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 identi...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.展开更多
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 f...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.展开更多
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 ...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.展开更多
High-yield gas layers,low-yield gas layers and(gas bearing)water layers of Upper Paleozoic coexist in the Shilijiahan zone in the northern Ordos Basin,but gasewater distribution characteristics,laws and influence fact...High-yield gas layers,low-yield gas layers and(gas bearing)water layers of Upper Paleozoic coexist in the Shilijiahan zone in the northern Ordos Basin,but gasewater distribution characteristics,laws and influence factors are not understood well,so the exploration and development of natural gas in this zone are restricted.In this paper,statistical analysis was carried out on the data of Upper Paleozoic formation water in this zone,e.g.salinity,pH value and ion concentration.It is shown that the formation water in this zone is of CaCl_(2)type.Then,the origin,types,controlling factors and spatial distribution characteristics of formation water were figured out by using core,mud logging,well logging and testing data,combined with the classification and evaluation results of geochemical characteristics of formation water.Besides,the logging identification chart of gas,water and dry layers in this zone was established.Finally,the occurrence and distribution laws of reservoir fluids were defined.The formation water of CaCl_(2)type indicates a good sealing capacity in this zone,which is favorable for natural gas accumulation.It is indicated that the reservoir fluids in this zone exist in the state of free water,capillary water and irreducible water.Free water is mainly distributed in the west of this zone,irreducible water in the east,and capillary water in the whole zone.The logging identification chart has been applied in many wells in this zone likeWell Jin 86.The identification result is basically accordant with the gas testing result.It is verified that gas and water layers can be identified effectively based on this logging identification chart.展开更多
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,mo...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.展开更多
基金Supported by projects of the National Natural Science Foundatio n of China(Nos.41972313,41790453).
文摘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.
基金Supported by the National Natural Science Foundation of China(41790453,41972313).
文摘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.
基金Supported by CNPC Innovation Foundation,Research Projects of PetroChina,Xinjiang and Tarim Oil Companies
文摘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.
基金supported by the National Major Science and Technology Project“Accumulation and Key Exploration Technology for Large and Middle Oil and Gas Fields in Clastic Rock”(No.:2011ZX05002-001-002).
文摘High-yield gas layers,low-yield gas layers and(gas bearing)water layers of Upper Paleozoic coexist in the Shilijiahan zone in the northern Ordos Basin,but gasewater distribution characteristics,laws and influence factors are not understood well,so the exploration and development of natural gas in this zone are restricted.In this paper,statistical analysis was carried out on the data of Upper Paleozoic formation water in this zone,e.g.salinity,pH value and ion concentration.It is shown that the formation water in this zone is of CaCl_(2)type.Then,the origin,types,controlling factors and spatial distribution characteristics of formation water were figured out by using core,mud logging,well logging and testing data,combined with the classification and evaluation results of geochemical characteristics of formation water.Besides,the logging identification chart of gas,water and dry layers in this zone was established.Finally,the occurrence and distribution laws of reservoir fluids were defined.The formation water of CaCl_(2)type indicates a good sealing capacity in this zone,which is favorable for natural gas accumulation.It is indicated that the reservoir fluids in this zone exist in the state of free water,capillary water and irreducible water.Free water is mainly distributed in the west of this zone,irreducible water in the east,and capillary water in the whole zone.The logging identification chart has been applied in many wells in this zone likeWell Jin 86.The identification result is basically accordant with the gas testing result.It is verified that gas and water layers can be identified effectively based on this logging identification chart.
基金Supported by the China Youth Program of National Natural Science Foundation(42002134)The 14th Special Support Program of China Postdoctoral Science Foundation(2021T140735).
文摘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.
