Facies logging identification of intermediate-basic volcanic rocks in Huoshiling Formation of Songliao Basin

Volcanic oil and gas reservoirs are generally buried deep,which leads to a high whole-well coring cost,and the degree of development and size of reservoirs are controlled by volcanic facies.Therefore,accurately identifying volcanic facies by logging curves not only provides the basis of volcanic reservoir prediction but also saves costs during exploration.The Songliao Basin is a‘fault-depression superimposed’composite basin with a typical binary filling structure.Abundant types of volcanic lithologies and facies are present in the Lishu fault depression.Volcanic activity is frequent during the sedimentary period of the Huoshiling Formation.Through systematic petrographic identification of the key exploratory well(SN165C)of the Lishu fault-depression,which is a whole-well core,it is found that the Huoshiling Formation in SN165C contains four facies and six subfacies,including the volcanic conduit facies(crypto explosive breccia subfacies),explosive facies(pyroclastic flow and thermal wave base subfacies),effusive facies(upper and lower subfacies),and volcanogenic sedimentary facies(pyroclastic sedimentary subfacies).Combining core,thin section,and logging data,the authors established identification markers and petrographic chart logging phases,and also interpreted the longitudinal variation in volcanic petro-graphic response characteristics to make the charts more applicable to this area's volcanic petrographic interpretation of the Huoshiling Formation.These charts can provide a basis for the further exploration and development of volcanic oil and gas in this area.

Division and identification of vertical reservoir units in Archaeozoic metamorphic buried hill of Bozhong Sag,Bohai Bay Basin,East China

Based on the data associated with cores,sidewall cores,casting thin sections,reservoir physical properties,conventional logging and imaging logging,the classification schemes of vertical reservoir units are proposed for the two types of Archaeozoic buried hills(exposed and covered ones)in the Bozhong Sag,Bohai Bay Basin.The geological characteristics and storage spaces of these reservoir units are described,and their identification markers in conventional and imaging log curves are established.The Archaeozoic metamorphic buried hills can be vertically classified into two primary reservoir units:weathering crust and inner buried hill.The weathering crust contains four secondary units,i.e.,the clay zone,weathered glutenite zone,leached zone,disaggregation zone;and the interiors contain two secondary units,i.e.,interior fracture zone and tight zone.In particular,the inner fracture zone was further divided into cataclasite belts and dense-fracture belts.It is proposed that the favorable reservoirs of exposed Archaeozoic metamorphic buried hills are mainly developed in four parts including weathered glutenite zone,leached zone,disintegration zone superposed with the cataclasite belt and the cataclasite belt of inner fracture zone,and are controlled by both weathering and tectonic actions.Favorable reservoirs in covered Archaeozoic metamorphic buried hills are mainly developed in the weathering crust superposed with the cataclasite belts and the cataclasite belts of inner fracture zone,and are mainly controlled by tectonic actions.

Identification and Evaluation of Low Resistivity Pay Zones by Well Logs and the Petrophysical Research in China

This paper presents an overview of petrophysical research and exploration achievements of low resistivity pay （LRP） zone by well logs in China. It includes geological characteristics and characteristics of well log response of the low resistivity pay zones discovered and evaluated in recent years, as well as the problems in recognizing and evaluating low resistivity pay zones by well logs. The research areas mainly include the Neogene formations in the Bohai Bay Basin, the Triassic formations in the northern Tarim Basin and the Cretaceous formations in the Junggar Basin, The petrophysical research concerning recognition and evaluation of the low resistivity pays, based on their genetic types, is introduced in this paper.

An intelligent prediction method of fractures in tight carbonate reservoirs

An intelligent prediction method for fractures in tight carbonate reservoir has been established by upgrading single-well fracture identification and interwell fracture trend prediction with artificial intelligence,modifying construction of interwell fracture density model,and modeling fracture network and making fracture property equivalence.This method deeply mines fracture information in multi-source isomerous data of different scales to reduce uncertainties of fracture prediction.Based on conventional fracture indicating parameter method,a prediction method of single-well fractures has been worked out by using 3 kinds of artificial intelligence methods to improve fracture identification accuracy from 3 aspects,small sample classification,multi-scale nonlinear feature extraction,and decreasing variance of the prediction model.Fracture prediction by artificial intelligence using seismic attributes provides many details of inter-well fractures.It is combined with fault-related fracture information predicted by numerical simulation of reservoir geomechanics to improve inter-well fracture trend prediction.An interwell fracture density model for fracture network modeling is built by coupling single-well fracture identification and interwell fracture trend through co-sequential simulation.By taking the tight carbonate reservoir of Oligocene-Miocene AS Formation of A Oilfield in Zagros Basin of the Middle East as an example,the proposed prediction method was applied and verified.The single-well fracture identification improves over 15%compared with the conventional fracture indication parameter method in accuracy rate,and the inter-well fracture prediction improves over 25%compared with the composite seismic attribute prediction.The established fracture network model is well consistent with the fluid production index.