It is of great significance to accurately and rapidly identify shale lithofacies in relation to the evaluation and prediction of sweet spots for shale oil and gas reservoirs.To address the problem of low resolution in...It is of great significance to accurately and rapidly identify shale lithofacies in relation to the evaluation and prediction of sweet spots for shale oil and gas reservoirs.To address the problem of low resolution in logging curves,this study establishes a grayscale-phase model based on high-resolution grayscale curves using clustering analysis algorithms for shale lithofacies identification,working with the Shahejie For-mation,Bohai Bay Basin,China.The grayscale phase is defined as the sum of absolute grayscale and relative amplitude as well as their features.The absolute grayscale is the absolute magnitude of the gray values and is utilized for evaluating the material composition(mineral composition+total organic carbon)of shale,while the relative amplitude is the difference between adjacent gray values and is used to identify the shale structure type.The research results show that the grayscale phase model can identify shale lithofacies well,and the accuracy and applicability of this model were verified by the fitting relationship between absolute grayscale and shale mineral composition,as well as corresponding re-lationships between relative amplitudes and laminae development in shales.Four lithofacies are iden-tified in the target layer of the study area:massive mixed shale,laminated mixed shale,massive calcareous shale and laminated calcareous shale.This method can not only effectively characterize the material composition of shale,but also numerically characterize the development degree of shale laminae,and solve the problem that difficult to identify millimeter-scale laminae based on logging curves,which can provide technical support for shale lithofacies identification,sweet spot evaluation and prediction of complex continental lacustrine basins.展开更多
Volcanic reservoirs demonstrate strong heterogeneity and substantial variations in productivity due to the complexity of volcanic eruption and lithology.The main types of reservoir space are not clear,and the dominant...Volcanic reservoirs demonstrate strong heterogeneity and substantial variations in productivity due to the complexity of volcanic eruption and lithology.The main types of reservoir space are not clear,and the dominant lithofacies distribution,particularly the favorable areas for high-quality reservoirs,remains to be determined.In this paper,the Huoshiling Formation in the Dehui faulted depression,Songliao Basin is taken as an example to carry out the multi-scale joint characterization of its pore throat structure,establish a reservoir evaluation standard that considers both the gas content and seepage capacity,and perform reservoir evaluation and play fairway mapping under facies control.The results show that the storage space types of the gas-bearing reservoirs in the faulted depression can be ascribed into three categories and six subcategories according to the pore throat and pore characteristics.In terms of pore sizes,volcaniclastic lava rank the first,followed by volcaniclastic rocks,sedimentary volcaniclastic rocks and volcanic lava.The comprehensive evaluation parameter(Φ·K·Sg,whereΦis porosity,K permeability,and Sggas saturation)of high-quality reservoirs are all greater than 0.1.The volcanic reservoirs in the Stage-III strata are the highest in quality and largest in area of play fairways.The thermal debris flow sub-facies developed at Stage III are mainly seen along the western strike-slip fault zone in the Debei sub-sag and the southwest Nong'an tectonic belt,while those developed at Stage I are distributed along the central and eastern fault zones in the southeastern Baojia sub-sag.The favorable layer evaluation and favorable area delineation under facies control will be of certain reference significance for subsequent exploration and development of volcanic gas reservoirs.展开更多
The black shales of Wufeng and Longmaxi Formation(Late Ordovician-Early Silurian period)in Sichuan Basin are the main strata for marine shale gas exploration,which have a yearly shale gas production of 228×10^(8)...The black shales of Wufeng and Longmaxi Formation(Late Ordovician-Early Silurian period)in Sichuan Basin are the main strata for marine shale gas exploration,which have a yearly shale gas production of 228×10^(8)m^(3)and cumulative shale gas production of 919×10^(8)m^(3).According to the lithological and biological features,filling sequences,sedimentary structures and lab analysis,the authors divided the Wufeng/Guanyinqiao and Longmaxi Formations into shore,tidal flat,shoal,shallow water shelf and deep water shelf facies,and confirmed that a shallow water deposition between the two sets of shales.Although both Formations contain similar shales,their formation mechanisms differ.During the deposition of Wufeng shale,influenced by the Caledonian Movement,the Central Sichuan and Guizhou Uplifts led to the transformation of the Sichuan Basin into a back-bulge basin.Coinstantaneous volcanic activity provided significant nutrients,contributing to the deposition of Wufeng Formation black shales.In contrast,during the deposition of Longmaxi shale,collisions caused basement subsidence,melting glaciers raised sea levels,and renewed volcanic activity provided additional nutrients,leading to Longmaxi Formation black shale accumulation.Considering the basic sedimentary geology and shale gas characteristics,areas such as Suijiang-Leibo-Daguan,Luzhou-Zigong,Weirong-Yongchuan,and Nanchuan-Dingshan are identified as key prospects for future shale gas exploration in the Wufeng-Longmaxi Formations.展开更多
By integrating core observations,logging data and seismic interpretation,this study takes the massive Cretaceous carbonates in the M block of the Santos Basin,Brazil,as an example to establish the sequence filling pat...By integrating core observations,logging data and seismic interpretation,this study takes the massive Cretaceous carbonates in the M block of the Santos Basin,Brazil,as an example to establish the sequence filling pattern of fault-bounded isolated platforms in rift lake basins,reveal the control mechanisms of shoal-body development and reservoir formation,and reconstruct the evolutionary history of lithofacies paleogeography.The following results are obtained.(1)Three tertiary sequences(SQ1-SQ3)are identified in the Lower Cretaceous Itapema-Barra Velha of the M block.During the depositional period of SQ1,the rift basement faults controlled the stratigraphic distribution pattern of thick on both sides and thin in the middle.The strata overlapped to uplift in the early stage.During the depositional period of SQ2-SQ3,the synsedimentary faults controlled the paleogeomorphic reworking process with subsidence in the northwest and uplifting in the northeast,accompanied with the relative fall of lake level.(2)The Lower Cretaceous in the M block was deposited in a littoral-shallow lake,with the lithofacies paleogeographic pattern transiting from the inner clastic shoals and outer shelly shoals in SQ1 to the alternation of mounds and shoals in SQ2-SQ3.(3)Under the joint control of relative lake-level fluctuation,synsedimentary faults and volcanic activity,the shelly shoals in SQ1 tend to accumulated vertically in the raised area,and the mound-shoal complex in SQ2-SQ3 tends to migrate laterally towards the slope-break belt due to the reduction of accommodation space.(4)The evolution pattern of high-energy mounds and shoals,which were vertically accumulated in the early stage and laterally migrated in the later stage,controlled the transformation of high-quality reservoirs from“centralized”to“ring shaped”distribution.The research findings clarify the sedimentary patterns of mounds and shoals and the distribution of favorable reservoirs in the fault-controlled lacustrine isolated platform,providing support for the deepwater hydrocarbon exploration in the subsalt carbonate rocks in the Santos Basin.展开更多
The marine-continental transitional shale of the Upper Permian Longtan Formation is widely distributed in Hunan and shows significant exploration potential.Frequent changes in lithofacies have however notably influenc...The marine-continental transitional shale of the Upper Permian Longtan Formation is widely distributed in Hunan and shows significant exploration potential.Frequent changes in lithofacies have however notably influenced the shale gas enrichment.The strata of the Longtan Formation in the Shaoyang Depression,central Hunan,were taken as the study object for this project.Three lithofacies assemblages were identified:shale interbedded with sandstone layer(SAL),sandstone interbedded with shale layer(ASL)and laminated shale layer(LSL).The SAL shale shows significant variability in hydrocarbon generation potential,which leads to shale gas characterized by'hydrocarbon generation in high total organic carbon(TOC)shale,retention in low TOC shale and accumulation in sandstone'.The ASL shale,influenced by the redox conditions of the depositional environment,shows a lower concentration of organic matter.This results in an enrichment model of'hydrocarbon generation and accumulation in shale,with sealing by sandstone'.The laminar structure of LSL shale causes both quartz and clay minerals to control the reservoir.Shale gas is characterized by'hydrocarbon generation in mud laminae,retention and accumulation in silty laminae,with multiple intra-source migration paths'.In the marine-continental transitional shale gas system,the enrichment intervals of different types of shale gas reservoirs exhibit significant variability.展开更多
