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The diagenetic facies of tight oil reservoirs reflect the diagenetic characteristics and micro-pore structure of reservoirs, determining the formation and distribution of sweet spot zones. By establishing the correlation between diagenetic facies and logging curves, we can effectively identify the vertical variation of diagenetic facies types and predict the spatial variation of reservoir quality. However, it is still challenging work to establish the correlation between logging and diagenetic facies, and there are some problems such as low accuracy, high time consumption and high cost. To this end, we propose a lithofacies identification method for tight oil reservoirs based on hybrid intelligence using the Fuyu oil layer of the Sanzhao depression in Songliao Basin as the target area. Firstly, the geological characteristics of the selected area were analyzed, the definition and classification scheme of diagenetic facies and the dominant diagenetic facies were discussed, and the logging response characteristics of various diagenetic facies were summarized. Secondly, based on the standardization of logging curves, the logging image data set of various diagenetic facies was built, and the imbalanced data set processing was performed. Thirdly, by integrating CNN (Convolutional Neural Networks) and ViT (Visual Transformer), the C-ViTM hybrid intelligent model was constructed to identify the diagenetic facies of tight oil reservoirs. Finally, the effectiveness of the method is demonstrated through experiments with different thicknesses, accuracy and single-well identification. The experimental results show that the C-ViTM method has the best identification effect at the sample thickness of 0.5 m, with Precision of above 86%, Recall of above 90% and F1 score of above 89%. The calculation result of the Jaccard index in the identification of a single well was 0.79, and the diagenetic facies of tight reservoirs can be identified efficiently and accurately. At the same time, it also provides a new idea for the identification of the diagenetic facies of old oilfields with only logging image data sets.

Recently, the great potential of seismic dispersion attributes in oil and gas exploration has attracted extensive attention. The frequency-dependent amplitude versus offset (FAVO) technology, with dispersion gradient as a hydrocarbon indicator, has developed rapidly. Based on the classical AVO theory, the technology works on the assumption that elastic parameters are frequency-dependent, and implements FAVO inversion using spectral decomposition methods, so that it can take dispersive effects into account and effectively overcome the limitations of the classical AVO. However, the factors that affect FAVO are complicated. To this end, we construct a unified equation for FAVO inversion by combining several Zoeppritz approximations. We study and compare two strategies respectively with (strategy 1) and without (strategy 2) velocity as inversion input data. Using theoretical models, we investigate the influence of various factors, such as the Zoeppritz approximation used, P- and S-wave velocity dispersion, inversion input data, the strong reflection caused by non-reservoir interfaces, and the noise level of the seismic data. Our results show that FAVO inversion based on different Zoeppritz approximations gives similar results. In addition, the inversion results of strategy 2 are generally equivalent to that of strategy 1, which means that strategy 2 can be used to obtain dispersion attributes even if the velocity is not available. We also found that the existence of non-reservoir strong reflection interface may cause significant false dispersion. Therefore, logging, geological, and other relevant data should be fully used to prevent this undesirable consequence. Both the P- and S-wave related dispersion obtained from FAVO can be used as good indicators of a hydrocarbon reservoir, but the P-wave dispersion is more reliable. In fact, due to the mutual coupling of P- and S-wave dispersion terms, the P-wave dispersion gradient inverted from PP reflection seismic data has a stronger hydrocarbon detection ability than the S-wave dispersion gradient. Moreover, there is little difference in using post-stack data or pre-stack angle gathers as inversion input when only the P-wave dispersion is desired. The real application examples further demonstrate that dispersion attributes can not only indicate the location of a hydrocarbon reservoir, but also, to a certain extent, reveal the physical properties of reservoirs. [ABSTRACT FROM AUTHOR]

