首页期刊导航|Journal of Petroleum Science & Engineering
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Journal of Petroleum Science & Engineering
Elsevier Science B.V.
Journal of Petroleum Science & Engineering

Elsevier Science B.V.

0920-4105

Journal of Petroleum Science & Engineering/Journal Journal of Petroleum Science & Engineering
正式出版
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    Characteristics of trace elements in crude oil in the east section of the south slope of Dongying Sag and their application in crude oil classification

    Zicheng NiuWei MengYongshi Wang
    11页
    查看更多>>摘要：The east section of the south slope of the Dongying Sag is rich in oil and gas resources, with diverse reservoir types and complex physical properties of crude oil. It has typical characteristics of multi-source and multi-stage accumulation. The classification of crude oil types is the basis of petroleum geology research. By analyzing the content and distribution characteristics of trace elements in typical crude oil samples, combined with mathematical statistics, the effective classification of crude oil types in the study area is realized. The content of alkali metals, alkaline earth metals and transition metal elements in crude oil is significantly higher than that of rare earth elements, and the content of light rare earth elements is higher than that of heavy rare earth elements. There is a good correlation between the total amount of trace elements and the bulk composition of crude oil, while the correlation between the total amount of rare earth elements and the bulk composition of crude oil is relatively poor. The cluster analysis of multiple trace element-related parameters can classify the crude oil into three types, and the biomarker parameters of different type crude oils show obvious differences, indicating that the use of trace elements to classify crude oil types is effective and feasible in the study area. This study shows that the content, distribution characteristics and related parameters of trace elements in crude oil can be used as effective oil source correlation tools, reasonable selection of correlation parameters, and comprehensive analysis of cluster analysis methods can accurately classify the types of crude oil.
      原文链接:
    • NSTL

    Lithofacies classification and sequence stratigraphic description as a guide for the prediction and distribution of carbonate reservoir quality;; A case study of the Upper Cretaceous Khasib Formation (East Baghdad oilfield, central Iraq)

    Mohamed I. Abdel-FattahAdnan Q. MahdiMustafa A. Theyab
    15页
    查看更多>>摘要：In the East Baghdad oilfield of central Iraq, the Upper Cretaceous Khasib Formation is the largest producing carbonate reservoir. The basic architecture of the "Khasib reservoirs" is heavily impacted by the lithofacies classification and sequence stratigraphic interpretation at East Baghdad field. A description of the Khasib Formation in terms of depositional systems, environments and lithofacies was obtained by studying the vertical distribution of the lithofacies, defined using well-log values and their mutual statistical relationships and constrained by the core data (thin sections). In this paper, a lithofacies classification and sequence stratigraphic interpretation are used to define reservoir geometry, lateral continuity and petrophysical characteristics of the Khasib reservoirs. The "Khasib" carbonate ramp stratigraphy exhibits different lithofacies in different depositional systems at the East Baghdad field. In addition, the log signatures and petrophysical properties of the same lithofacies vary. Multivariate statistical methods (principal component analysis and clustering) have been applied to separate stratigraphic packages and different carbonate lithofacies. Khasib carbonate lithofacies can be better discriminated from cluster analysis based on principal components rather than from arbitrary well log observations alone. The geometry of the strata in the 'Khasib' outer ramp is retrogradational, becoming aggradational during the lowstand systems tract (LST) and transgressive systems tract (TST). The highstand systems tract (HST) of the Khasib Formation is typically distinguished by a relatively thick aggradational-to-progradational geometry. This parasequence set is widespread upon the "Khasib" mid-and inner ramp. Petrographic analysis reveals the relative abundance and distribution of characteristic porosity types of the "Khasib" reservoirs. The HST (mid-and inner ramp) of "Khasib" Formation reveals the best reservoir characteristics (thickness, porosity, and hydrocarbon saturation) in the East Baghdad field. This enhancement of the knowledge of the lithofacies within a sequence stratigraphic framework through the application of principal component and cluster analysis is especially important to-optimally delineate and define the potential reservoir intervals of Iraq's Khasib Formation. Such multivariate statistical techniques may have similar beneficial application to carbonate reservoirs elsewhere in the world.
      原文链接:
    • NSTL

    Multi-parameter pre-stack seismic inversion based on deep learning with sparse reflection coefficient constraints