Triassic-Jurassic carbonates widely distributed in Eastern Indonesia are believed as oils source rock. The Mesozoic Tokala Formation exhibit source rock potential, as evidenced by high contents of organic matter. Rece...Triassic-Jurassic carbonates widely distributed in Eastern Indonesia are believed as oils source rock. The Mesozoic Tokala Formation exhibit source rock potential, as evidenced by high contents of organic matter. Recent exploration has been conducted in southeastern Sulawesi, targeting the Mesozoic intervals. Therefore, in this study, we attempted to determine source rock potential of Tokala Formation outcropped in southeastern Sulawesi area and its capability to generate hydrocarbon. Five distinct lithofacies were delineated, emphasizing lithological and mineralogical features: foraminifera wackestone (FW), lime mudstone (LM), massive bioturbated calcareous-argillaceous shale (MBCAS), weakly laminated argillaceous-calcareous shale (WLACS), and strongly laminated calcareous-argillaceous shale (SLCAS). Subsequent analyses showed that carbonate-rich samples (FW and LM facies, >50% CaO) had poor source rock potential. Conversely, shale facies with moderate carbonate content (WLACS, MBCAS, and SLCAS, 15% - 50% CaO) had good to excellent source rock characteristics, qualifying them as preferable source rock. In addition, levels of SiO2 and Al2O3 should not be neglected, as these constituents play important roles in clay mineral adsorption. Laminated shale facies with moderate CaO content tended to be more promising as source rock than bioturbated facies. The shale facies of Tokala Formation indicate prospective source rock horizon.展开更多
The saline lacustrine hybrid sedimentary rocks are complex in lithology and unknown for their sedimentary mechanisms.The hybrid sedimentary rocks samples from the Neogene upper Ganchaigou Formation to lower Youshashan...The saline lacustrine hybrid sedimentary rocks are complex in lithology and unknown for their sedimentary mechanisms.The hybrid sedimentary rocks samples from the Neogene upper Ganchaigou Formation to lower Youshashan Formation(N_(1)-N_(2)^(1))in the Fengxi area Qaidam Basin,were investigated through core-log and petrology-geochemistry cross-analysis by using the core,casting thin section,scanning electron microscope,X-ray diffraction,logging,and carbon/oxygen isotopic data.The hybrid sedimentary rocks in the Fengxi area,including terrigenous clastic rock and lacustrine carbonate rock,were deposited in a shallow lake environment far from the source,or occasionally in a semi-deep lake environment,with 5 lithofacies types and 6 microfacies types recognized.Stable carbon and oxygen isotopic compositions reveal that the formation of sedimentary cycles is controlled by a climate-driven compensation-undercompensation cyclic mechanism.A sedimentary cycle model of hybrid sedimentary rocks in an arid and saline setting is proposed.According to this model,in the compensation period,the lake level rises sharply,and microfacies such as mud flat,sand-mud flat and beach are developed,with physical subsidence as the dominant sedimentary mechanism;in the undercompensation period,the lake level falls slowly,and microfacies such as lime-mud flat,lime-dolomite flat and algal mound/mat are developed,with chemical-biological process as the dominant sedimentary mechanism.In the saline lacustrine sedimentary system,lacustrine carbonate rock is mainly formed along with regression,the facies change is not interpreted by the accommodation believed traditionally,but controlled by the temporary fluctuation of lake water chemistry caused by climate change.The research results update the interpreted high-resolution sequence model and genesis of hybrid sedimentary rocks in the saline lacustrine basin and provide a valuable guidance for exploring unconventional hydrocarbons of saline lacustrine facies.展开更多
The lithofacies and thermal maturity of the over-mature Lower Cambrian marine shale in the Northern Guizhou Region,and their impacts on reservoir properties in this shale were analyzed by combining geochemistry,minera...The lithofacies and thermal maturity of the over-mature Lower Cambrian marine shale in the Northern Guizhou Region,and their impacts on reservoir properties in this shale were analyzed by combining geochemistry,mineralogy,and gas adsorption methods.Ten lithofacies were identified,and the dominant lithofacies in the studied shale are lean-total organic carbon(TOC)argillaceous-rich siliceous shale(LTAS),medium-TOC siliceous shale(MTSS),and rich-TOC siliceous shale(RTSS).Since the gas generation potential of organic matter was weak,meso-and macro-pores were compressed or filled during the thermal evolution stage with a vitrinite reflectance(RO)range of 3.0%–4.0%.The controlling factors for methane adsorption capacity in the shale samples are significantly influenced by TOC content rather than thermal maturity.Among the RTSS,MTSS,and LTAS samples,RTSS exhibits the highest favorability for preserving hydrocarbon gas,followed by MTSS.The shale types in this study play a significant role in determining the properties of shale reservoirs,serving as an effective parameter for evaluating shale gas development potential.The RTSS and MTSS with a RO range of 2.0%–3.0%stand out as the most favorable target shale types for shale gas exploration and development.展开更多
Recently,machine learning(ML)has been considered a powerful technological element of different society areas.To transform the computer into a decision maker,several sophisticated methods and algorithms are constantly ...Recently,machine learning(ML)has been considered a powerful technological element of different society areas.To transform the computer into a decision maker,several sophisticated methods and algorithms are constantly created and analyzed.In geophysics,both supervised and unsupervised ML methods have dramatically contributed to the development of seismic and well-log data interpretation.In well-logging,ML algorithms are well-suited for lithologic reconstruction problems,once there is no analytical expressions for computing well-log data produced by a particular rock unit.Additionally,supervised ML methods are strongly dependent on a accurate-labeled training data-set,which is not a simple task to achieve,due to data absences or corruption.Once an adequate supervision is performed,the classification outputs tend to be more accurate than unsupervised methods.This work presents a supervised version of a Self-Organizing Map,named as SSOM,to solve a lithologic reconstruction problem from well-log data.Firstly,we go for a more controlled problem and simulate well-log data directly from an interpreted geologic cross-section.We then define two specific training data-sets composed by density(RHOB),sonic(DT),spontaneous potential(SP)and gamma-ray(GR)logs,all simulated through a Gaussian distribution function per lithology.Once the training data-set is created,we simulate a particular pseudo-well,referred to as classification well,for defining controlled tests.First one comprises a training data-set with no labeled log data of the simulated fault zone.In the second test,we intentionally improve the training data-set with the fault.To bespeak the obtained results for each test,we analyze confusion matrices,logplots,accuracy and precision.Apart from very thin layer misclassifications,the SSOM provides reasonable lithologic reconstructions,especially when the improved training data-set is considered for supervision.The set of numerical experiments shows that our SSOM is extremely well-suited for a supervised lithologic reconstruction,especially to recover lithotypes that are weakly-sampled in the training log-data.On the other hand,some misclassifications are also observed when the cortex could not group the slightly different lithologies.展开更多
Fine identification and division of lithofacies types of continental shale strata is an important basis for the evaluation of shale gas exploration and development potential.At present,however,there is no consensus on...Fine identification and division of lithofacies types of continental shale strata is an important basis for the evaluation of shale gas exploration and development potential.At present,however,there is no consensus on the identification standard and division scheme of shale lithofacies.Taking the continental shale strata of theMiddleeLower Jurassic in the SichuanBasin as an example,this paper established a lithofacies division method bymeans of core observation,whole-rockmineral X-ray diffraction analysis,thin section analysis,total organic carbon(TOC)measurement and heliumporosity measurement after analyzing whole-rock mineral composition and shale characteristics.Then lithofacies types of shale strata were identified and divided,and characteristics of lithofacies assemblages in different scales were investigated.Finally,their significance for shale gas exploration was discussed.The following research results were obtained.First,20 shale lithofacies types of 6 categories are totally identified in this continental shale strata using the newly established three-step lithofacies divisionmethod(whole-rock mineral composition partitione-TOC classification-correction and improvement of mineral texture and sedimentary structure).Among them,mediumehigh TOC clay shale lithofacies,laminaethin layer clay shale lithofacies and lowemedium TOC silty shale lithofacies are dominant,followed by lowemedium TOC shell limy clay shale lithofacies,and TOC bearing and low TOC silty clay shale lithofacies.Second,the average TOC and the average porosity of clay shale lithofacies and shell limestone clay shale lithofacies are higher than those of silty and silty clay shale lithofacies.It is indicated thatmineral composition and lithofacies types of shale have a certain impact on gas source and reservoir performance.Third,three types of assemblages are identified in the continental shale strata,including mudstoneelimestone assemblage,mudstoneesandstone assemblage and mudstoneelimestoneesandstone mixed assemblage,which reflect the sedimentary characteristics of distal region,proximal region and transitional region in the lacustrine environment,respectively;and that the characterization of different lithofacies assemblages is conducive to recognizing the differences between different shale sedimentary environments.Fourth,fine identification and statistic of the number and frequency of limy shell laminae and thin layers in the terrestrial organic-rich shale with high claymineral content can provide a basis for the fracturability evaluation of gas-rich zones and the optimization of optimum exploration and development intervals.展开更多
