Hydrocarbon Potential of the Triassic-Jurassic Sediments in Southeast Sulawesi, Indonesia, Based on Lithofacies and Geochemical Analysis

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.

A log-based method for fine-scale evaluation of lithofacies and its applications to the Gulong shale in the Songliao Basin,Northeast China

The Gulong shale demonstrates high clay content and pronounced thin laminations,with limited vertical variability in log curves,complicating lithofacies classification.To comprehend the distribution and compositional features of lithofacies in the Gulong shale for optimal sweet spot selection and reservoir stimulation,this study introduced a lithofacies classification scheme and a log-based lithofacies evaluation method.Specifically,theΔlgR method was utilized for accurately determining the total organic carbon(TOC)content;a multi-mineral model based on element-to-mineral content conversion coefficients was developed to enhance mineral composition prediction accuracy,and the microresistivity curve variations derived from formation micro-image(FMI)log were used to compute lamination density,offering insights into sedimentary structures.Using this method,integrating TOC content,sedimentary structures,and mineral compositions,the Qingshankou Formation is classified into four lithofacies and 12 sublithofacies,displaying 90.6%accuracy compared to core description outcomes.The classification results reveal that the northern portion of the study area exhibits more prevalent fissile felsic shales,siltstone interlayers,shell limestones,and dolomites.Vertically,the upper section primarily exhibits organic-rich felsic shale and siltstone interlayers,the middle part is characterized by moderate organic quartz-feldspathic shale and siltstone/carbonate interlayers,and the lower section predominantly features organic-rich fissile felsic/clayey felsic shales.Analyzing various sublithofacies in relation to seven petrophysical parameters,oil test production,and fracturing operation conditions indicates that the organic-rich felsic shales in the upper section and the organic-rich/clayey felsic shales in the lower section possess superior physical properties and oil content,contributing to smoother fracturing operation and enhanced production,thus emerging as dominant sublithofacies.Conversely,thin interlayers such as siltstones and limestones,while producing oil,demonstrate higher brittleness and pose great fracturing operation challenges.The methodology and insights in this study will provide a valuable guide for sweet spot identification and horizontal well-based exploitation of the Gulong shale.

A deep kernel method for lithofacies identification using conventional well logs

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.

How to improve machine learning models for lithofacies identification by practical and novel ensemble strategy and principles

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.

High-resolution coupling of stratigraphic'sweet-spot'lithofacies and petrophysical properties:A multiscale study of Ordovician Goldwyer Formation,Western Australia

The identification of stratigraphic'sweet-spot'interval is significant in oil and gas formation evaluation.However,formation evaluation in macroscopic-scale merely provides low resolution and limited infor-mation,thus may lead to uncertainties in resource estimation.To accurately identify the'sweet-spot'intervals amongst heterogeneous lithofacies,we conducted a very high-resolution and quantitative analysis from in-situ macroscopic scale to laboratory microscopic scale on the Goldwyer formation of Canning Basin,Western Australia.The comprehensive advanced well logging and slim-compact micro imager(SCMI)technologies were synthetically applied in couple with the laboratory nanoscaled ex-periments.The results unveiled an extraordinarily large lithofacies heterogeneity between different rock intervals,with distinguished features shown in Goldwyer Ⅰ,Ⅱ,and Ⅲ members.The most favorable lithofacies is recognized as the laminated argillaceous thermally-matured organic matter(OM)-rich mudstone,which is widely developed in Goldwyer Ⅲ as the major attributor to'sweet-spot'intervals.Goldwyer Ⅱ is exclusively characterized by thick mudstone intervals(94.4%),interbedded with thin calcareous mudstones(5.5%),corresponding to a depositional environment of low-energy distal section of the outer ramp settings.Microscopically,the most favorable lithofacies in'sweet-spot'intervals develop numerous OM-/mineral nanopores for hydrocarbon storage.Illite-rich lithofacies develops abundant inter-particle pores from 2 to 17 nm that mainly contribute to pore volume for free gas storage capacity.OM-rich lithofacies with higher maturity have OM-pores with good connectivity,bearing large specific surface area that is beneficial for adsorbed gas capacity.