Due to the complicated interference sources and low signal-to-noise ratio of seismic records, conventional seismic inversion methods are difficult to accurately identify shale gas reservoirs with a thickness of less than 10 m. This presents great challenges to shale gas exploration and development in China. Seismic phase-controlled nonlinear stochastic inversion (SPCNSI) is related to the heterogeneity of underground media. With the constraints of the stratigraphic sequence or seismic facies models, the minimum value between the seismic model and seismic record can be solved through iterative processes. Based on the solved acoustic velocity in formation, the constraints for SPCNSI can be formed with the matching relationship between target layers and different sequences in three-dimensional space. The prediction resolution of an unconventional reservoir can be effectively improved by combining logging, geological and seismic information. The method is suitable for predicting thin shale gas reservoirs in complex geological structures. In this study, SPCNSI was developed to predict the thin-layer marine shale gas reservoir in the southeast of Chongqing; the horizontal and vertical distribution characteristics were proven in the Longmaxi Formation and the Wufeng Formation; thin layers with a thickness of less than 7 m were also discovered. According to the results, three sets of potential development areas were determined by the inversion method, and the accuracy and reliability of the method were verified by logging and productivity testing. [ABSTRACT FROM AUTHOR]

The Permian coal seams in eastern Yunnan and western Guizhou are thin, numerous, and staggered with other thin coal seams. Depicting the fine characteristics of coal reservoirs is pivotal for the safe and efficient exploitation of coal and coalbed methane (CBM), and is important for transparent mining. To improve the inverse resolution and accuracy of predicting reservoir thickness, this study used the model-based acoustic impedance (AI) inversion method that utilizes seismic and logging data. This method changes seismic data, reflecting stratigraphic interfaces, into AI data, reflecting lithologic structures. Moreover, it avoids the relevant assumptions of wavelets and reflection coefficients. Compared with other inversion methods, model-based AI inversion strengthens the description of thin reservoir horizontal and vertical changes. The results showed that comprehensively using intermediate-frequency seismic information and high-low-frequency logging data greatly broadens the seismic data frequency band and improves the dominant frequency of the reflected wave. Furthermore, the AI profile resolution and the prediction accuracy of the physical parameters for the target geological body can be improved. A cross-validation comparing the inverted thickness and measured thickness of borehole cores was applied to achieve fine prediction (error of appropriately 0.02–0.4 m), providing a basis for CBM development. [ABSTRACT FROM AUTHOR]

Copyright of Petroleum Geology & Oilfield Development in Daqing is the property of Editorial Department of Petroleum Geology & Oilfield Development in Daqing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Time-frequency analysis is an important tool for seismic signal analysis and time-frequency resolution is the key to high-precision reservoir prediction. The time-frequency method of conventional S-transform is difficult to meet the requirements of high precision reservoir prediction at present. In this paper, an improved window-parameter optimization S-transform (IWPOST) method is proposed. In the IWPOST method, the scale parameters of the window function can be adaptively obtained according to the amplitude spectrum of the actual signal, and a new optimization adjustment parameter is introduced to further improve the window parameters. The comparison analysis of the synthetic signals shows that the IWPOST method has better time-frequency aggregation and can maintain high resolution at both high-frequency and low-frequency. Finally, the proposed method is used to extract the reservoir fluid mobility attribute from the real seismic data and the results indicate that the fluid mobility has a higher time resolution and exhibits better performance for reservoir delineation. The proposed method is conducive to delineate the reservoir range and provides reference for the exploration and development of the oil and gas reservoirs in this area.