    Danping CaoYuqi SuRongang Cui
    16页
    查看更多>>摘要：In the field of seismic inversion, Convolutional Neural Network (CNN) has been extensively applied for their powerful capability of feature extraction and nonlinear fitting. However, the insufficient amount of labeled seismic inversion dataset impedes the application of CNN in seismic elastic inversion. Besides, the lack of effective geophysical constraints in conventional CNN will make the network prone to over-fitting, leading to unstable inversion results. In this paper, a workflow is developed for generating sufficient and diverse datasets for pre-stack seismic inversion with limited log and seismic data. The Sequential Gaussian Co-Simulation algorithm is used to simulate the changes in the reservoir space under the constraints of the low-frequency model. At the same time, the Elastic Distortion algorithm is used to simulate the complex geological structures. This can increase the diversity of the strata longitudinal combination by enriching the combination mode of stratigraphic parameters. Besides, the combination of a U-net and three fully connected networks (UCNN) is proposed to predict the elastic parameters from seismic data. In UCNN, the sparse reflection coefficient is used as a constraint to improve the accuracy of the network. The performance of this method was evaluated by synthetic and field data examples. The results show not only the effectiveness of the proposed method but also demonstrate its outperfbrmance over the conventional deep learning method. The R2 scores of density, Vp and Vs are 0.94, 0.98, 0.98.
      原文链接:
    • NSTL

    Controlling factors on elastic wave velocities of conglomerate-Experimental and theoretical study

    Xuehui HanHao ZhangJunxin Guo
    15页
    查看更多>>摘要：As an important type of clastic rocks, the conglomerate has quite different physical properties compared to sandstone due to its strong heterogeneity. Hence, studying the relationship between conglomerate physical properties and elastic wave velocities is essential for the seismic exploration or sonic logging interpretation of conglomeratic reservoirs. In this work, various physical properties and elastic wave velocities of dry conglomerate samples were measured, based on which their relationships were investigated. The results show that the gravel content and porosity are two controlling factors on conglomerate elastic wave velocities. For the studied samples, the content of middle-sized gravels (diameter >4 mm) affects the elastic wave velocities greatly due to their high elastic moduli. Besides the gravel content and porosity, the gravel contact patterns and cementation types are key geometrical details that also affect elastic wave velocities significantly. Different gravel contact patterns and cementation types result in notable differences in elastic wave velocities. To further illustrate the effects of geometrical details on elastic wave velocities, a theoretical modelling approach that accounts for gravel contact patterns and cementation types was proposed. The predictions given by this theoretical modelling approach agree well with experimental results, which validates the important roles of gravel contact patterns and cementation types on conglomerate elastic wave velocities. This work provides a basis for inverting conglomerate physical properties from elastic wave velocities, which is helpful for the seismic exploration or sonic logging interpretation of conglomeratic reservoirs.
      原文链接:
    • NSTL

    Fractured formation evaluation by seismic attenuation derived from array acoustic log waves based on modified spectral ratio method and an extended Biot's poroelastic model

    Yaxi LiJinsong ShenWenyuan Cai
    13页
    查看更多>>摘要：A comprehensive application of the seismic attenuation deduced from the array acoustic log waves by using modified spectral ratio method together with an extended Biot's poroelastic model has been developed for detection and evaluation of fractures in carbonate and sandstone formations. First, profile of amplitude ratio based on deconvolution interferometry (DCI) and modified spectral ratio method (MSRM) are introduced and applied on array acoustic waveforms to derive the seismic attenuation factors. Second, an extended Biot's poroelastic model with consideration of fracture parameters is described and derived for its expression of attenuation. Both the computational method and the theoretical model are tested on simulated sonic data and meaningful results are obtained which are connected with analysis of application on field data in final section. Finally, the MSRM and extended Biot's poroelastic model are applied to field waveforms acquired from carbonate and sandstone formation in oilfields of Petro China. In the present study, we mainly focused on the subject of fracture identification and evaluation in both carbonate and sandstone formation. Field application results show that within frequency range of 1-40 kHz, attenuation derived from sonic log waves by using MSRM has close relationship with fracture development. Meanwhile, the simulation responses based on the extended Biot's poroelastic model verify the fact that the attenuation magnitude is mainly sensitive to fracture porosity. Our results also suggest that profile of amplitude ratio can be a potentially useful indicator of fracture identification and evaluation, and, maybe also an interpreting toll of radial heterogeneity.
      原文链接:
    • NSTL

