Identification of reservoir types in deep carbonates based on mixedkernel machine learning using geophysical logging data

Identification of reservoir types in deep carbonates has always been a great challenge due to complex logging responses caused by the heterogeneous scale and distribution of storage spaces.Traditional cross-plot analysis and empirical formula methods for identifying reservoir types using geophysical logging data have high uncertainty and low efficiency,which cannot accurately reflect the nonlinear relationship between reservoir types and logging data.Recently,the kernel Fisher discriminant analysis(KFD),a kernel-based machine learning technique,attracts attention in many fields because of its strong nonlinear processing ability.However,the overall performance of KFD model may be limited as a single kernel function cannot simultaneously extrapolate and interpolate well,especially for highly complex data cases.To address this issue,in this study,a mixed kernel Fisher discriminant analysis(MKFD)model was established and applied to identify reservoir types of the deep Sinian carbonates in central Sichuan Basin,China.The MKFD model was trained and tested with 453 datasets from 7 coring wells,utilizing GR,CAL,DEN,AC,CNL and RT logs as input variables.The particle swarm optimization(PSO)was adopted for hyper-parameter optimization of MKFD model.To evaluate the model performance,prediction results of MKFD were compared with those of basic-kernel based KFD,RF and SVM models.Subsequently,the built MKFD model was applied in a blind well test,and a variable importance analysis was conducted.The comparison and blind test results demonstrated that MKFD outperformed traditional KFD,RF and SVM in the identification of reservoir types,which provided higher accuracy and stronger generalization.The MKFD can therefore be a reliable method for identifying reservoir types of deep carbonates.

A Transient-Pressure-Based Numerical Approach for Interlayer Identification in Sand Reservoirs

Almost all sandstone reservoirs contain interlayers. The identification and characterization of these interlayers iscritical for minimizing the uncertainty associated with oilfield development and improving oil and gas recovery.Identifying interlayers outside wells using identification methods based on logging data and machine learning isdifficult and seismic-based identification techniques are expensive. Herein, a numerical model based on seepageand well-testing theories is introduced to identify interlayers using transient pressure data. The proposed modelrelies on the open-source MATLAB Reservoir Simulation Toolbox. The effects of the interlayer thickness, position,and width on the pressure response are thoroughly investigated. A procedure for inverting interlayer parametersin the reservoir using the bottom-hole pressure is also proposed. This method uses only transient pressuredata during well testing and can effectively identify the interlayer distribution near the wellbore at an extremelylow cost. The reliability of the model is verified using effective oilfield examples.

Fracture identification of carbonate reservoirs by deep forest model:An example from the D oilfield in Zagros Basin

Identifying fractures along a well trajectory is of immense significance in determining the subsurface fracture network distribution.Typically,conventional logs exhibit responses in fracture zones,and almost all wells have such logs.However,detecting fractures through logging responses can be challenging since the log response intensity is weak and complex.To address this problem,we propose a deep learning model for fracture identification using deep forest,which is based on a cascade structure comprising multi-layer random forests.Deep forest can extract complex nonlinear features of fractures in conventional logs through ensemble learning and deep learning.The proposed approach is tested using a dataset from the Oligocene to Miocene tight carbonate reservoirs in D oilfield,Zagros Basin,Middle East,and eight logs are selected to construct the fracture identification model based on sensitivity analysis of logging curves against fractures.The log package includes the gamma-ray,caliper,density,compensated neutron,acoustic transit time,and shallow,deep,and flushed zone resistivity logs.Experiments have shown that the deep forest obtains high recall and accuracy(>92%).In a blind well test,results from the deep forest learning model have a good correlation with fracture observation from cores.Compared to the random forest method,a widely used ensemble learning method,the proposed deep forest model improves accuracy by approximately 4.6%.