A set of methods for predicting the favorable reservoir of deep shale gas based on machine learning is proposed through research of parameter correlation feature analysis principle, intelligent prediction method based on convolution neural network (CNN), and integrated fusion characterization method based on kernel principal component analysis (KPCA) nonlinear dimension reduction principle. (1) High-dimensional correlation characteristics of core and logging data are analyzed based on the Pearson correlation coefficient. (2) The nonlinear dimension reduction method of KPCA is used to characterize complex high-dimensional data to efficiently and accurately understand the core and logging response laws to favorable reservoirs. (3) CNN and logging data are used to train and verify the model similar to the underground reservoir. (4) CNN and seismic data are used to intelligently predict favorable reservoir parameters such as organic carbon content, gas content, brittleness and in-situ stress to effectively solve the problem of nonlinear and complex feature extraction in reservoir prediction. (5) KPCA is used to eliminate complex redundant information, mine big data characteristics of favorable reservoirs, and integrate and characterize various parameters to comprehensively evaluate reservoirs. This method has been used to predict the spatial distribution of favorable shale reservoirs in the Ordovician Wufeng Formation to the Silurian Longmaxi Formation of the Weirong shale gas field in the Sichuan Basin, SW China. The predicted results are highly consistent with the actual core, logging and productivity data, proving that this method can provide effective support for the exploration and development of deep shale gas.

With the deepening exploratory development, geological targets are gradually shifting from conventional reservoirs to unconventional ones. The sandstone reservoirs in the northeast margin of the Ordos Basin are stacked vertically and change quickly horizontally with thin thicknesses. Therefore, traditional inversion technology cannot satisfy the requirements of precise prediction of thin reservoirs. To solve this problem, this paper integrated geological, seismic and logging data based on the GR curve. A wave form simulation of the MG block in the eastern margin of Ordos Basin was conducted, and the distribution of sandstone reservoirs in the target bed was depicted. The results showed that the waveform indication simulation profiles had a high resolution both horizontally and vertically. They were also in line with the drilling well and strongly correlated with the seismic waveform, which could reflect the spatial variation in the reservoir and accord with the law of geological deposition in the study area. Therefore, as a more efficient inversion method, waveform indication simulation provided a strong support for the refined study of thin inter-bedded reservoirs and helped to maximize the development and utilization of gas resources.

To improve the prediction of thin reservoirs with special geophysical responses, a geostatistical inversion technique is proposed based on an in-depth analysis of the theory of geostatistical inversion. This technique is based on the Markov chain Monte Carlo algorithm, to which we added the contents of facies-constrained. The feasibility of the technique and the reliability of the prediction results are demonstrated by a prediction of the sand bodies in the braided river channel bars in the Xiazijie Oilfield in the Junggar Basin. Based on the MCMC algorithm, the results show that leveraging the lateral changes in the seismic waveforms as geologically relevant information to drive the construction of the variogram and the optimization of the statistical sampling can largely overcome the obstacle that prevents traditional geostatistical inversions from accurately delineating the sedimentary characteristics; thereby, the proposed algorithm truly achieves facies-constrained geostatistical inversion. The case study of the Xiazijie Oilfield showed the feasibility and reliability of this technology. The prediction accuracy of the FCMCMC algorithm-based geostatistical inversion is as high as 6 m for thin interbedded reservoirs, and the coincidence rate between the prediction results and the well log data is more than 85%, which confirms the reliability of the technique. The demonstrated performance of the proposed technique provides a preliminary reference for the prediction of the thin interbedded reservoirs formed in terrestrial sedimentary basins and characterized by small thicknesses and rapid lateral changes with special geophysical responses. [ABSTRACT FROM AUTHOR]

Copyright of Petroleum Geology & Oilfield Development in Daqing is the property of Editorial Department of Petroleum Geology & Oilfield Development in Daqing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Dissolution mechanism and favorable reservoir distribution prediction are the key problems restricting oil and gas exploration in deep-buried layers. In this paper, the Enping Formation and Zhuhai Formation in Baiyun Sag of South China Sea was taken as a target. Based on the thin section, scanning electron microscopy, X-ray diffraction, porosity/permeability measurement, and mercury injection, influencing factors of dissolution were examined, and a dissolution model was established. Further, high-quality reservoirs were predicted temporally and spatially. The results show that dissolved pores constituted the main space of the Paleogene sandstone reservoir. Dissolution primarily occurred in the coarse- and medium-grained sandstones in the subaerial and subaqueous distributary channels, while dissolution was limited in fine-grained sandstones and inequigranular sandstones. The main dissolved minerals were feldspar, tuffaceous matrix, and diagenetic cement. Kaolinization of feldspar and illitization of kaolinite are the main dissolution pathways, but they occur at various depths and temperatures with different geothermal gradients. Dissolution is controlled by four factors, in terms of depositional facies, source rock evolution, overpressure, and fault activities, which co-acted at the period of 23.8–13.8 Ma, and resulted into strong dissolution. Additionally, based on these factors, high-quality reservoirs of the Enping and Zhuhai formations are predicted in the northern slope, southwestern step zone, and Liuhua uplift in the Baiyun Sag. [ABSTRACT FROM AUTHOR]

