained model cannot well generalize to the unseen data without calibrating the logs.In this paper,we formulated the geophysical logs calibration problem and give its statistical explanation,and then exhibited an interpretable machine learning method,i.e.,Unilateral Alignment,which could align the logs from one well to another without losing the physical meanings.The involved UA method is an unsupervised feature domain adaptation method,so it does not rely on any labels from cores.The experiments in 3 wells and 6 tasks showed the effectiveness and interpretability from multiple views.

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.

Application of geophysical well logs in solving geologic issues:Past,present and future prospect

Geophysical well logs are widely used in geological fields,however,there are considerable incompatibilities existing in solving geological issues using well log data.This review critically fills the gaps between geology and geophysical well logs,as assessed from peer reviewed papers and from the authors’personal experiences,in the particular goal of solving geological issues using geophysical well logs.The origin and history of geophysical logging are summarized.Next follows a review of the state of knowledge for geophysical well logs in terms of type of specifications,vertical resolution,depth of investigations and demonstrated applications.Then the current status and advances in applications of geophysical well logs in fields of structural geology,sedimentary geology and petroleum geology are discussed.Well logs are used in structural and sedimentary geology in terms of structure detection,in situ stress evaluation,sedimentary characterization,sequence stratigraphy division and fracture prediction.Well logs can also be applied in petroleum geology fields of optimizing sweet spots for hydraulic fracturing in unconventional oil and gas resource.Geophysical well logs are extending their application in other fields of geosciences,and geological issues will be efficiently solved via well logs with the improvements of advanced well log suits.Further work is required in order to improve accuracy and diminish uncertainties by introducing artificial intelligence.This review provides a systematic and clear descriptions of the applications of geophysical well log data along with examples of how the data is displayed and processed for solving geologic problems.

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.

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.