Sand-rich tight sandstone reservoirs are potential areas for oil and gas exploration. However, the high ratio of sandstone thickness to that of the strata in the formation poses many challenges and uncertainties to tr...Sand-rich tight sandstone reservoirs are potential areas for oil and gas exploration. However, the high ratio of sandstone thickness to that of the strata in the formation poses many challenges and uncertainties to traditional lithofacies paleogeography mapping. Therefore, the prediction of reservoir sweet spots has remained problematic in the field of petroleum exploration. This study provides new insight into resolving this problem, based on the analyses of depositional characteristics of a typical modern sand-rich formation in a shallow braided river delta of the central Sichuan Basin, China. The varieties of sand-rich strata in the braided river delta environment include primary braided channels,secondary distributary channels and the distribution of sediments is controlled by the successive superposed strata deposited in paleogeomorphic valleys. The primary distributary channels have stronger hydrodynamic forces with higher proportions of coarse sand deposits than the secondary distributary channels. Therefore, lithofacies paleogeography mapping is controlled by the geomorphology, valley locations, and the migration of channels. We reconstructed the paleogeomorphology and valley systems that existed prior to the deposition of the Xujiahe Formation. Following this, rock-electro identification model for coarse skeletal sand bodies was constructed based on coring data. The results suggest that skeletal sand bodies in primary distributary channels occur mainly in the valleys and low-lying areas,whereas secondary distributary channels and fine deposits generally occur in the highland areas. The thickness distribution of skeletal sand bodies and lithofacies paleogeography map indicate a positive correlation in primary distributary channels and reservoir thickness. A significant correlation exists between different sedimentary facies and petrophysical properties. In addition, the degree of reservoir development in different sedimentary facies indicates that the mapping method reliably predicts the distribution of sweet spots. The application and understanding of the mapping method provide a reference for exploring tight sandstone reservoirs on a regional basis.展开更多
In this study, we used the multi-resolution graph-based clustering （MRGC） method for determining the electrofacies （EF） and lithofacies （LF） from well log data obtained from the intraplatform bank gas fields loc...In this study, we used the multi-resolution graph-based clustering （MRGC） method for determining the electrofacies （EF） and lithofacies （LF） from well log data obtained from the intraplatform bank gas fields located in the Amu Darya Basin. The MRGC could automatically determine the optimal number of clusters without prior knowledge about the structure or cluster numbers of the analyzed data set and allowed the users to control the level of detail actually needed to define the EF. Based on the LF identification and successful EF calibration using core data, an MRGC EF partition model including five clusters and a quantitative LF interpretation chart were constructed. The EF clusters 1 to 5 were interpreted as lagoon, anhydrite flat, interbank, low-energy bank, and high-energy bank, and the coincidence rate in the cored interval could reach 85%. We concluded that the MRGC could be accurately applied to predict the LF in non-cored but logged wells. Therefore, continuous EF clusters were partitioned and corresponding LF were characteristics ＆different LF were analyzed interpreted, and the distribution and petrophysical in the framework of sequence stratigraphy.展开更多
Oil and gas exploration in lacustrine mud shale has focused on laminated calcareous lithofacies rich in type Ⅰ or type Ⅱ1 organic matter, taking into account the mineralogy and bedding structure, and type and abunda...Oil and gas exploration in lacustrine mud shale has focused on laminated calcareous lithofacies rich in type Ⅰ or type Ⅱ1 organic matter, taking into account the mineralogy and bedding structure, and type and abundance of organic matter. Using the lower third member of the Shahejie Formation, Zhanhua Sag, Jiyang Depression as the target lithology, we applied core description, thin section observations, electron microscopy imaging, nuclear magnetic resonance, and fullbore formation microimager （FMI） to study the mud shale lithofacies and features. First, the lithofacies were classified by considering the bedding structure, lithology, and organic matter and then a lithofacies classification scheme of lacustrine mud shale was proposed. Second, we used optimal filtering of logging data to distinguish the lithologies. Because the fractals of logging data are good indicators of the bedding structure, gamma-ray radiation was used to optimize the structural identification. Total organic carbon content （TOC） and pyrolyzed hydrocarbons （S2） were calculated from the logging data, and the hydrogen index （HI） was obtained to identify the organic matter type of the different strata （HI vs Tmax）. Finally, a method for shale lithofacies identification based on logging data is proposed for exploring mud shale reservoirs and sweet spots from continuous wellbore profiles.展开更多
Lithofacies identification is a crucial work in reservoir characterization and modeling.The vast inter-well area can be supplemented by facies identification of seismic data.However,the relationship between lithofacie...Lithofacies identification is a crucial work in reservoir characterization and modeling.The vast inter-well area can be supplemented by facies identification of seismic data.However,the relationship between lithofacies and seismic information that is affected by many factors is complicated.Machine learning has received extensive attention in recent years,among which support vector machine(SVM) is a potential method for lithofacies classification.Lithofacies classification involves identifying various types of lithofacies and is generally a nonlinear problem,which needs to be solved by means of the kernel function.Multi-kernel learning SVM is one of the main tools for solving the nonlinear problem about multi-classification.However,it is very difficult to determine the kernel function and the parameters,which is restricted by human factors.Besides,its computational efficiency is low.A lithofacies classification method based on local deep multi-kernel learning support vector machine(LDMKL-SVM) that can consider low-dimensional global features and high-dimensional local features is developed.The method can automatically learn parameters of kernel function and SVM to build a relationship between lithofacies and seismic elastic information.The calculation speed will be expedited at no cost with respect to discriminant accuracy for multi-class lithofacies identification.Both the model data test results and the field data application results certify advantages of the method.This contribution offers an effective method for lithofacies recognition and reservoir prediction by using SVM.展开更多
Researches into shale lithofacies,their sedimentary environments and relationship benefit understanding both of sedimentary cycle division and unconventional hydrocarbon exploration in lacustrine basins.Based on a 100...Researches into shale lithofacies,their sedimentary environments and relationship benefit understanding both of sedimentary cycle division and unconventional hydrocarbon exploration in lacustrine basins.Based on a 100~300-m-thick dark shale,mudstone and limestone encountered in the lower third member of the Eocene Shahejie Formation(Es3l member)in Zhanhua Sag,Bohai Bay Basin,eastern China,routine core analysis,thin sectioning,scanning electron microscopy(SEM),mineralogical and geochemical measurements were used to understand detailed facies characterization and paleoclimate in the member.This Es3l shale sediment includes three sedimentary cycles(C3,C2 and C1),from bottom to top,with complex sedimentary characters and spatial distribution.In terms of the composition,texture,bedding and thickness,six lithofacies are recognized in this succession.Some geochemical parameters,such as trace elements(Sr/Ba,Na/Al,V/Ni,V/(V+Ni),U/Th),carbon and oxygen isotopes(δ^(18)O,δ^(13)C),and total organic carbon content(TOC)indicate that the shales were deposited in a deep to semi-deep lake,with the water column being salty,stratified,enclosed and reductive.During cycles C3 and C2 of the middle-lower sections,the climate was arid,and the water was salty and stratified.Laminated and laminar mudstone-limestone was deposited with moderate organic matter(average TOC 1.8%)and good reservoir quality(average porosity 6.5%),which can be regarded as favorable reservoir.During the C1 cycle,a large amount of organic matter was input from outside the basin and this led to high productivity with a more humid climate.Massive calcareous mudstone was deposited,and this is characterized by high TOC(average 3.6%)and moderate porosity(average 4%),and provides favorable source rocks.展开更多
Typically, relationship between well logs and lithofacies is complex, which leads to low accuracy of lithofacies identification. Machine learning (ML) methods are often applied to identify lithofacies using logs label...Typically, relationship between well logs and lithofacies is complex, which leads to low accuracy of lithofacies identification. Machine learning (ML) methods are often applied to identify lithofacies using logs labelled by rock cores. However, these methods have accuracy limits to some extent. To further improve their accuracies, practical and novel ensemble learning strategy and principles are proposed in this work, which allows geologists not familiar with ML to establish a good ML lithofacies identification model and help geologists familiar with ML further improve accuracy of lithofacies identification. The ensemble learning strategy combines ML methods as sub-classifiers to generate a comprehensive lithofacies identification model, which aims to reduce the variance errors in prediction. Each sub-classifier is trained by randomly sampled labelled data with random features. The novelty of this work lies in the ensemble principles making sub-classifiers just overfitting by algorithm parameter setting and sub-dataset sampling. The principles can help reduce the bias errors in the prediction. Two issues are discussed, videlicet (1) whether only a relatively simple single-classifier method can be as sub-classifiers and how to select proper ML methods as sub-classifiers;(2) whether different kinds of ML methods can be combined as sub-classifiers. If yes, how to determine a proper combination. In order to test the effectiveness of the ensemble strategy and principles for lithofacies identification, different kinds of machine learning algorithms are selected as sub-classifiers, including regular classifiers (LDA, NB, KNN, ID3 tree and CART), kernel method (SVM), and ensemble learning algorithms (RF, AdaBoost, XGBoost and LightGBM). In this work, the experiments used a published dataset of lithofacies from Daniudi gas field (DGF) in Ordes Basin, China. Based on a series of comparisons between ML algorithms and their corresponding ensemble models using the ensemble strategy and principles, conclusions are drawn: (1) not only decision tree but also other single-classifiers and ensemble-learning-classifiers can be used as sub-classifiers of homogeneous ensemble learning and the ensemble can improve the accuracy of the original classifiers;(2) the ensemble principles for the introduced homogeneous and heterogeneous ensemble strategy are effective in promoting ML in lithofacies identification;(3) in practice, heterogeneous ensemble is more suitable for building a more powerful lithofacies identification model, though it is complex.展开更多