Taking the SaErTU and Putaohua oil layers in the L29 well area of LHP oilfield in the northern Songliao Basin as the research object, In response to the difficulty of interference between thin interbedded sand and mudstone and strong reflection between strata, which have a significant impact on fine prediction of sand bodies, a stratigraphic model is established based on the geological characteristics of the target layer to eliminate the impact of stratigraphic reflection; Then add a thin layer of sand body to its interior, establish a thin interlayer model, and obtain a geological model that is more in line with the actual situation of the target layer. Using forward simulation, analyze the seismic response characteristics of sand bodies, extract 30 seismic attributes from 4 categories: amplitude statistics, composite seismic trace statistics, sequence and frequency spectrum statistics, and calculate the correlation between cumulative sandstone thickness and seismic attributes, and select a sensitive seismic attribute set;By combining sedimentary and drilling data, the seismic attribute with the highest sensitivity to the target layer is selected. The multi-level dimensionality reduction and gradual improvement of seismic attribute selection methods using “model forward modeling, attribute analysis, relevant optimization, and drilling implementation” can effectively improve the prediction accuracy of thin interbedded sand bodies and greatly reduce the risk of oilfield exploration and development.

Due to that the main reservoirs of the X gas field in the East China Sea were deeply buried and have large lateral changes, the conventional seismic data resulted in poor quality and low resolution, which couldn’t meet the increasingly refined geological requirements in exploration and development. Seismic data with wideband and wide azimuth was obtained by using the acquisition method of three ships and four sources with oblique cables, which held the characteristics of high resolution, high signal-to-noise ratio and high fidelity. By taking advantage of the superior information of wideband and wide azimuth seismic data which was high-resolutional and anisotropic, combined with the simultaneous prestack inversion, the inversion body of sensitive elastic parameters of channel sand bodies in different azimuths can be obtained, and superimpose multiple azimuth inversion bodies perpendicular to the direction of the river channel to carry out fine predictions of channel sand bodies. Compared the conventional seismic data, reservoir inversion based on wideband and wide azimuth seismic data improved the prediction accuracy of channel sand bodies, laying a foundation for the progressive exploration and development of the X gas field in the East China Sea.

Amplitude-versus-angle (AVA) inversion for pre-stack seismic data is a key technology in oil and gas reservoir prediction. Conventional AVA inversion contains two main stages. Stage one estimates the relative change rates of P-wave velocity, S-wave velocity and density, and stage two obtains the P-wave velocity, S-wave velocity and density based on their relative change rates through trace integration. An alternative way merges these two stages to estimate P-wave velocity, S-wave velocity and density directly. This way is less sensitive to noise in seismic data compared to conventional two-stage AVA inversion. However, the regularization for the direct AVA inversion is more complex. To regularize this merged inverse problem, the L0-norm-gradient of P-wave velocity, S-wave velocity and density was used. L0-norm-gradient regularization can provide inversion results with blocky features to make formation interfaces and geological edges precise. Then, L0-norm-gradient regularized AVA inversion was performed on the synthetic seismic traces. Next, a real seismic data line that contains three partial angle stack profiles was used to test the practice application. The inversion results from synthetic and real seismic data showed that L0-norm-gradient regularized AVA inversion is an effective way to estimate P-wave velocity, S-wave velocity and density. [ABSTRACT FROM AUTHOR]