Depositional Environment and Lithofacies Analyses of Eocene Lacustrine Shale in the Bohai Bay Basin:Insights from Mineralogy and Elemental Geochemistry

The effect of various depositional parameters including paleoclimate,paleosalinity and provenance,on the depositional mechanism of lacustrine shale is very important in reconstructing the depositional environment.The classification of shale lithofacies and the interpretation of shale depositional environment are key features used in shale oil and gas exploration and development activity.The lower 3rd member of the Eocene Shahejie Formation(Es_(3)^(x)shale)was selected for this study,as one of the main prospective intervals for shale oil exploration and development in the intracratonic Bohai Bay Basin.Mineralogically,it is composed of quartz(avg.9.6%),calcite(avg.58.5%),dolomite(avg.7%),pyrite(avg.3.3%)and clay minerals(avg.20%).An advanced methodology(thin-section petrography,total organic carbon and total organic sulfur contents analysis,X-ray diffraction(XRD),X-ray fluorescence(XRF),field-emission scanning electron microscopy(FE-SEM))was adopted to establish shale lithofacies and to interpret the depositional environment in the lacustrine basin.Six different types of lithofacies were recognized,based on mineral composition,total organic carbon(TOC)content and sedimentary structures.Various inorganic geochemical proxies(Rb/Sr,Ca/(Ca+Fe),Ti/Al,Al/Ca,Al/Ti,Zr/Rb)have been used to interpret and screen variations in depositional environmental parameters during the deposition of the Es_(3)^(x)shale.The experimental results indicate that the environment during the deposition of the Es_(3)^(x)shale was warm and humid with heightened salinities,moderate to limited detrital input,higher paleohydrodynamic settings and strong oxygen deficient(reducing)conditions.A comprehensive depositional model of the lacustrine shale was developed.The interpretations deduced from this research work are expected to not only expand the knowledge of shale lithofacies classification for lacustrine fine-grained rocks,but can also offer a theoretical foundation for lacustrine shale oil exploration and development.

Favorable lithofacies and pore characteristics of the Permian Longtan Formation shale in the southern Sichuan Basin

The marine–continental transitional shale (MCTS) reservoirs of the Longtan Formation (LTF) are widely distributed in the Sichuan Basin. However, the LTF shale exhibits considerable variations in mineral composition and pore characteristics, which makes identifying the 'sweet spot'a challenging task. To address this issue, 10 samples from four typical shale gas wells in the LTF in the southern Sichuan Basin were selected and analyzed for total organic carbon (TOC) content, whole-rock composition using X-ray diffraction (XRD), low-pressure gas adsorption, and high-pressure mercury intrusion. The lithofacies distribution and pore structure of the MCTS were studied to determine the pore structural characteristics and the primary factors influencing pore formation in different types of shale lithofacies in the LTF. The lithofacies of the LTF shale in the study area can be classified into three categories: siliceous clay shale, clay shale and mixed shale. Mineral content has a significant impact on the pore characteristics, while TOC content has a minor effect on the pore volume and specific surface area of micropores and mesopores. It can be inferred that the mesopores in the MCTS are mainly related to clay mineral pores, and mineral dissolution and TOC content are not the primary factors contributing to pore formation.

The Stratigraphy-Lithofacies-Paleogeography and Geothermal Storage Recharge Analysis of the Dongying Formation in Tanggu of Tianjin

On the basis of the stratigraphy introducing stratum general situation of the Dongying Formation of Paleogene in Tanggu of Tianjin,Stratigraphy-lithofacies -paleogeography of the Dongying Formation are analyzed.The Dongying Formation appears in proper order deposition array of delta plain and delta leading edge from dry land to lake basin in this area.According to a synthetic results

Lithofacies paleogeography mapping and reservoir prediction in tight sandstone strata: A case study from central Sichuan Basin, China

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.

Lower Es3 in Zhanhua Sag, Jiyang Depression： a case study for lithofacies classification in lacustrine mud shale

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.