Through the analysis of logging,field outcrops,cores and geochemical data,and based on the study of the relationships between sea level changes,sequence filling,paleo-geomorphy and lithofacies,the sequence lithofacies...Through the analysis of logging,field outcrops,cores and geochemical data,and based on the study of the relationships between sea level changes,sequence filling,paleo-geomorphy and lithofacies,the sequence lithofacies paleo-geography and evolution process of the Lower Permian Liangshan-Qixia Formation(Qixia Stage for short)in Sichuan Basin and its surrounding areas are restored.The Qixia Stage can be divided into three third-order sequences,in which SQ0,SQ1 and SQ2 are developed in the depression area,and SQ1 and SQ2 are only developed in other areas.The paleo-geomorphy reflected by the thickness of each sequence indicates that before the deposition of the Qixia Stage in the Early Permian,the areas surrounding the Sichuan Basin are characterized by“four uplifts and four depressions”,namely,four paleo-uplifts/paleo-lands of Kangdian,Hannan,Shennongjia and Xuefeng Mountain,and four depressions of Chengdu-Mianyang,Kangdian front,Jiangkou and Yichang;while the interior of the basin is characterized by“secondary uplifts,secondary depressions and alternating convex-concave”.SQ2 is the main shoal forming period of the Qixia Formation,and the high-energy mound shoal facies mainly developed in the highs of sedimentary paleo-geomorphy and the relative slope break zones.The distribution of dolomitic reservoirs(dolomite,limy dolomite and dolomitic limestone)has a good correlation with the sedimentary geomorphic highs and slope break zones.The favorable mound-shoal and dolomitic reservoirs are distributed around depressions at platform-margin and along highs and around sags in the basin.It is pointed out that the platform-margin area in western Sichuan Basin is still the key area for exploration at present;while areas around Chengdu-Mianyang depression and Guangwang secondary depression inside the platform and areas around sags in central Sichuan-southern Sichuan are favorable exploration areas for dolomitic reservoirs of the Qixia Formation in the next step.展开更多
Based on core description,thin section identification,X-ray diffraction analysis,scanning electron microscopy,low-temperature gas adsorption and high-pressure mercury intrusion porosimetry,the shale lithofacies of Sha...Based on core description,thin section identification,X-ray diffraction analysis,scanning electron microscopy,low-temperature gas adsorption and high-pressure mercury intrusion porosimetry,the shale lithofacies of Shan23 sub-member of Permian Shanxi Formation in the east margin of Ordos Basin was systematically analyzed in this study.The Shan23 sub-member has six lithofacies,namely,low TOC clay shale(C-L),low TOC siliceous shale(S-L),medium TOC siliceous shale(S-M),medium TOC hybrid shale(M-M),high TOC siliceous shale(S-H),and high TOC clay shale(C-H).Among them,S-H is the best lithofacies,S-M and M-M are the second best.The C-L and C-H lithofacies,mainly found in the upper part of Shan23 sub-member,generally developed in tide-dominated delta facies;the S-L,S-M,S-H and M-M shales occurring in the lower part of Shan23 sub-member developed in tide-dominated estuarine bay facies.The S-H,S-M and M-M shales have good pore struc-ture and largely organic matter pores and mineral interparticle pores,including interlayer pore in clay minerals,pyrite inter-crystalline pore,and mineral dissolution pore.C-L and S-L shales have mainly mineral interparticle pores and clay mineral in-terlayer pores,and a small amount of organic matter pores,showing poorer pore structure.The C-H shale has organic mi-cro-pores and a small number of interlayer fissures of clay minerals,showing good micro-pore structure,and poor meso-pore and macro-pore structure.The formation of favorable lithofacies is jointly controlled by depositional environment and diagen-esis.Shallow bay-lagoon depositional environment is conducive to the formation of type II2 kerogen which can produce a large number of organic cellular pores.Besides,the rich biogenic silica is conducive to the preservation of primary pores and en-hances the fracability of the shale reservoir.展开更多
How to fit a properly nonlinear classification model from conventional well logs to lithofacies is a key problem for machine learning methods.Kernel methods(e.g.,KFD,SVM,MSVM)are effective attempts to solve this issue...How to fit a properly nonlinear classification model from conventional well logs to lithofacies is a key problem for machine learning methods.Kernel methods(e.g.,KFD,SVM,MSVM)are effective attempts to solve this issue due to abilities of handling nonlinear features by kernel functions.Deep mining of log features indicating lithofacies still needs to be improved for kernel methods.Hence,this work employs deep neural networks to enhance the kernel principal component analysis(KPCA)method and proposes a deep kernel method(DKM)for lithofacies identification using well logs.DKM includes a feature extractor and a classifier.The feature extractor consists of a series of KPCA models arranged according to residual network structure.A gradient-free optimization method is introduced to automatically optimize parameters and structure in DKM,which can avoid complex tuning of parameters in models.To test the validation of the proposed DKM for lithofacies identification,an open-sourced dataset with seven con-ventional logs(GR,CAL,AC,DEN,CNL,LLD,and LLS)and lithofacies labels from the Daniudi Gas Field in China is used.There are eight lithofacies,namely clastic rocks(pebbly,coarse,medium,and fine sand-stone,siltstone,mudstone),coal,and carbonate rocks.The comparisons between DKM and three commonly used kernel methods(KFD,SVM,MSVM)show that(1)DKM(85.7%)outperforms SVM(77%),KFD(79.5%),and MSVM(82.8%)in accuracy of lithofacies identification;(2)DKM is about twice faster than the multi-kernel method(MSVM)with good accuracy.The blind well test in Well D13 indicates that compared with the other three methods DKM improves about 24%in accuracy,35%in precision,41%in recall,and 40%in F1 score,respectively.In general,DKM is an effective method for complex lithofacies identification.This work also discussed the optimal structure and classifier for DKM.Experimental re-sults show that(m_(1),m_(2),O)is the optimal model structure and linear svM is the optimal classifier.(m_(1),m_(2),O)means there are m KPCAs,and then m2 residual units.A workflow to determine an optimal classifier in DKM for lithofacies identification is proposed,too.展开更多
In recent years, natural gas exploration in the Sinian Dengying Formation and shale gas exploration in Doushantuo Formation have made major breakthroughs in the Sichuan Basin and its adjacent areas. However, the sedim...In recent years, natural gas exploration in the Sinian Dengying Formation and shale gas exploration in Doushantuo Formation have made major breakthroughs in the Sichuan Basin and its adjacent areas. However, the sedimentary background of the Doushantuo Formation hasn't been studied systematically. The lithofacies paleogeographic pattern, sedimentary environment, sedimentary evolution and distribution of source rocks during the depositional stage of Doushantuo Formation were systematically analyzed by using a large amount of outcrop data, and a small amount of drilling and seismic data.(1) The sedimentary sequence and stratigraphic distribution of the Sinian Doushantuo Formation in the middle-upper Yangtze region were controlled by paleouplifts and marginal sags. The Doushantuo Formation in the paleouplift region was overlayed with thin thickness, including shore facies, mixed continental shelf facies and atypical carbonate platform facies. The marginal sag had complete strata and large thickness, and developed deep water shelf facies and restricted basin facies.(2) The Doushantuo Formation is divided into four members from bottom to top, and the sedimentary sequence is a complete sedimentary cycle of transgression–high position–regression. The first member is atypical carbonate gentle slope deposit in the early stage of the transgression, the second member is shore-mixed shelf deposit in the extensive transgression period, and the third member is atypical restricted–open sea platform deposit of the high position of the transgression.(3) The second member has organic-rich black shale developed with stable distribution and large thickness, which is an important source rock interval and major shale gas interval. The third member is characterized by microbial carbonate rock and has good storage conditions which is conducive to the accumulation of natural gas, phosphate and other mineral resources, so it is a new area worthy of attention. The Qinling trough and western Hubei trough are favorable areas for exploration of natural gas(including shale gas) and mineral resources such as phosphate and manganese ore.展开更多
基金supported by the National Natural Science Foundation of China(42122017,41821002)the Independent Innovation Research Program of China University of Petroleum(East China)(21CX06001A).