In exploration geophysics, seismic impedance is a physical characteristic parameter of underground formations. It can mark rock characteristics and help stratigraphic analysis. Hence, seismic data inversion for impedance is a key technology in oil and gas reservoir prediction. To invert impedance from seismic data, one can perform reflectivity series inversion first. Then, under a simple exponential integration transformation, the inverted reflectivity series can give the final inverted impedance. The quality of the inverted reflectivity series directly affects the quality of impedance. Sparse-spike inversion is the most common method to obtain reflectivity series with high resolution. It adopts a sparse regularization to impose sparsity on the inverted reflectivity series. However, the high resolution of sparse-spike-like reflectivity series is obtained at the cost of sacrificing small reflectivity. This is the inherent problem of sparse regularization. In fact, the reflectivity series from the actual impedance well log is not strictly sparse. It contains not only the sparse major large reflectivity, but also small reflectivity between major reflectivity. That is to say, the large reflectivity is sparse, but the small reflectivity is dense. To combat this issue, we adopt elastic-net regularization to replace sparse regularization in seismic impedance inversion. The elastic net is a hybrid regularization that combines sparse regularization and dense regularization. The proposed inversion method was performed on a synthetic seismic trace, which is created from an actual well log. Then, a real seismic data profile was used to test the practice application. The inversion results showed that it provides an effective new alternative method to invert impedance. [ABSTRACT FROM AUTHOR]

In view of the great difficulty in classifying Carboniferous volcanic rock reservoirs in Kebai Fault Area 1 and the inconsistent classification standards, eight important parameters affecting the classification of volcanic rock reservoirs in this zone are analyzed according to the drilling, logging, testing and other data. The parameters such as lithology, lithofacies, matrix porosity, fracture porosity, permeability, reservoir space type, lithofacies thickness and volcanic mechanism facies zone are assigned according to the reservoir characteristics of the research area and related reservoir classification data are calculated. Combined with the reservoir classification results of each single well, the classification method and classification standard of Carboniferous volcanic rock reservoirs in the research area are obtained. The study results show that according to the classification indicators of volcanic rock reservoirs, the Carboniferous volcanic rock reservoirs in Area 1 can be divided into three types: Type I (0.6≤RCI<1.0), Type II (0.4≤RCI<0.6), Type (III 0.0≤RCI<0.4), among which Type I reservoirs are the best, Type II reservoirs are the better and Type III reservoirs are the worst. The research results are applied to the reservoir classification of 16 wells that are not involved in the formulation of the standard, and the accuracy rate reaches 93. 8%, indicating that the classification standard is suitable for the research area. The research results have important guiding significance for the classification and prediction of Carboniferous volcanic reservoirs in this zone. [ABSTRACT FROM AUTHOR]

The Carboniferous KT-Ⅱ shoal facies reservoirs in the southeastern part of the middle block of the eastern margin of the Pre-Caspian Basin are characterized by small thickness, rapid lateral change and strong heterogeneity. Guided by evaluation of shoal facies reservoirs, thin reservoirs were predicted by using seismic, logging and core data and with the sensitive attributes and geostatistical inversion methods. The KT-II shoal facies reservoirs in the study area were divided into the porous type, fracture-pore type and pore-vug-fracture type and are dominated by the porous type. The lower limit of porosity for the effective reservoirs was 4%. The shoal facies deposits were characterized by medium-weak amplitude and low-frequency seismic reflection. First, sensitive seismic attributes such as root mean square amplitude, instantaneous frequency, and effective bandwidth were selected, and the reservoir distribution was predicted qualitatively through clustering of optimized attributes. Then, high resolution geostatistical inversion and cloud transformation and geostatistical co-simulation were carried out based on the Markov chain-Monte Carlo algorithm (MCMC), and the effective thin reservoir distribution was described by integrating the reservoir evaluation result. The inversion result has the high longitudinal resolution and shows reasonably the lateral variation between wells, and it reflects the variation characteristics of carbonate reservoirs. The prediction results show that the shoal facies reservoirs are mainly distributed in the east side of the study area. The results provide guide for drilling of exploratory well X-7, and the high yield oil flow has been obtained. It is proposed that the favorable area for further exploration is targeted at the southeast of the study area. [ABSTRACT FROM AUTHOR]