Lithofacies Characters and Significance of the Submarine Fan of the Liufengguan Group in Qinling

Field investigation and laboratory research on flysch of the Liufengguan Group in Qinling indicate the following： （1） Sandstone of the Liufengguan Group is categorized as feldspathic lithic graywacke with a minor amount of lithic graywacke in the QFR triangular diagram. Grain size〈0.3 mm. Bedding plane structures such as groove casts and suspected flute casts can be found at the bottom of the sandstone. It is inferred that currents may have come from the southeast during deposition. Bedding structures such as ripple marks, graded bedding, parallel bedding, small-scale cross bedding, climbing bedding, suspected convolute bedding, microlamination and sliding structures have also been observed, which are of indicative significance. It is thought that the Liufengguan Group has the sedimentary characteristics of bedding, bedding plane structures and lithologicai assemblages of deep-sea low-density turbidity current deposits. The vertical succession of the Bouma sequence in the inner fan subfacies zone is generally incomplete： the assemblage of Ta and Tabc is commonly seen; the succession of the middle fan subfacies zone is relatively complete; and divisions Te and Tb are common in the outer fan subfacies zone. （2） The flysh of the Liufengguan Group is a sequence of deep-sea argillaceous-arenaceous submarine fan deposits, in which the authors recognize the inner, middle and outer fan subfacies and also nine types of lithofacies： normal graded sandstone （A1）, medium- to thick-bedded, fine-grained sandstone （A2）, medium- to thick-bedded and massive siltstone （A3）, thin-bedded, fine-grained sandstone and mudstone （B1）, irregular interbeds of thinbedded, fine-grained sandstone and siltstone （B2）, thin-bedded, fine-grained sandstone （C1）, very thin-bedded, fine-grained sandstone （D1）, olistostromes （El） anddeep-sea mudstone （F）. The inner fan consists of four microfacies： natural levee （A1）, water channel （A2, A3） and olistostrome （El）; in the middle fan there also occur four microfacies, i.e., branch channel （B1）, branch channel （B2）, interdistributary bay （D1） and olistostrome. The outer fan is made up of the branch channel （C1） and sheet sand （D1） microfacies, which alternate vertically with sediments of deep-sea plain subfacies （F）. There occur fining- and thinning-upward channel deposits in the outer-fan subfacies zone of the submarine fan of the Liufengguan Group observed in this study. The quartz content of the graywacke of the deposits is all higher than 40% and may reach as high as 60%. Therefore, on the basis of the aforementioned features, this flysh should be formed in a passive continental-margin tectonic environment.

Lithofacies identi cation using support vector machine based on local deep multi-kernel learning

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.

Multi-resolution graph-based clustering analysis for lithofacies identifi cation from well log data: Case study of intraplatform bank gas fi elds, Amu Darya Basin

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.

Lithofacies paleogeography and exploration significance of Sinian Doushantuo depositional stage in the middle-upper Yangtze region, Sichuan Basin, SW China

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.

Aptian-Late Cenomanian Fluvio-Lacustrine Lithofacies and Palynomorphs from Mamfe Basin, Southwest Cameroon, West Africa

The sedimentary sequences in the Mamfe Basin are generally thought of as continental (fluvio-lacustrine) in origin. But the wide spread occurrence of salt springs and salt accumulations and in places gypsum, often puts to question the exclusive continentality throughout this basin’s history. The sequences studied portray a wide range of complex lithologies and lithofacies relationships and include basal and intra-formational conglomerates, sandstones, shales, carbonaceous, carbonate and halite facies respectively. All these are well exposed at several outcrop sections distributed all over the basin with some measuring up to and above 30 m in height.?Sedimentary structures include: parallel aligned imbricated prismatic pebbles, plannar to trough-cross stratifications, fining upward grain size distribution and thin frequently alternating sandstone-shale beds. Palynological data have been obtained for the first time from the shally and carbonate intervals and integrated with five lithofacies associations, permitting the determination of the ages and paleo environmental reconstruction. The palynomorphs are characterized by pteridophytic spores dominated by?Cicatricosisporites?sp.,Cyathidites?sp. and?Deltoidspora?sp., pollen grains include gymnosperms dominated by?Classopollis annulatus?and?Classopollis todosus?and angiosperms represented by?Retitricolpites?sp. and?Retimoncolpites?sp., fungal remains include some hyphae and?Fusiformisporites?sp. Only a few dinoflagellate cysts dominantly?Spiniferites?were encountered at Nfaitok outcrop, suggesting a proximal sea water influence. A fluvial, lacustrine-deltaic and an anoxic deep bottom lake setting sub-environments were reconstructed with no marine strata encountered. The brines are here suggested to have originated from sea water splay over a barrier and complimented by the chemistry of the drained surrounding country rocks. The palynomorph assemblage suggests an Aptian-Turonian age for the studied sequences and a chronostratigraphic sequence in which the Nfaitok and Satom Bridge units are both Late Cenomanian-Turonian (lateral equivalents of each other) are younger and overlie the Aptian-Early-Cenomanian Okoyong sandstone unit.