    Three-dimensional interwell electromagnetic detection

    Xijin SongXuelong WangHuiqin Jia
    20页
    查看更多>>摘要：Electromagnetic methods play an important role in the exploration and development of oil and gas resources and metal minerals. The ground electromagnetic method is largely limited by the detection depth and resolution in actual work. In order to improve the detection accuracy and working efficiency of interwell electromagnetic detection, this paper proposes a three-dimensional inter-well electromagnetic detection method. The method utilises one or more production wells in the production well pattern to construct a transmission galvanic couple source, which effectively reduces the shielding effect of the metal casing on the transmission signal in the well and improves the transmission signal power. The downhole and the ground receiving arrays were used for simultaneous observation, with high observation network density and detection accuracy. With pseudo-random multi-frequency signals as excitation, the working efficiency of the electromagnetic detection is significantly improved. The downhole receiving array makes the measuring electrode closer to the target geological body, increasing the response signal intensity of the anomalous body. Meanwhile, the impact of electromagnetic interference on the ground is effectively reduced, and the detection depth is increased. The ground receiving array is composed of multiple measurements points located on concentric circular measuring lines, which is beneficial for the identification of the azimuth and angle information of underground geological targets. A detection model for anomalous bodies with different parameters in the formation was established. Numerical results show that for anomalous bodies with different resistivities in the formation, the electric field response curves on the ground and the downhole receiving arrays are significantly different. Because the emission source is located on the symmetry axis of the model, the excited field is distributed axisymmetrically. The ground observation response can effectively identify anomalous bodies with different azimuths and electrical parameters in the formation. The downhole observation response is sensitive to the upper and lower interfaces of the abnormal body, and the depth information of the abnormal body can be accurately determined. The calculation results of the oilfield water injection dynamic monitoring model show that the electric field response curves on the ground receiving arrays of different measuring lines can effectively reflect the water injection process of the underground reservoir, including the direction of water penetration and the change in reservoir resistivity. The research results show that this method can better identify the resistivity characteristics, azimuth information, and depth information of anomalous downhole bodies, while effectively delineating the reservoir boundary and revealing reservoir changes. This provides a theoretical basis and technical ideas for the three-dimensional electromagnetic detection of complex interwell geological bodies.
      原文链接:
    • NSTL

    Application of machine learning to quantification of mineral composition on gas hydrate-bearing sediments, Ulleung Basin, Korea

    Sun Young ParkByeong-Kook SonJiyoung Choi
    14页
    查看更多>>摘要：Mineral quantification is essential to evaluate gas hydrate (GH) resources because the mineral composition is closely related to the origin of sediment, the reservoir properties, and even the existence of GH. However, it is difficult to analyze the mineral compositions of GH-rich unconsolidated layers because of complex compositional combinations. Thus far, mineral composition has relied on experts' analysis. In this study, a machine learning model was developed that can efficiently probe the mineral composition from the X-ray diffraction (XRD) patterns of GH sediments in the Ulleung Basin, Korea, which has a complex composition including 12 minerals. It is the first time that machine learning algorithms have been applied to complex natural GH sediment with 12 minerals, including amorphous materials. To build a reliable data-driven analysis model, after data acquisition and preprocessing were conducted, various machine learning algorithms were employed, including convolutional neural network (CNN), recurrent neural network (RNN), multi-layer perceptron (MLP), and random forest (RF). For data acquisition, 488 sediment samples were analyzed by XRD experiment, and the intensity profiles were manually analyzed by an expert to obtain their mineral compositions. In particular, the intensity profile according to the XRD angle of incidence serves as the input data, and the corresponding 12 mineral composition provides label data for supervised learning. The RF approach yields the best prediction result with an average mean absolute error (MAE) of 2.56%, and the other algorithms also exhibits reasonable performance with average MAEs of less than 3%. If XRD is newly performed for GH sediment, the intensity profile can be automatically analyzed by the trained machine learning model in seconds. This approach will enable experts to analyze mineral compositions efficiently and reliably.
      原文链接:
    • NSTL

    Estimation of shear wave velocity in an Iranian oil reservoir using machine learning methods