文摘It is of great significance to accurately and rapidly identify shale lithofacies in relation to the evaluation and prediction of sweet spots for shale oil and gas reservoirs.To address the problem of low resolution in logging curves,this study establishes a grayscale-phase model based on high-resolution grayscale curves using clustering analysis algorithms for shale lithofacies identification,working with the Shahejie For-mation,Bohai Bay Basin,China.The grayscale phase is defined as the sum of absolute grayscale and relative amplitude as well as their features.The absolute grayscale is the absolute magnitude of the gray values and is utilized for evaluating the material composition(mineral composition+total organic carbon)of shale,while the relative amplitude is the difference between adjacent gray values and is used to identify the shale structure type.The research results show that the grayscale phase model can identify shale lithofacies well,and the accuracy and applicability of this model were verified by the fitting relationship between absolute grayscale and shale mineral composition,as well as corresponding re-lationships between relative amplitudes and laminae development in shales.Four lithofacies are iden-tified in the target layer of the study area:massive mixed shale,laminated mixed shale,massive calcareous shale and laminated calcareous shale.This method can not only effectively characterize the material composition of shale,but also numerically characterize the development degree of shale laminae,and solve the problem that difficult to identify millimeter-scale laminae based on logging curves,which can provide technical support for shale lithofacies identification,sweet spot evaluation and prediction of complex continental lacustrine basins.
基金funded by the National Natural Science Foundation of China(No.42172145)Natural Science Foundation of Heilongjiang Provincial(No.LH2022D014)。
文摘Volcanic reservoirs demonstrate strong heterogeneity and substantial variations in productivity due to the complexity of volcanic eruption and lithology.The main types of reservoir space are not clear,and the dominant lithofacies distribution,particularly the favorable areas for high-quality reservoirs,remains to be determined.In this paper,the Huoshiling Formation in the Dehui faulted depression,Songliao Basin is taken as an example to carry out the multi-scale joint characterization of its pore throat structure,establish a reservoir evaluation standard that considers both the gas content and seepage capacity,and perform reservoir evaluation and play fairway mapping under facies control.The results show that the storage space types of the gas-bearing reservoirs in the faulted depression can be ascribed into three categories and six subcategories according to the pore throat and pore characteristics.In terms of pore sizes,volcaniclastic lava rank the first,followed by volcaniclastic rocks,sedimentary volcaniclastic rocks and volcanic lava.The comprehensive evaluation parameter(Φ·K·Sg,whereΦis porosity,K permeability,and Sggas saturation)of high-quality reservoirs are all greater than 0.1.The volcanic reservoirs in the Stage-III strata are the highest in quality and largest in area of play fairways.The thermal debris flow sub-facies developed at Stage III are mainly seen along the western strike-slip fault zone in the Debei sub-sag and the southwest Nong'an tectonic belt,while those developed at Stage I are distributed along the central and eastern fault zones in the southeastern Baojia sub-sag.The favorable layer evaluation and favorable area delineation under facies control will be of certain reference significance for subsequent exploration and development of volcanic gas reservoirs.
基金supported by the project of the China Geological Survey(DD20221661).
文摘The black shales of Wufeng and Longmaxi Formation(Late Ordovician-Early Silurian period)in Sichuan Basin are the main strata for marine shale gas exploration,which have a yearly shale gas production of 228×10^(8)m^(3)and cumulative shale gas production of 919×10^(8)m^(3).According to the lithological and biological features,filling sequences,sedimentary structures and lab analysis,the authors divided the Wufeng/Guanyinqiao and Longmaxi Formations into shore,tidal flat,shoal,shallow water shelf and deep water shelf facies,and confirmed that a shallow water deposition between the two sets of shales.Although both Formations contain similar shales,their formation mechanisms differ.During the deposition of Wufeng shale,influenced by the Caledonian Movement,the Central Sichuan and Guizhou Uplifts led to the transformation of the Sichuan Basin into a back-bulge basin.Coinstantaneous volcanic activity provided significant nutrients,contributing to the deposition of Wufeng Formation black shales.In contrast,during the deposition of Longmaxi shale,collisions caused basement subsidence,melting glaciers raised sea levels,and renewed volcanic activity provided additional nutrients,leading to Longmaxi Formation black shale accumulation.Considering the basic sedimentary geology and shale gas characteristics,areas such as Suijiang-Leibo-Daguan,Luzhou-Zigong,Weirong-Yongchuan,and Nanchuan-Dingshan are identified as key prospects for future shale gas exploration in the Wufeng-Longmaxi Formations.
基金Supported by the China National Science and Technology Major Project(2025ZD1403000)CNPC Major Science and Technology Project(2023ZZ19).
文摘By integrating core observations,logging data and seismic interpretation,this study takes the massive Cretaceous carbonates in the M block of the Santos Basin,Brazil,as an example to establish the sequence filling pattern of fault-bounded isolated platforms in rift lake basins,reveal the control mechanisms of shoal-body development and reservoir formation,and reconstruct the evolutionary history of lithofacies paleogeography.The following results are obtained.(1)Three tertiary sequences(SQ1-SQ3)are identified in the Lower Cretaceous Itapema-Barra Velha of the M block.During the depositional period of SQ1,the rift basement faults controlled the stratigraphic distribution pattern of thick on both sides and thin in the middle.The strata overlapped to uplift in the early stage.During the depositional period of SQ2-SQ3,the synsedimentary faults controlled the paleogeomorphic reworking process with subsidence in the northwest and uplifting in the northeast,accompanied with the relative fall of lake level.(2)The Lower Cretaceous in the M block was deposited in a littoral-shallow lake,with the lithofacies paleogeographic pattern transiting from the inner clastic shoals and outer shelly shoals in SQ1 to the alternation of mounds and shoals in SQ2-SQ3.(3)Under the joint control of relative lake-level fluctuation,synsedimentary faults and volcanic activity,the shelly shoals in SQ1 tend to accumulated vertically in the raised area,and the mound-shoal complex in SQ2-SQ3 tends to migrate laterally towards the slope-break belt due to the reduction of accommodation space.(4)The evolution pattern of high-energy mounds and shoals,which were vertically accumulated in the early stage and laterally migrated in the later stage,controlled the transformation of high-quality reservoirs from“centralized”to“ring shaped”distribution.The research findings clarify the sedimentary patterns of mounds and shoals and the distribution of favorable reservoirs in the fault-controlled lacustrine isolated platform,providing support for the deepwater hydrocarbon exploration in the subsalt carbonate rocks in the Santos Basin.
基金financial support from the National Natural Science Foundation of China(Grant Nos.U23B20155 and 42202140)the Science and Technology Innovation Program of Hunan Province(2023RC1021)+1 种基金the China Geological Survey(DD20221659)the Science and Technology Bureau,Changsha,China(kq2208261)。
文摘The marine-continental transitional shale of the Upper Permian Longtan Formation is widely distributed in Hunan and shows significant exploration potential.Frequent changes in lithofacies have however notably influenced the shale gas enrichment.The strata of the Longtan Formation in the Shaoyang Depression,central Hunan,were taken as the study object for this project.Three lithofacies assemblages were identified:shale interbedded with sandstone layer(SAL),sandstone interbedded with shale layer(ASL)and laminated shale layer(LSL).The SAL shale shows significant variability in hydrocarbon generation potential,which leads to shale gas characterized by'hydrocarbon generation in high total organic carbon(TOC)shale,retention in low TOC shale and accumulation in sandstone'.The ASL shale,influenced by the redox conditions of the depositional environment,shows a lower concentration of organic matter.This results in an enrichment model of'hydrocarbon generation and accumulation in shale,with sealing by sandstone'.The laminar structure of LSL shale causes both quartz and clay minerals to control the reservoir.Shale gas is characterized by'hydrocarbon generation in mud laminae,retention and accumulation in silty laminae,with multiple intra-source migration paths'.In the marine-continental transitional shale gas system,the enrichment intervals of different types of shale gas reservoirs exhibit significant variability.