The second member of Triassic Feixianguan Formation in Jiulongshan area of northwestern Sichuan Basin has made breakthroughs in natural gas exploration and has great exploration potential. Drilling data show that the second section of Feixianguan Formation has developed multiple sets of reservoirs, which are mainly concentrated in the upper sub-member of the second member of Feixianguan Formation. The overall heterogeneity is strong, the lithology and physical properties are complex and changeable, the reservoir thickness is thin, and drilling is less, so it is difficult to determine the spatial distribution characteristics of oolitic beach reservoir, which restricts the natural gas exploration process in this area. For the oolitic beach reservoir in the upper sub-member of the second member of Feixianguan Formation of Jiulongshan area, using downhole reservoir characteristics and 3D seismic data, the reflection characteristics of oolitic beach reservoir are analyzed, and then the seismic response model of oolitic beach reservoir is established by forward modeling. It is clear that the seismic response characteristics of the reservoir in this area are weak amplitude-continuous reflection; through the use of layer flattening impression method to restore the ancient landscape of the second section of Feixianguan Formation, combined with seismic facies analysis and seismic amplitude attribute characteristics, The distribution characteristics of favorable facies belt for oolitic beach development are determined. Based on the distribution of favorable facies belt, this paper uses the relative impedance-based tuning amplitude inversion and waveform difference inversion method to make a fine prediction of the spatial distribution of the reservoir, defines the spatial distribution characteristics of the reservoir, and predicts the thickness and fracture development of oolitic beach reservoir. The prediction results of the reservoir thickness are in good agreement with the actual drilling results, which confirms the application of this prediction method in this area. According to the fracture development, wave impedance value less than 1.6×106 g/(cm2·s), reservoir thickness, and other indicators, the favorable development area of oolitic beach reservoir is further delineated, which provides a favorable basis for the next exploration and development.

Fluid discrimination is challenging for reservoir prediction, especially for tight sandstones with special petrophysical properties. In this paper, we first review the effective medium models that are widely used in seismic exploration and a variety of inversion methods and reservoir prediction strategies in reservoir prediction. Rock physics modeling takes an important role in reservoir prediction by linking petrophysical properties and elastic parameters. We also review the theoretical implications for different rock physics models that are based on the inclusion-based method, focusing specifically on the modeling workflow for conventional sand-shale reservoirs and two models for tight sandstones. The applicability of the conventional fluid substitution equations is analyzed in detail. Then, a new inclusion-based rock physics model for tight sandstones is proposed by considering the fluid pressure ratio between cracks and stiff pores. The proposed model helps to highlight the difference between different pores and present reasonable fluid information. In the application, a detailed prediction process for fluid discrimination is given, in which the Bayes posterior prediction framework is adopted to provide the maximum posterior probability solution and its posterior probability. Field data applications demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

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Copyright of Petroleum Geology & Oilfield Development in Daqing is the property of Editorial Department of Petroleum Geology & Oilfield Development in Daqing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Special Oil & Gas Reservoirs is the property of Special Oil & Gas Reservoirs Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