A systematic machine learning method for reservoir identification and production prediction

Reservoir identification and production prediction are two of the most important tasks in petroleum exploration and development.Machine learning(ML)methods are used for petroleum-related studies,but have not been applied to reservoir identification and production prediction based on reservoir identification.Production forecasting studies are typically based on overall reservoir thickness and lack accuracy when reservoirs contain a water or dry layer without oil production.In this paper,a systematic ML method was developed using classification models for reservoir identification,and regression models for production prediction.The production models are based on the reservoir identification results.To realize the reservoir identification,seven optimized ML methods were used:four typical single ML methods and three ensemble ML methods.These methods classify the reservoir into five types of layers:water,dry and three levels of oil(I oil layer,II oil layer,III oil layer).The validation and test results of these seven optimized ML methods suggest the three ensemble methods perform better than the four single ML methods in reservoir identification.The XGBoost produced the model with the highest accuracy;up to 99%.The effective thickness of I and II oil layers determined during the reservoir identification was fed into the models for predicting production.Effective thickness considers the distribution of the water and the oil resulting in a more reasonable production prediction compared to predictions based on the overall reservoir thickness.To validate the superiority of the ML methods,reference models using overall reservoir thickness were built for comparison.The models based on effective thickness outperformed the reference models in every evaluation metric.The prediction accuracy of the ML models using effective thickness were 10%higher than that of reference model.Without the personal error or data distortion existing in traditional methods,this novel system realizes rapid analysis of data while reducing the time required to resolve reservoir classification and production prediction challenges.The ML models using the effective thickness obtained from reservoir identification were more accurate when predicting oil production compared to previous studies which use overall reservoir thickness.

Genesis, evolution and reservoir identification of a Neogene submarine channel in the southwestern Qiongdongnan Basin, South China Sea

A rarely reported middle-late Miocene-Pliocene channel(incised valley fill),the Huaguang Channel(HGC),has been found in the deep-water area of the southwestern Qiongdongnan Basin(QDNB).This channel is almost perpendicular to the orientation of another well-known,large,and nearly coeval submarine channel in this area.Based on the interpretation of high-resolution 3D seismic data,this study describes and analyzes the stratigraphy,tectonics,sedimentation,morphology,structure and evolution of HGC by means of well-seismic synthetic calibration,one-and two-dimensional forward modeling,attribute interpretation,tectonic interpretation,and gas detection.The HGC is located on the downthrown side of an earlier activated normal fault and grew northwestward along the fault strike.The channel is part of a slope that extends from the western Huaguang Sag to the eastern Beijiao Uplift.The HGC underwent four developmental stages:the(1)incubation(late Sanya Formation,20.4–15.5 Ma),(2)embryonic(Meishan Formation,15.5–10.5 Ma),(3)peak(Huangliu Formation,10.5–5.5 Ma)and(4)decline(Yinggehai Formation,5.5–1.9 Ma)stages.The channel sandstones have a provenance from the southern Yongle Uplift and filled the channel via multistage vertical amalgamation and lateral migration.The channel extended 42.5 km in an approximately straight pattern in the peak stage.At 10.5 Ma,sea level fell relative to its lowest level,and three oblique progradation turbidite sand bodies filled the channel from south to north.A channel sandstone isopach map demonstrated a narrow distribution in the early stages and a fan-shaped distribution in the late stage.The formation and evolution of the HGC were controlled mainly by background tectonics,fault strike,relative sea level change,and mass supply from the Yongle Uplift.The HGC sandstone reservoir is near the Huaguangjiao Sag,where hydrocarbons were generated.Channel-bounding faults and underlying faults link the source rock with the reservoir.A regionally extensive mudstone caprock overlies the channel sandstone.Two traps likely containing gas were recognized in a structural high upstream of the channel from seismic attenuation anomalies.The HGC will likely become an important oil and gas accumulation setting in the QDNB deep-water area.

Fine-grained gravity flow sedimentation and its influence on development of shale oil sweet sections in lacustrine basins in China