Lithofacies Phases Analysis of Cover Flux in Tundish for Rare Earth Treated Steel

Lithofacies structure of commercial cover flux and experimental slags were analyzed,and states of rare earth oxides in cover flux were studied. The results show that under the experiment conditions,the mineral structures of original slags are mainly the calcium feldspar,a lot of magnesium olivine and Fe-bearing silicate. Rare earth oxides in the cover flux exist mainly as rare earth silicates. When the content of rare earth oxides reaches a certain value,rare earth oxides precipitate in molten cover flux. It means that the solubility of rare earth oxides is limited. Rare earth oxides may be partly substituted for CaO to form rare earth-silicate which may increase with the basicity reducing.

Favorable lithofacies types and genesis of marine- continental transitional black shale: A case study of Permian Shanxi Formation in the eastern margin of Ordos Basin, NW China

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.

Evolution of lithofacies and paleogeography and hydrocarbon distribution worldwide(I)

By using a large amount of geological and geophysical data, the geological characteristics such as lithofacies and paleogeography of 4981 geological units at thirteen key geological periods or epoches since the Precambrian in the world have been figured out. The global lithofacies and paleogeography charts have been compiled by ArcGis mapping technology. Combined with the results of plate-paleogeography reconstruction, the lithofacies and paleogeography as well as the prototype basins of these global paleoplates have been restored with the Gplate software. Results show that there are 22 kinds of lithofacies combinations and 10 types of paleogeography units developed since Precambrian. These features of lithofacies and paleogeography as well as their evolution were mainly controlled by the divergent and convergent movements of those plates. Taking the results of the lithofacis and paleogeography at the present and paleoplate location during the seven key geological periods from the Precambrian to Paleozoic for example, during the Late Precambrian and Cambrian, the large-scale disintegration of the Rodinia supercontinent resulted in reduction of uplift denudation area and clastic terrestrial facies area, the expansion of coastal-shallow marine facies and shallow-water carbonate platform. In Devonian, uplift denudation area and clastic terrestrial facies area began to increase and littoral-shallow marine facies area and shallow-water carbonate platform shrank as a result of the formation of Larussia supercontinent. In the Permian, with the formation of the Pangea continent, the development of the global uplift denudation area and clastic terrestrial facies reached its peak, while the littoral and shallow marine facies were very limited in distribution. The lithofacies and paleogeography features and evolution patterns of different stages lay a solid foundation for analyzing the formation conditions of geological elements, such as source rocks, reservoirs and cap rocks for oil and gas accumulation, and revealing the distribution regularity of oil and gas around the world.

Oil-gas reservoir lithofacies stochastic modeling based on one- to three-dimensional Markov chains

Stochastic modeling techniques have been widely applied to oil-gas reservoir lithofacies. Markov chain simulation~ however~ is still under development~ mainly because of the difficulties in reasonably defining conditional probabilities for multi-dimensional Markov chains and determining transition probabilities for horizontal strike and dip directions. The aim of this work is to solve these problems. Firstly~ the calculation formulae of conditional probabilities for multi-dimensional Markov chain models are proposed under the full independence and conditional independence assumptions. It is noted that multi-dimensional Markov models based on the conditional independence assumption are reasonable because these models avoid the small-class underestimation problem. Then~ the methods for determining transition probabilities are given. The vertical transition probabilities are obtained by computing the transition frequencies from drilling data~ while the horizontal transition probabilities are estimated by using well data and the elongation ratios according to Walther＇s law. Finally~ these models are used to simulate the reservoir lithofacies distribution of Tahe oilfield in China. The results show that the conditional independence method performs better than the full independence counterpart in maintaining the true percentage composition and reproducing lithofacies spatial features.

Shale Lithofacies and Sedimentary Environment of the Third Member,Shahejie Formation,Zhanhua Sag,Eastern China

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.