文摘Triassic-Jurassic carbonates widely distributed in Eastern Indonesia are believed as oils source rock. The Mesozoic Tokala Formation exhibit source rock potential, as evidenced by high contents of organic matter. Recent exploration has been conducted in southeastern Sulawesi, targeting the Mesozoic intervals. Therefore, in this study, we attempted to determine source rock potential of Tokala Formation outcropped in southeastern Sulawesi area and its capability to generate hydrocarbon. Five distinct lithofacies were delineated, emphasizing lithological and mineralogical features: foraminifera wackestone (FW), lime mudstone (LM), massive bioturbated calcareous-argillaceous shale (MBCAS), weakly laminated argillaceous-calcareous shale (WLACS), and strongly laminated calcareous-argillaceous shale (SLCAS). Subsequent analyses showed that carbonate-rich samples (FW and LM facies, >50% CaO) had poor source rock potential. Conversely, shale facies with moderate carbonate content (WLACS, MBCAS, and SLCAS, 15% - 50% CaO) had good to excellent source rock characteristics, qualifying them as preferable source rock. In addition, levels of SiO2 and Al2O3 should not be neglected, as these constituents play important roles in clay mineral adsorption. Laminated shale facies with moderate CaO content tended to be more promising as source rock than bioturbated facies. The shale facies of Tokala Formation indicate prospective source rock horizon.
基金Supported by CNPC Prospective and Basic Science and Technology Major Project(2023ZZ02)Petro China Science and Technology Major Project(2021DJ0402.2021DJ0202)。
文摘The saline lacustrine hybrid sedimentary rocks are complex in lithology and unknown for their sedimentary mechanisms.The hybrid sedimentary rocks samples from the Neogene upper Ganchaigou Formation to lower Youshashan Formation(N_(1)-N_(2)^(1))in the Fengxi area Qaidam Basin,were investigated through core-log and petrology-geochemistry cross-analysis by using the core,casting thin section,scanning electron microscope,X-ray diffraction,logging,and carbon/oxygen isotopic data.The hybrid sedimentary rocks in the Fengxi area,including terrigenous clastic rock and lacustrine carbonate rock,were deposited in a shallow lake environment far from the source,or occasionally in a semi-deep lake environment,with 5 lithofacies types and 6 microfacies types recognized.Stable carbon and oxygen isotopic compositions reveal that the formation of sedimentary cycles is controlled by a climate-driven compensation-undercompensation cyclic mechanism.A sedimentary cycle model of hybrid sedimentary rocks in an arid and saline setting is proposed.According to this model,in the compensation period,the lake level rises sharply,and microfacies such as mud flat,sand-mud flat and beach are developed,with physical subsidence as the dominant sedimentary mechanism;in the undercompensation period,the lake level falls slowly,and microfacies such as lime-mud flat,lime-dolomite flat and algal mound/mat are developed,with chemical-biological process as the dominant sedimentary mechanism.In the saline lacustrine sedimentary system,lacustrine carbonate rock is mainly formed along with regression,the facies change is not interpreted by the accommodation believed traditionally,but controlled by the temporary fluctuation of lake water chemistry caused by climate change.The research results update the interpreted high-resolution sequence model and genesis of hybrid sedimentary rocks in the saline lacustrine basin and provide a valuable guidance for exploring unconventional hydrocarbons of saline lacustrine facies.
基金sponsored by the National Natural Science Foundation of China,China(Grant Nos.42162016,42002166,and 42063009)Guizhou Provincial Fund Project,China(Grant No.(2022)ZD005)Geological Exploration Fund Project of Guizhou Province,China(Grant No.52000021MGQSE7S7K6PRP).
文摘The lithofacies and thermal maturity of the over-mature Lower Cambrian marine shale in the Northern Guizhou Region,and their impacts on reservoir properties in this shale were analyzed by combining geochemistry,mineralogy,and gas adsorption methods.Ten lithofacies were identified,and the dominant lithofacies in the studied shale are lean-total organic carbon(TOC)argillaceous-rich siliceous shale(LTAS),medium-TOC siliceous shale(MTSS),and rich-TOC siliceous shale(RTSS).Since the gas generation potential of organic matter was weak,meso-and macro-pores were compressed or filled during the thermal evolution stage with a vitrinite reflectance(RO)range of 3.0%–4.0%.The controlling factors for methane adsorption capacity in the shale samples are significantly influenced by TOC content rather than thermal maturity.Among the RTSS,MTSS,and LTAS samples,RTSS exhibits the highest favorability for preserving hydrocarbon gas,followed by MTSS.The shale types in this study play a significant role in determining the properties of shale reservoirs,serving as an effective parameter for evaluating shale gas development potential.The RTSS and MTSS with a RO range of 2.0%–3.0%stand out as the most favorable target shale types for shale gas exploration and development.
文摘Recently,machine learning(ML)has been considered a powerful technological element of different society areas.To transform the computer into a decision maker,several sophisticated methods and algorithms are constantly created and analyzed.In geophysics,both supervised and unsupervised ML methods have dramatically contributed to the development of seismic and well-log data interpretation.In well-logging,ML algorithms are well-suited for lithologic reconstruction problems,once there is no analytical expressions for computing well-log data produced by a particular rock unit.Additionally,supervised ML methods are strongly dependent on a accurate-labeled training data-set,which is not a simple task to achieve,due to data absences or corruption.Once an adequate supervision is performed,the classification outputs tend to be more accurate than unsupervised methods.This work presents a supervised version of a Self-Organizing Map,named as SSOM,to solve a lithologic reconstruction problem from well-log data.Firstly,we go for a more controlled problem and simulate well-log data directly from an interpreted geologic cross-section.We then define two specific training data-sets composed by density(RHOB),sonic(DT),spontaneous potential(SP)and gamma-ray(GR)logs,all simulated through a Gaussian distribution function per lithology.Once the training data-set is created,we simulate a particular pseudo-well,referred to as classification well,for defining controlled tests.First one comprises a training data-set with no labeled log data of the simulated fault zone.In the second test,we intentionally improve the training data-set with the fault.To bespeak the obtained results for each test,we analyze confusion matrices,logplots,accuracy and precision.Apart from very thin layer misclassifications,the SSOM provides reasonable lithologic reconstructions,especially when the improved training data-set is considered for supervision.The set of numerical experiments shows that our SSOM is extremely well-suited for a supervised lithologic reconstruction,especially to recover lithotypes that are weakly-sampled in the training log-data.On the other hand,some misclassifications are also observed when the cortex could not group the slightly different lithologies.
基金supported by the National Major Science and Technology Project“Evaluation of shale gas exploration potential in continental strata”(No.:2017ZX05036004)Sinopec Technology Development Project“Main control factors of shale gas enrichment and favorable targets in Ziliujing Formation in Northeast Sichuan”(No.:P19017-2).
文摘Fine identification and division of lithofacies types of continental shale strata is an important basis for the evaluation of shale gas exploration and development potential.At present,however,there is no consensus on the identification standard and division scheme of shale lithofacies.Taking the continental shale strata of theMiddleeLower Jurassic in the SichuanBasin as an example,this paper established a lithofacies division method bymeans of core observation,whole-rockmineral X-ray diffraction analysis,thin section analysis,total organic carbon(TOC)measurement and heliumporosity measurement after analyzing whole-rock mineral composition and shale characteristics.Then lithofacies types of shale strata were identified and divided,and characteristics of lithofacies assemblages in different scales were investigated.Finally,their significance for shale gas exploration was discussed.The following research results were obtained.First,20 shale lithofacies types of 6 categories are totally identified in this continental shale strata using the newly established three-step lithofacies divisionmethod(whole-rock mineral composition partitione-TOC classification-correction and improvement of mineral texture and sedimentary structure).Among them,mediumehigh TOC clay shale lithofacies,laminaethin layer clay shale lithofacies and lowemedium TOC silty shale lithofacies are dominant,followed by lowemedium TOC shell limy clay shale lithofacies,and TOC bearing and low TOC silty clay shale lithofacies.Second,the average TOC and the average porosity of clay shale lithofacies and shell limestone clay shale lithofacies are higher than those of silty and silty clay shale lithofacies.It is indicated thatmineral composition and lithofacies types of shale have a certain impact on gas source and reservoir performance.Third,three types of assemblages are identified in the continental shale strata,including mudstoneelimestone assemblage,mudstoneesandstone assemblage and mudstoneelimestoneesandstone mixed assemblage,which reflect the sedimentary characteristics of distal region,proximal region and transitional region in the lacustrine environment,respectively;and that the characterization of different lithofacies assemblages is conducive to recognizing the differences between different shale sedimentary environments.Fourth,fine identification and statistic of the number and frequency of limy shell laminae and thin layers in the terrestrial organic-rich shale with high claymineral content can provide a basis for the fracturability evaluation of gas-rich zones and the optimization of optimum exploration and development intervals.