The geological conditions and processes of fine-grained gravity flow sedimentation in continental lacustrine basins in China are analyzed to construct the model of fine-grained gravity flow sedimentation in lacustrine basin,reveal the development laws of fine-grained deposits and source-reservoir,and identify the sweet sections of shale oil.The results show that fine-grained gravity flow is one of the important sedimentary processes in deep lake environment,and it can transport fine-grained clasts and organic matter in shallow water to deep lake,forming sweet sections and high-quality source rocks of shale oil.Fine-grained gravity flow deposits in deep waters of lacustrine basins in China are mainly fine-grained high-density flow,fine-grained turbidity flow(including surge-like turbidity flow and fine-grained hyperpycnal flow),fine-grained viscous flow(including fine-grained debris flow and mud flow),and fine-grained transitional flow deposits.The distribution of fine-grained gravity flow deposits in the warm and humid unbalanced lacustrine basins are controlled by lake-level fluctuation,flooding events,and lakebed paleogeomorphology.During the lake-level rise,fine-grained hyperpycnal flow caused by flooding formed fine-grained channel–levee–lobe system in the flat area of the deep lake.During the lake-level fall,the sublacustrine fan system represented by unconfined channel was developed in the flexural slope breaks and sedimentary slopes of depressed lacustrine basins,and in the steep slopes of faulted lacustrine basins;the sublacustrine fan system with confined or unconfined channel was developed on the gentle slopes and in axial direction of faulted lacustrine basins,with fine-grained gravity flow deposits possibly existing in the lower fan.Within the fourth-order sequences,transgression might lead to organic-rich shale and fine-grained hyperpycnal flow deposits,while regression might cause fine-grained high-density flow,surge-like turbidity flow,fine-grained debris flow,mud flow,and fine-grained transitional flow deposits.Since the Permian,in the shale strata of lacustrine basins in China,multiple transgression-regression cycles of fourth-order sequences have formed multiple source-reservoir assemblages.Diverse fine-grained gravity flow sedimentation processes have created sweet sections of thin siltstone consisting of fine-grained high-density flow,fine-grained hyperpycnal flow and surge-like turbidity flow deposits,sweet sections with interbeds of mudstone and siltstone formed by fine-grained transitional flows,and sweet sections of shale containing silty and muddy clasts and with horizontal bedding formed by fine-grained debris flow and mud flow.The model of fine-grained gravity flow sedimentation in lacustrine basin is significant for the scientific evaluation of sweet shale oil reservoir and organic-rich source rock.

Characteristics of Shale Reservoir and Sweet Spot Identification of the Lower Cambrian Niutitang Formation in Northwestern Hunan Province, China

The accumulation and productivity of shale gas are mainly controlled by the characteristics of shale reservoirs;study of these characteristics forms the basis for the shale gas exploitation of the Lower Cambrian Niutitang Formation(Fm),Southern China.In this study,core observation and lithology study were conducted along with X-ray diffraction(XRD)and electronic scanning microscopy(SEM)examinations and liquid nitrogen(N2)adsorption/desorption and CH4 isothermal adsorption experiments for several exploration wells in northwestern Hunan Province,China.The results show that one or two intervals with high-quality source rocks(TOC>2 wt%)were deposited in the deep-shelf facies.The source rocks,which were mainly composed of carbonaceous shales and siliceous shales,had high quartz contents(>40 wt%)and low clay mineral(<30 wt%,mainly illites)and carbonate mineral(<20 wt%)contents.The SEM observations and liquid nitrogen(N2)adsorption/desorption experiments showed that the shale is tight,and nanoscale pores and microscale fractures are well developed.BJH volume(VBJH)of shale ranged from 2.144×10^-3 to 20.07×10^-3 cm^3/g,with an average of 11.752×10^-3 cm3/g.Pores mainly consisted of opened and interconnected mesopores(2–50 nm in diameter)or macropores(>50 nm in diameter).The shale reservoir has strong adsorption capacity for CH4.The Langmuir volume(VL)varied from 1.63 to 7.39 cm^3/g,with an average of 3.95 cm^3/g.The characteristics of shale reservoir are controlled by several factors:(1)A deep muddy continental shelf is the most favorable environment for the development of shale reservoirs,which is controlled by the development of basic materials.(2)The storage capacity of the shale reservoir is positively related to the TOC contents and plastic minerals and negatively related to cement minerals.(3)High maturity or overmaturity leads to the growth of organic pores and microfractures,thereby improving the reservoir storage capacity.It can be deduced that the high percentage of residual gas in Niutitang Fm results from the strong reservoir storage capacity of adsorbed gas.Two layers of sweet spots with strong storage capacity of free gas,and they are characterized by the relatively high TOC contents ranging from 4 wt%to 8 wt%.

