Logging curves characteristics and response mechanisms of basaltic facies and sub-facies: A case study from eastern sag of Liaohe depression, Northeast China

Based on five types of conventional logging curves including GR,RLLD,CNL,DEN and AC,and 39 core samples from 30 representative boreholes,the logging characteristics and lithofacies and sub-facies of the basaltic rocks were studied.Three basaltic facies and four sub-facies are recognized from the well logs,includ-ing volcanic conduit facies(post intrusive sub-facies),explosive facies,and effusive lava flow facies(tabular flow,compound flow and hyaloclastite sub-facies).The post intrusive,tabular flow and compound flow sub-facies logging curves are mainly controlled by the distribution of vesiculate zones and vesiculate content,which are characterized by four curves with good correlation.Post intrusive sub-facies are characterized by high RLLD,high DEN,with a micro-dentate logging curve pattern,abrupt contact relationships at the top and base.Tabular flow sub-facies are characterized by high RLLD,high DEN,with a bell-shaped log curve pattern,abrupt contact at the base and gradational contact at the top.Compound flow sub-facies are characterized by medium-low RLLD,with a micro-dentate or finger-like logging curve pattern,abrupt contact at the base and gradational contact at the top.Explosive facies and hyaloclastite sub-facies logging curves are mainly controlled by the distribution of the size and sorting of rock particles,which can be recognized by four kinds of logging curves with poor cor-relation.Explosive facies are characterized by low RLLD,medium-low CNL and low DEN,with a micro-dentate logging curve pattern.Hyaloclastite sub-facies are characterized by low RLLD,high CNL,low DEN and high AC,with a micro-dentate logging curve pattern.The present research is beneficial for the prediction of basaltic reser-voirs not only in the Liaohe depression but also in the other volcanic-sedimentary basins.

Integrative method in lithofacies characteristics and 3D velocity volume of the Permian igneous rocks in H area, Tarim Basin

This paper introduces horizon control, seismic control, logging control and facies control methods through the application of the least squares fitting of logging curves, seismic inversion and facies-controlled techniques. Based on the microgeology and thin section analyses, the lithology, lithofacies and periods of the Permian igneous rocks are described in detail. The seismic inversion and facies-controlled techniques were used to find the distribution characteristics of the igneous rocks and the 3D velocity volume. The least squares fitting of the logging curves overcome the problem that the work area is short of density logging data. Through analysis of thin sections, the lithofacies can be classified into eruption airfall subfacies, eruption pyroclastic flow subfacies and eruption facies.

Enhancing lithofacies machine learning predictions with gamma-ray attributes for boreholes with limited diversity of recorded well logs

Derivative and volatility attributes can be usefully calculated from recorded gamma ray(GR)data to enhance lithofacies classification in wellbores penetrating multiple lithologies.Such attributes extract information about the log curve shape that cannot be readily discerned from the recorded well log data.A logged wellbore section for which 8911 data records are available for the three recorded logs(GR,sonic(DT)and bulk density(PB))is evaluated.That section demonstrates the value of the GR attributes for machine learning(ML)lithofacies predictions.Five feature selection configurations are considered.The 9-var configuration including GR,DT,PB and six GR attributes,and the 7-var configuration of GR and the six GR attributes,provide the most accurate and reproducible lithofacies predictions.The other three feature configurations evaluated do not include the GR attributes but just one to three of the recorded log features.The results of seven ML models and two regression models reveal that K-nearest neighbor(KNN),random forest(RF)and extreme gradient boosting(XGB)are the best performing models.They generate between 14 and 23 misclassification from 8911 data records for the 9-var model.Multi-layer perceptron(MLP)and support vector classification(SVC)do not perform well with the 7-var model which lacks the PB feature displaying the highest correlation with facies class.Annotated confusion matrices reveal that KNN,RF and XGB models can effectively distinguish all facies classes for the 9-var and 7-var configurations(that includes the GR attributes),whereas none of the models can achieve that outcome for the 3-var configuration(that excludes the GR attributes).Accurately distinguishing lithofacies using well-log data in sedimentary sections is an important objective in applied geoscience.The straightforward,GR-attribute method proposed works to improve confidence in ML-lithofacies classifications based on limited recorded well-log data.