基金financially supported by the “13th Five-Year Plan” National Science and Technology Major Projects(No. 2016ZX05002006-005)the National Natural Science Foundation of China (No. 41502147)the Sichuan Provincial University “nonconventional oil and gas” scientific research and innovation team construction plan
文摘Sand-rich tight sandstone reservoirs are potential areas for oil and gas exploration. However, the high ratio of sandstone thickness to that of the strata in the formation poses many challenges and uncertainties to traditional lithofacies paleogeography mapping. Therefore, the prediction of reservoir sweet spots has remained problematic in the field of petroleum exploration. This study provides new insight into resolving this problem, based on the analyses of depositional characteristics of a typical modern sand-rich formation in a shallow braided river delta of the central Sichuan Basin, China. The varieties of sand-rich strata in the braided river delta environment include primary braided channels,secondary distributary channels and the distribution of sediments is controlled by the successive superposed strata deposited in paleogeomorphic valleys. The primary distributary channels have stronger hydrodynamic forces with higher proportions of coarse sand deposits than the secondary distributary channels. Therefore, lithofacies paleogeography mapping is controlled by the geomorphology, valley locations, and the migration of channels. We reconstructed the paleogeomorphology and valley systems that existed prior to the deposition of the Xujiahe Formation. Following this, rock-electro identification model for coarse skeletal sand bodies was constructed based on coring data. The results suggest that skeletal sand bodies in primary distributary channels occur mainly in the valleys and low-lying areas,whereas secondary distributary channels and fine deposits generally occur in the highland areas. The thickness distribution of skeletal sand bodies and lithofacies paleogeography map indicate a positive correlation in primary distributary channels and reservoir thickness. A significant correlation exists between different sedimentary facies and petrophysical properties. In addition, the degree of reservoir development in different sedimentary facies indicates that the mapping method reliably predicts the distribution of sweet spots. The application and understanding of the mapping method provide a reference for exploring tight sandstone reservoirs on a regional basis.
基金supported by the National Science and Technology Major Project of China(No.2011ZX05029-003)CNPC Science Research and Technology Development Project,China(No.2013D-0904)
文摘In this study, we used the multi-resolution graph-based clustering （MRGC） method for determining the electrofacies （EF） and lithofacies （LF） from well log data obtained from the intraplatform bank gas fields located in the Amu Darya Basin. The MRGC could automatically determine the optimal number of clusters without prior knowledge about the structure or cluster numbers of the analyzed data set and allowed the users to control the level of detail actually needed to define the EF. Based on the LF identification and successful EF calibration using core data, an MRGC EF partition model including five clusters and a quantitative LF interpretation chart were constructed. The EF clusters 1 to 5 were interpreted as lagoon, anhydrite flat, interbank, low-energy bank, and high-energy bank, and the coincidence rate in the cored interval could reach 85%. We concluded that the MRGC could be accurately applied to predict the LF in non-cored but logged wells. Therefore, continuous EF clusters were partitioned and corresponding LF were characteristics ＆different LF were analyzed interpreted, and the distribution and petrophysical in the framework of sequence stratigraphy.
基金This work was supported by the National Natural Science Foundation of China （Nos. 41202110 and 51674211） and Open Fund of State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation （Southwest Petroleum University） （No. PLN201612）, the Applied Basic Research Projects in Sichuan Province （No. 2015JY0200） and the Open Fund Project from Sichuan Key Laboratory of Natural Gas Geology （No. 2015trqdz07）.
文摘Oil and gas exploration in lacustrine mud shale has focused on laminated calcareous lithofacies rich in type Ⅰ or type Ⅱ1 organic matter, taking into account the mineralogy and bedding structure, and type and abundance of organic matter. Using the lower third member of the Shahejie Formation, Zhanhua Sag, Jiyang Depression as the target lithology, we applied core description, thin section observations, electron microscopy imaging, nuclear magnetic resonance, and fullbore formation microimager （FMI） to study the mud shale lithofacies and features. First, the lithofacies were classified by considering the bedding structure, lithology, and organic matter and then a lithofacies classification scheme of lacustrine mud shale was proposed. Second, we used optimal filtering of logging data to distinguish the lithologies. Because the fractals of logging data are good indicators of the bedding structure, gamma-ray radiation was used to optimize the structural identification. Total organic carbon content （TOC） and pyrolyzed hydrocarbons （S2） were calculated from the logging data, and the hydrogen index （HI） was obtained to identify the organic matter type of the different strata （HI vs Tmax）. Finally, a method for shale lithofacies identification based on logging data is proposed for exploring mud shale reservoirs and sweet spots from continuous wellbore profiles.
基金financially supported by the National Natural Science Foundation of China (41774129, 41904116)the Foundation Research Project of Shaanxi Provincial Key Laboratory of Geological Support for Coal Green Exploitation (MTy2019-20)。
文摘Lithofacies identification is a crucial work in reservoir characterization and modeling.The vast inter-well area can be supplemented by facies identification of seismic data.However,the relationship between lithofacies and seismic information that is affected by many factors is complicated.Machine learning has received extensive attention in recent years,among which support vector machine(SVM) is a potential method for lithofacies classification.Lithofacies classification involves identifying various types of lithofacies and is generally a nonlinear problem,which needs to be solved by means of the kernel function.Multi-kernel learning SVM is one of the main tools for solving the nonlinear problem about multi-classification.However,it is very difficult to determine the kernel function and the parameters,which is restricted by human factors.Besides,its computational efficiency is low.A lithofacies classification method based on local deep multi-kernel learning support vector machine(LDMKL-SVM) that can consider low-dimensional global features and high-dimensional local features is developed.The method can automatically learn parameters of kernel function and SVM to build a relationship between lithofacies and seismic elastic information.The calculation speed will be expedited at no cost with respect to discriminant accuracy for multi-class lithofacies identification.Both the model data test results and the field data application results certify advantages of the method.This contribution offers an effective method for lithofacies recognition and reservoir prediction by using SVM.
基金This work is granted by the China State Lithologic Key Program(grant no.2017ZX05001-002-002).
文摘Researches into shale lithofacies,their sedimentary environments and relationship benefit understanding both of sedimentary cycle division and unconventional hydrocarbon exploration in lacustrine basins.Based on a 100~300-m-thick dark shale,mudstone and limestone encountered in the lower third member of the Eocene Shahejie Formation(Es3l member)in Zhanhua Sag,Bohai Bay Basin,eastern China,routine core analysis,thin sectioning,scanning electron microscopy(SEM),mineralogical and geochemical measurements were used to understand detailed facies characterization and paleoclimate in the member.This Es3l shale sediment includes three sedimentary cycles(C3,C2 and C1),from bottom to top,with complex sedimentary characters and spatial distribution.In terms of the composition,texture,bedding and thickness,six lithofacies are recognized in this succession.Some geochemical parameters,such as trace elements(Sr/Ba,Na/Al,V/Ni,V/(V+Ni),U/Th),carbon and oxygen isotopes(δ^(18)O,δ^(13)C),and total organic carbon content(TOC)indicate that the shales were deposited in a deep to semi-deep lake,with the water column being salty,stratified,enclosed and reductive.During cycles C3 and C2 of the middle-lower sections,the climate was arid,and the water was salty and stratified.Laminated and laminar mudstone-limestone was deposited with moderate organic matter(average TOC 1.8%)and good reservoir quality(average porosity 6.5%),which can be regarded as favorable reservoir.During the C1 cycle,a large amount of organic matter was input from outside the basin and this led to high productivity with a more humid climate.Massive calcareous mudstone was deposited,and this is characterized by high TOC(average 3.6%)and moderate porosity(average 4%),and provides favorable source rocks.
基金financially supported by the National Natural Science Foundation of China(Grant No.42002134)China Postdoctoral Science Foundation(Grant No.2021T140735)Science Foundation of China University of Petroleum,Beijing(Grant Nos.2462020XKJS02 and 2462020YXZZ004).
文摘Typically, relationship between well logs and lithofacies is complex, which leads to low accuracy of lithofacies identification. Machine learning (ML) methods are often applied to identify lithofacies using logs labelled by rock cores. However, these methods have accuracy limits to some extent. To further improve their accuracies, practical and novel ensemble learning strategy and principles are proposed in this work, which allows geologists not familiar with ML to establish a good ML lithofacies identification model and help geologists familiar with ML further improve accuracy of lithofacies identification. The ensemble learning strategy combines ML methods as sub-classifiers to generate a comprehensive lithofacies identification model, which aims to reduce the variance errors in prediction. Each sub-classifier is trained by randomly sampled labelled data with random features. The novelty of this work lies in the ensemble principles making sub-classifiers just overfitting by algorithm parameter setting and sub-dataset sampling. The principles can help reduce the bias errors in the prediction. Two issues are discussed, videlicet (1) whether only a relatively simple single-classifier method can be as sub-classifiers and how to select proper ML methods as sub-classifiers;(2) whether different kinds of ML methods can be combined as sub-classifiers. If yes, how to determine a proper combination. In order to test the effectiveness of the ensemble strategy and principles for lithofacies identification, different kinds of machine learning algorithms are selected as sub-classifiers, including regular classifiers (LDA, NB, KNN, ID3 tree and CART), kernel method (SVM), and ensemble learning algorithms (RF, AdaBoost, XGBoost and LightGBM). In this work, the experiments used a published dataset of lithofacies from Daniudi gas field (DGF) in Ordes Basin, China. Based on a series of comparisons between ML algorithms and their corresponding ensemble models using the ensemble strategy and principles, conclusions are drawn: (1) not only decision tree but also other single-classifiers and ensemble-learning-classifiers can be used as sub-classifiers of homogeneous ensemble learning and the ensemble can improve the accuracy of the original classifiers;(2) the ensemble principles for the introduced homogeneous and heterogeneous ensemble strategy are effective in promoting ML in lithofacies identification;(3) in practice, heterogeneous ensemble is more suitable for building a more powerful lithofacies identification model, though it is complex.