Pore Characteristics of the Fine-Grained Tight Reservoirs in the Yabulai Basin, Northwestern China

This work investigated the pore structure characteristics and reservoir features of the finegrained tight reservoirs in the lower member of the Xinhe Formation(J2x1) in the Xiaohu subsag,Yabulai Basin based on core samples through various techniques. Interbedded silt/fine sandstones and mudstones are developed in the study area. Scanning electron microscopy(SEM) images were used to delineate different types of pores, including primary intergranular pores, secondary intergranular and intragranular pores, organic pores and fractures. The pore types were distinguished by pore size, pore area, location and formation process. The pore radii of the fine-grained rocks range from 1 nm to 1.55μm, mainly concentrated between 5 and 300 nm by low pressure N2adsorption and MICP analyses. The pore structure parameters of pore throat size and pore throat sorting coefficient are both positively correlated with porosity, while pore throat sorting coefficient has a negative correlation with permeability. The pore structures of the studied samples are much related to the mineral type and content and grain size, followed by TOC content. In these rocks with relatively low TOC and low maturity, the rigid minerals protect pores with pressure shadow from collapse, and dissolution-related pores contribute a lot to inorganic porosity. In contrast, these rocks with abundant TOC contain a large number of organic pores. The permeability of the fine-grained tight reservoir is mainly dominated by larger pore throats, while a large number of small pores(mostly <0.1 μm) contribute considerably to porosity. These results have deepened our understanding of the interbedded fine-grained tight reservoirs and can be applicable to fine-grained reservoirs in a similar setting.

Comprehensive security risk factor identification for small reservoirs with heterogeneous data based on grey relational analysis model

Identification of security risk factors for small reservoirs is the basis for implementation of early warning systems.The manner of identification of the factors for small reservoirs is of practical significance when data are incomplete.The existing grey relational models have some disadvantages in measuring the correlation between categorical data sequences.To this end,this paper introduces a new grey relational model to analyze heterogeneous data.In this study,a set of security risk factors for small reservoirs was first constructed based on theoretical analysis,and heterogeneous data of these factors were recorded as sequences.The sequences were regarded as random variables,and the information entropy and conditional entropy between sequences were measured to analyze the relational degree between risk factors.Then,a new grey relational analysis model for heterogeneous data was constructed,and a comprehensive security risk factor identification method was developed.A case study of small reservoirs in Guangxi Zhuang Autonomous Region in China shows that the model constructed in this study is applicable to security risk factor identification for small reservoirs with heterogeneous and sparse data.

Prediction of multiscale laminae structure and reservoir quality in fine-grained sedimentary rocks:The Permian Lucaogou Formation in Jimusar Sag,Junggar Basin

Fine-grained sedimentary rocks have become a research focus as important reservoirs and source rocks for tight and shale oil and gas.Laminae development determines the accumulation and production of tight and shale oil and gas in fine-grained rocks.However,due to the resolution limit of conventional logs,it is challenging to recognize the features of centimeter-scale laminae.To close this gap,complementary studies,including core observation,thin section,X-ray diffraction(XRD),conventional log analysis,and slabs of image logs,were conducted to unravel the centimeter-scale laminae.The laminae recognition models were built using well logs.The fine-grained rocks can be divided into laminated rocks(lamina thickness of<0.01 m),layered rocks(0.01-0.1 m),and massive rocks(no layer or layer spacing of>0.1 m)according to the laminae scale from core observations.According to the mineral superposition assemblages from thin-section observations,the laminated rocks can be further divided into binary,ternary,and multiple structures.The typical mineral components,slabs,and T2spectrum distributions of various lamina types are unraveled.The core can identify the centimeter-millimeter-scale laminae,and the thin section can identify the millimeter-micrometer-scale laminae.Furthermore,they can detect mineral types and their superposition sequence.Conventional logs can identify the meter-scale layers,whereas image logs and related slabs can identify the laminae variations at millimeter-centimeter scales.Therefore,the slab of image logs combined with thin sections can identify laminae assemblage characteristics,including the thickness and vertical assemblage.The identification and classification of lamina structure of various scales on a single well can be predicted using conventional logs,image logs,and slabs combined with thin sections.The layered rocks have better reservoir quality and oil-bearing potential than the massive and laminated rocks.The laminated rocks’binary lamina is better than the ternary and multiple layers due to the high content of felsic minerals.The abovementioned results build the prediction model for multiscale laminae structure using well logs,helping sweet spots prediction in the Permian Lucaogou Formation in the Jimusar Sag and fine-grained sedimentary rocks worldwide.