X gas field is located in the central inversion structural belt of Xihu Sag, East China Sea Basin.The main target layer H4 is in shallow water delta sedimentary environment.The dominant frequency of seismic data of the gas field is low (25 Hz), while the buried depth of H4 layer is large (3 300-3 400 m).The reservoir has low porosity and low permeability, and the consistency of sand body thickness predicted by conventional seismic inversion is low.Aiming at the characteristics of full coverage of 3D seismic data and less drilling in X gas field, this paper makes up for the lack of sample points in SVR algorithm through forward and inverse modeling under the guidance of geological model and setting up virtual wells.Then, by extracting seismic attributes and optimizing sensitive attributes characterizing sand body thickness, SVR algorithm is used for multi-attribute fusion to complete the quantitative prediction of H4 sand body.Based on the reservoir prediction results, it is proposed that H4 layer is a distributary channel deposit of shallow water delta meandering flow type, and further completed the dissection of sand body deposition model.After the development adjustment wells drilled, the coincidence between the actual drilled sand body thickness and the predicted sand body thickness is more than 84%.This paper explores and obtains the quantitative reservoir prediction scheme of SVR algorithm constrained by geological model under the condition of few wells on the sea, which completes the accurate prediction of the middle and deep distributary channel reservoir in X gas field, and also has certain guiding significance for the reservoir description of the same type of oil and gas fields.

Glutenite reservoirs are characterized by rapid lateral change, strong heterogeneity, and complex main controlling factors. This research aims to identify macro and micro characteristics of glutenite reservoirs and establish criteria for identifying favorable reservoirs studies. To this end, the tight sandy conglomerate of the Upper Wuerhe Formation in the Permian Upper Wuerhe Formation in the Dinan 15 well area of the eastern slope of the Dongdaohaizi sag in the Junggar Basin is studied. The core observation, physical property analysis, sensitivity analysis, casting thin section, scanning electron microscope and logging data are adopted to comprehensively analyze the characteristics of glutenite reservoirs. By integrating various reservoir characteristic parameters, this work constructed a new set of reservoir evaluation criteria to predict favorable areas for the Upper Wuerhe Formation in Dongdaohaizi Sag. The results show that the lithology of the Upper Wuerhe Formation in the Dinan 15 well area is dominated by gray glutenite; the reservoir is an ultra-low porosity and ultra-low permeability reservoir with moderately weak water sensitivity and weak velocity sensitivity. The reservoir space types of the layers are mainly intergranular pores; the shape of the mercury intrusion curve and the pore throat radius distribution of the samples show that the reservoir is skewed, poorly sorted, and has the characteristics of small pores and thin throats. This work constructed evaluation criteria for reservoirs from I to III by utilizing lithology, physical properties, sensitivity, reservoir space type, and microstructural characteristics as key parameters. The favorable reservoir distribution area is mainly located in the west of the block, which is the focus of the next exploration of the Upper Wuerhe Formation area. The research results improved understanding of glutenite reservoir characteristics and will serve as significant guidance for the oil and gas exploration in the Dinan 15 well area.

Influenced by tectonic activities such as the Tan-Lu Fault and mantle uplift, frequent volcanic activity in the Eastern Sag of the Liaohe Basin has resulted in the widespread development of volcanic rocks, such as basalt and trachyte, in the third member of the Shahejie Formation. Exploration and development have confirmed the presence of large oil and gas displays in trachyte reservoirs. However, previous studies have focused on the whole Eastern Sag or the volcanic rocks in the north, middle, and south sections of the Eastern Sag, while the J34 block has rarely been examined. There are challenges associated with the exploration and development of the J34 block, such as difficulties with trachyte lithology identification, reservoir space identification, quantitative logging identification, and prediction of horizontal and vertical distribution of lithologic characteristics. Therefore, this study presents an investigation and analysis of the characteristics of trachyte reservoirs, and the identification and prediction of favorable reservoirs based on core data, core sampling analysis, well logging, seismic data, and development dynamic data. The results showed that the volcanic rocks in the study area mainly include three types of lithology: trachytic breccia, trachytic lava, and basaltic lava, with trachytic breccia being the most important and favorable reservoir lithology. The trachyte reservoir space is mainly fractured and vuggy, and the secondary reservoir space is often superimposed onto the primary reservoir space, which significantly increases reservoir performance. Using the logging response characteristics of different lithologies and optimizing sensitivity curves, the quantitative identification criteria of different types of volcanic rocks were established. At the same time, the distribution characteristics of favorable reservoirs were determined by reconstructed wave impedance inversion with sensitive parameters, which showed that favorable reservoirs are mainly distributed along faults. Further analysis of the controlling factors of trachyte reservoirs can effectively guide the oil and gas development in this block and provide a reference for the exploration and development of similar blocks. [ABSTRACT FROM AUTHOR]