基金Supported by the PetroChina and Southwest Petroleum University Innovation Consortium Science and Technology Cooperation Project(2020CX010000)Basic Forward-Looking Project in Upstream Field of CNPC(2021DJ0501)General Program of NSFC(42172166).
文摘Through the analysis of logging,field outcrops,cores and geochemical data,and based on the study of the relationships between sea level changes,sequence filling,paleo-geomorphy and lithofacies,the sequence lithofacies paleo-geography and evolution process of the Lower Permian Liangshan-Qixia Formation(Qixia Stage for short)in Sichuan Basin and its surrounding areas are restored.The Qixia Stage can be divided into three third-order sequences,in which SQ0,SQ1 and SQ2 are developed in the depression area,and SQ1 and SQ2 are only developed in other areas.The paleo-geomorphy reflected by the thickness of each sequence indicates that before the deposition of the Qixia Stage in the Early Permian,the areas surrounding the Sichuan Basin are characterized by“four uplifts and four depressions”,namely,four paleo-uplifts/paleo-lands of Kangdian,Hannan,Shennongjia and Xuefeng Mountain,and four depressions of Chengdu-Mianyang,Kangdian front,Jiangkou and Yichang;while the interior of the basin is characterized by“secondary uplifts,secondary depressions and alternating convex-concave”.SQ2 is the main shoal forming period of the Qixia Formation,and the high-energy mound shoal facies mainly developed in the highs of sedimentary paleo-geomorphy and the relative slope break zones.The distribution of dolomitic reservoirs(dolomite,limy dolomite and dolomitic limestone)has a good correlation with the sedimentary geomorphic highs and slope break zones.The favorable mound-shoal and dolomitic reservoirs are distributed around depressions at platform-margin and along highs and around sags in the basin.It is pointed out that the platform-margin area in western Sichuan Basin is still the key area for exploration at present;while areas around Chengdu-Mianyang depression and Guangwang secondary depression inside the platform and areas around sags in central Sichuan-southern Sichuan are favorable exploration areas for dolomitic reservoirs of the Qixia Formation in the next step.
基金China National Science and Technology Major Project(2017ZX05035).
文摘Based on core description,thin section identification,X-ray diffraction analysis,scanning electron microscopy,low-temperature gas adsorption and high-pressure mercury intrusion porosimetry,the shale lithofacies of Shan23 sub-member of Permian Shanxi Formation in the east margin of Ordos Basin was systematically analyzed in this study.The Shan23 sub-member has six lithofacies,namely,low TOC clay shale(C-L),low TOC siliceous shale(S-L),medium TOC siliceous shale(S-M),medium TOC hybrid shale(M-M),high TOC siliceous shale(S-H),and high TOC clay shale(C-H).Among them,S-H is the best lithofacies,S-M and M-M are the second best.The C-L and C-H lithofacies,mainly found in the upper part of Shan23 sub-member,generally developed in tide-dominated delta facies;the S-L,S-M,S-H and M-M shales occurring in the lower part of Shan23 sub-member developed in tide-dominated estuarine bay facies.The S-H,S-M and M-M shales have good pore struc-ture and largely organic matter pores and mineral interparticle pores,including interlayer pore in clay minerals,pyrite inter-crystalline pore,and mineral dissolution pore.C-L and S-L shales have mainly mineral interparticle pores and clay mineral in-terlayer pores,and a small amount of organic matter pores,showing poorer pore structure.The C-H shale has organic mi-cro-pores and a small number of interlayer fissures of clay minerals,showing good micro-pore structure,and poor meso-pore and macro-pore structure.The formation of favorable lithofacies is jointly controlled by depositional environment and diagen-esis.Shallow bay-lagoon depositional environment is conducive to the formation of type II2 kerogen which can produce a large number of organic cellular pores.Besides,the rich biogenic silica is conducive to the preservation of primary pores and en-hances the fracability of the shale reservoir.
基金supported by the National Natural Science Foundation of China(Grant No.42002134)China Postdoctoral Science Foundation(Grant No.2021T140735)Science Foundation of China University of Petroleum,Beijing(Grant Nos.2462020XKJS02 and 2462020YXZZ004).
文摘How to fit a properly nonlinear classification model from conventional well logs to lithofacies is a key problem for machine learning methods.Kernel methods(e.g.,KFD,SVM,MSVM)are effective attempts to solve this issue due to abilities of handling nonlinear features by kernel functions.Deep mining of log features indicating lithofacies still needs to be improved for kernel methods.Hence,this work employs deep neural networks to enhance the kernel principal component analysis(KPCA)method and proposes a deep kernel method(DKM)for lithofacies identification using well logs.DKM includes a feature extractor and a classifier.The feature extractor consists of a series of KPCA models arranged according to residual network structure.A gradient-free optimization method is introduced to automatically optimize parameters and structure in DKM,which can avoid complex tuning of parameters in models.To test the validation of the proposed DKM for lithofacies identification,an open-sourced dataset with seven con-ventional logs(GR,CAL,AC,DEN,CNL,LLD,and LLS)and lithofacies labels from the Daniudi Gas Field in China is used.There are eight lithofacies,namely clastic rocks(pebbly,coarse,medium,and fine sand-stone,siltstone,mudstone),coal,and carbonate rocks.The comparisons between DKM and three commonly used kernel methods(KFD,SVM,MSVM)show that(1)DKM(85.7%)outperforms SVM(77%),KFD(79.5%),and MSVM(82.8%)in accuracy of lithofacies identification;(2)DKM is about twice faster than the multi-kernel method(MSVM)with good accuracy.The blind well test in Well D13 indicates that compared with the other three methods DKM improves about 24%in accuracy,35%in precision,41%in recall,and 40%in F1 score,respectively.In general,DKM is an effective method for complex lithofacies identification.This work also discussed the optimal structure and classifier for DKM.Experimental re-sults show that(m_(1),m_(2),O)is the optimal model structure and linear svM is the optimal classifier.(m_(1),m_(2),O)means there are m KPCAs,and then m2 residual units.A workflow to determine an optimal classifier in DKM for lithofacies identification is proposed,too.
基金Supportd by the China National Science and Technology Major Project(2016ZX05004-001)
文摘In recent years, natural gas exploration in the Sinian Dengying Formation and shale gas exploration in Doushantuo Formation have made major breakthroughs in the Sichuan Basin and its adjacent areas. However, the sedimentary background of the Doushantuo Formation hasn't been studied systematically. The lithofacies paleogeographic pattern, sedimentary environment, sedimentary evolution and distribution of source rocks during the depositional stage of Doushantuo Formation were systematically analyzed by using a large amount of outcrop data, and a small amount of drilling and seismic data.(1) The sedimentary sequence and stratigraphic distribution of the Sinian Doushantuo Formation in the middle-upper Yangtze region were controlled by paleouplifts and marginal sags. The Doushantuo Formation in the paleouplift region was overlayed with thin thickness, including shore facies, mixed continental shelf facies and atypical carbonate platform facies. The marginal sag had complete strata and large thickness, and developed deep water shelf facies and restricted basin facies.(2) The Doushantuo Formation is divided into four members from bottom to top, and the sedimentary sequence is a complete sedimentary cycle of transgression–high position–regression. The first member is atypical carbonate gentle slope deposit in the early stage of the transgression, the second member is shore-mixed shelf deposit in the extensive transgression period, and the third member is atypical restricted–open sea platform deposit of the high position of the transgression.(3) The second member has organic-rich black shale developed with stable distribution and large thickness, which is an important source rock interval and major shale gas interval. The third member is characterized by microbial carbonate rock and has good storage conditions which is conducive to the accumulation of natural gas, phosphate and other mineral resources, so it is a new area worthy of attention. The Qinling trough and western Hubei trough are favorable areas for exploration of natural gas(including shale gas) and mineral resources such as phosphate and manganese ore.