Identification of the Quaternary low gas-saturation reservoirs in the Sanhu area of the Qaidam Basin,China

Low gas-saturation reservoirs are gas bearing intervals whose gas saturation is less than 47%. They are common in the Quaternary of the Sanhu area in the Qaidam Basin.Due to the complex genesis mechanisms and special geological characteristics,the logging curves of low gas-saturation reservoirs are characterized by ambiguity and diversity,namely without significant log response characteristics. Therefore,it is particularly difficult to identify the low gas-saturation reservoirs in the study area.In addition,the traditional methods such as using the relations among lithology,electrical property,physical property and gas bearing property,as well as their threshold values,can not effectively identify low gas-saturation reservoirs.To solve this problem,we adopt the decision tree,support vector machine and rough set methods to establish a predictive model of low gas-saturation reservoirs,which is capable of classifying a mass of multi-dimensional and fuzzy data.According to the transparency of learning processes and the understandability of learning results,the predictive model was also revised by absorbing the actual reservoir characteristics.Practical applications indicate that the predictive model is effective in identifying low gas-saturation reservoirs in the study area.

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

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

A new method for reservoir fluid identification

The wavelet transform （WT） method has been employed to decompose an original geophysical signal into a series of components containing different information about reservoir features such as pore fluids, lithology, and pore structure. We have developed a new method based on WT energy spectra analysis, by which the signal component reflecting the reservoir fluid property is extracted. We have successfully processed real log data from an oil field in central China using this method. The results of the reservoir fluid identification agree with the results of well tests.

Log identification method in complex sand reservoirs

In order to obtain effective parameters for complex sand reservoirs,a log evaluation method for relevant reservoir parameters is established based on an analysis in the gas-bearing sandstone with high porosity and low permeability,low porosity and permeability and on various characteristics of log responses to reservoir lithologies and physical properties in the Neopleozoic sand reservoir of the Ordos basin.This log evaluation method covers the Cook method that is used to evaluate the porosity and oiliness in high porosity and low permeability reservoirs and another method in which the mineral content,derived from geochemical logs,is used to identify formation lithologies.Some areas have high calcium and low silt content,not uniformly distributed,the results of which show up in the complex formation lithologies and conventional log responses with great deviation.The reliability of the method is verified by comparison with conventional log data and core analyses.The calculation results coincide with the core analytical data and gas tests,which indicate that this log evaluation method is available,provides novel ideas for study of similar complex reservoir lithologies and has some reference value.

Large-scale toppling slope under water level fluctuation of reservoir:A case of Yunnan Province,China

Landslides induced by reservoir inundation are common in Southwest China,negatively influencing hydropower stations.TheWunonglong hydropower station dam was constructed in the upper reaches of the Lancang River,accordingly causing the water level at the Lajinshengu slope to increase by 30 m.A tension crack with a visible depth of 8 m was observed in the upper sector of the Lajinshengu slope after reservoir impoundment for 170 d.In the following days,numerous cracks appeared on the surface of the slope,and the maximum displacement of the slope reached 3.22 m.Then,a large-scale active deformation body within the Lajinshengu slope formed with an area of 2.62×10^(5)m^(2)and a volume of 1.65×10^(7)m^(3).Detailed field investigations,on-site monitoring,and centrifugal model tests were carried out to analyze the surface features,deformation characteristics,and failure mechanism of the Lajinshengu slope.The results show that the slope is an ancient landslide,divided into two parts(i.e.zone A and zone B)by the gully.Zone B is a traction landslide caused by the displacement of zone A.The longterm inundation weakens the soft rock at the slope foot,intensifying the toppling of bedrock and consequently triggering the sliding of the overburden in zone A.The failure mode of the Lajinshengu slope is a typical case of toppling-sliding failure,and the underlying rock toppling drives the overlying sliding.In addition,early identification methods for toppling deformation covered by overburdened soil were proposed based on monitoring data and deformation signs.