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Porosity is a vital parameter in reservoir research. In the process of oil exploration, reservoir research is very important for oil and gas exploration. Because it is necessary to take cores for indoor test in order to accurately obtain the porosity value of cores, this process consumes significant manpower and material resources. Therefore, this paper introduces the method of machine learning to predict the porosity by using logging curves. This paper creatively develops a WOA (whale optimization algorithm) optimized Elman neural network model to predict porosity through logging parameters PE, DEN, M2R1, AC, GR, R25, R4 and CNL. Porosity measurement is constructed by taking cores for indoor experiments. It contains a total of 328 sample points. The data is divided into training set and test set. The logging parameters are used as the input parameters of the prediction model, and the porosity measured in the laboratory are used as the output parameter. In order to evaluate the performance of the model, RMSE, R2, MAE and VAF evaluation indexes are introduced to evaluate. This paper also introduces the non-optimized Elman neural network and BP neural network to compare with this optimization model. The research shows that the WOA algorithm optimizes the super parameters of the Elman neural network, so that the performance of the WOA–Elman model is better than the Elman neural network model and the BP neural network model. [ABSTRACT FROM AUTHOR]

J gas field of Xihu Sag is one of the major production contribution fields in the East China Sea. With the further development and production, the main gas well E began to produce water and gradually stopped production, which had a great impact on the oil and gas production in the gas field. It is urgent to analyze reservoir distribution and water energy. Based on the core observation description, the sedimentary environment and facies of Pinghu Formation in the study area were analyzed according to the rock types, sedimentary structures, sedimentary cycles and logging data. On this basis, seismic interpretation based on seismic waveform classification was carried out for the main layer P7, and three types of seismic waveform classification signs were identified and counted. According to seismic forward modeling, the reliability of interpretation results was preliminary verified. Combined with modern sedimentary model, the reservoir distribution of P7 was depicted. The results show that there are three delta lobes in the study area. Accordingly, the water multiple of P7 gas reservoir is about 20 times, and the main inflow direction is north to east, and the updated reservoir numerical effect is well fitted, which has important guiding significance for the treatment of well extraction and potential exploitation. [ABSTRACT FROM AUTHOR]

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Seismic attributes analysis technique is an important tool for sand thickness prediction. Due to the varieties of seismic attributes, the best seismic attributes need to be optimized before the seismic attributes analysis technique is applied to reduce the repeatability and redundancy of the attributes. Therefore, we present an improved random forest regression algorithm combined with associate rules for sand thickness prediction (AR-RFR). Although random forest regression algorithm(RFR) is powerful for the problem characterized for nonlinearity and high dimension in reservoir prediction, it cannot solve attribute reduction problems. The associate rules can find the non-linear relationship among the multi-attributes and can reduce some redundant attributes by means of Chi-squared Test. We apply ordinary RFR, AR-RFR and Neural network regression algorithm combined with associate rules(AR-BP) to a synthetic geological model and a real dataset. The results prove that the selection attributes from associate rules is more efficient than that from random forest. Compared to the drilled wells, AR-RFR has higher precision than RFR and AR-BP. And AR-RFR also can improve the lateral distribution of sand bodies. The method proposed is able to choose efficient seismic attributes and improve prediction of sand thickness.