Application of fluid modulus inversion to complex lithology reservoirs in deep-water areas

It has been a challenge to distinguish between seismic anomalies caused by complex lithology and hydrocarbon reservoirs using conventional fluid identification techniques,leading to difficulties in accurately predicting hydrocarbon-bearing properties and determining oil-water contacts in reservoirs.In this study,we built a petrophysical model tailored to the deep-water area of the Baiyun Sag in the eastern South China Sea based on seismic data and explored the feasibility of the tri-parameter direct inversion method in the fluid identification of complex lithology reservoirs,offering a more precise alternative to conventional techniques.Our research found that the fluid modulus can successfully eliminate seismic amplitude anomalies caused by lithological variations.Furthermore,the seismic databased direct inversion for fluid modulus can remove the cumulative errors caused by indirect inversion and the influence of porosity.We discovered that traditional methods using seismic amplitude anomalies were ineffective in detecting fluids,determining gas-water contacts,or delineating high-quality reservoirs.However,the fluid factor Kf,derived from solid-liquid decoupling,proved to be sensitive to the identification of hydrocarbon-bearing properties,distinguishing between high-quality and poor-quality gas zones.Our findings confirm the value of the fluid modulus in fluid identification and demonstrate that the tri-parameter direct inversion method can significantly enhance hydrocarbon exploration in deep-water areas,reducing associated risks.

The Fluid Identification Forward Modeling Study of Carbonate Cave Reservoir

The fluid identification of carbonate reservoir is a key factor to hydrocarbon exploration and reservoir development. In order to simulate the seismic response characteristics of the cave in the carbonate reservoir, three sets of models were designed, including the caves varied in width, the caves filled with different solids, and the oil-gas-water model. The numerical simulation technique was used to carry out the forward modeling and the AVO (Amplitude varies with offset) response characteristics of the three groups of models were analyzed. The results show that the AVO characteristics can be observed when the cave reaches a certain extent in the horizontal direction. When the surrounding rock is constant, the absolute value of the intercept of the AVO curve increases with the Vp/Vs decrease. The AVO technology can effectively identify the gas cave. The effect is not obvious to water or oil cave.

An Integrated Method of Data Mining and Flow Unit Identification for Typical Low Permeability Reservoir Prediction

With the development of oilfield exploration and mining, the research on continental oil and gas reservoirs has been gradually refined, and the exploration target of offshore reservoir has also entered the hot studystage of small sand bodies, small fault blocks, complex structures, low permeability and various heterogeneous geological bodies. Thus, the marine oil and gas development will inevitably enter thecomplicated reservoir stage;meanwhile the corresponding assessment technologies, engineering measures andexploration method should be designed delicately. Studying on hydraulic flow unit of low permeability reservoir of offshore oilfield has practical significance for connectivity degree and remaining oil distribution. An integrated method which contains the data mining and flow unit identification part was used on the flow unit prediction of low permeability reservoir;the predicted results?were compared with mature commercial system results for verifying its application. This strategy is successfully applied to increase the accuracy by choosing the outstanding prediction result. Excellent computing system could provide more accurate geological information for reservoir characterization.

Identification of Interlayer in Strong Bottom Water Reservoir and Its Influence on Development Effect

X oilfield is located in Bohai Sea area, in which G oil formation is a typical drape anticline structure, which is composed of multiple sets of thick sandy conglomerate and multiple sets of argillaceous intercalation. From the perspective of development effect, muddy interlayer has a great impact on the oilfield. In this paper, through core identification and well logging identification, the electrical discrimination standard is summarized to identify the interlayer. Through statistics and analysis of the production performance of actual wells, the influence of muddy interlayer on the development performance of oil wells is summarized. This study provides guidance for the development of strong bottom water reservoirs with interlayer.

Fluid identification based on frequency-dependent AVO attribute inversion in multi-scale fracture media

A key problem in seismic inversion is the identification of the reservoir fluids. Elastic parameters, such as seismic wave velocity and formation density, do not have sufficient sensitivity, thus, the conventional amplitude-versus-offset（AVO） method is not applicable. The frequency-dependent AVO method considers the dependency of the seismic amplitude to frequency and uses this dependency to obtain information regarding the fluids in the reservoir fractures. We propose an improved Bayesian inversion method based on the parameterization of the Chapman model. The proposed method is based on 1） inelastic attribute inversion by the FDAVO method and 2） Bayesian statistics for fluid identification. First, we invert the inelastic fracture parameters by formulating an error function, which is used to match observations and model data. Second, we identify fluid types by using a Markov random field a priori model considering data from various sources, such as prestack inversion and well logs. We consider the inelastic parameters to take advantage of the viscosity differences among the different fluids possible. Finally, we use the maximum posteriori probability for obtaining the best lithology/fluid identification results.