    Arash EbrahimiAmin IzadpanahiParirokh Ebrahimi
    16页
    查看更多>>摘要：Shear wave velocity is considered as one of the most important rock physical parameters which can be measured by dipole sonic imager (DSI) tool. This parameter is applied to evaluate porosity and permeability, rock mechanical parameters, lithology, fracture assessment, etc. On the other hand, this data is not available in all wells and hence, an accurate and reliable estimation of this parameter with the least uncertainty is of great importance in reservoir characterization. In this study, regression, multi-layer perceptron artificial neural network (MLP-ANN), adaptive neuro-fuzzy inference system (ANFIS) and multi-gene genetic programming (MGGP) methods are utilized to estimate the shear wave velocity using well log data. Also, the reported empirical correlations in the literature are also investigated in the studied field. The input data include depth, effective porosity, Vp, gamma ray logs (natural and spectral), neutron log, density log and caliper log from the Bangestan Group Formation in one of the fields in southwestern Iran. In this study, all the expressed methods are compared based on the best coefficient of determination (R2), root mean square error (RMSE), mean squared error (MSE), average absolute relative error (AARE), and average relative error (ARE). Among the used methods, MGGP was developed for using the useful features of this method including sensitivity analysis and correlation. Sensitivity analysis is performed on the input data using the MLP-ANN and MGGP method. Also, a correlation is suggested based on the MGGP method which is able to predict the shear wave velocity using the mentioned input parameters. The results show that the MLP-ANN method is more accurate, reliable and efficient compared to other methods studied in this paper. R2 for the train, validation, and test phase are 0.9973, 0.9901 and 0.9898, respectively. The results of sensitivity analysis imply that compressional wave velocity has the highest impact on the shear wave velocity. Finally, Young Dynamic Modulus and Poisson Dynamic Ratio are computed using both real and predicted shear wave velocities. The results indicate that these two parameters can be calculated with high accuracy using predicted shear wave velocity.
      原文链接:
    • NSTL

    An improved lithology identification approach based on representation enhancement by logging feature decomposition, selection and transformation

    Shangyuan LiKaibo ZhouLuanxiao Zhao
    13页
    查看更多>>摘要：As the accumulation of logging data and the enhancement of computational power, machine learning technology has been progressively applied to logging interpretation field such as lithology identification. However, in traditional data-driven lithology identification model, the implied variation information of logging curve and coupling relationships among features are not fully mined. Additionally, feature extraction cannot filter out information redundancy and noise. We propose logging data representation enhancement approach for lithology identification based on feature decomposition, selection and transformation, converting the raw logging curves into an improved high dimensional representation with more effective information and less noise. Local mean decomposition is used to extract the variation information of logging curves from multiple depth scales and add them to the features of adjacent samples. Considering the different contribution of features to lithology identification, an optimized feature selection method based on Shapley additive explanation is designed to reduce redundant and noisy information in logging data. To mine the complementary information among sequence features, a representation learning model integrating feature transformation and lithology classification is developed by multi-grained scanning and cascading extreme learning machine. The effectiveness and generalization of the proposed approach are verified on the baseline and shale oil field datasets. The results show that the proposed approach can make the logging data acquire more valid information through representation enhancement, which helps to achieve high-accuracy lithology identification.
      原文链接:
    • NSTL

    Mineralogy, organic geochemistry, and micro structural characterization of lacustrine Shahejie Formation, Qikou Sag, Bohai Bay Basin;; Contribution to understanding microcosmic storage mechanism of shale oil

    Chao MaXianzheng ZhaoTao Yang
    21页
    查看更多>>摘要：Shale samples of lacustrine Shahejie Formation from the Well F39X1 drilled in Qikou sag were used to study microstructures, mineralogy, and organic geochemistry and their impacts on oil storage response. Samples are quartz and clay rich and contain a variable amount of calcite, dolomite, plagioclase, and pyrite minerals. The TOC content ranges between 0.33 and 2.59 wt %, Rock-Eval Si and S2 values range from 0.05 to 1.07 mg/g and 0.12-9.57 mg/g, respectively. The maximum yield temperature (Tmax) of pyrolysate ranges from 440 °C to 467 °C, and vitrinite reflectance calculated based on Tmax between 0.76 and 1.25%. High frequency 2D nuclear magnetic resonance (NMR) results show free oil contents are 1.176~(-1).909 jil/g, with an average of 1.542 jil/g. Mineral-related pores are dominant and volumetrically significant with a multimodal pore-throat size distribution, they could provide significant storage space and have good microstructural connectivity. Their roles in microscopic storage of oil molecules mainly depends on two mechanisms;; (1) rigid mineral grains preserve large pore networks, within which residing a very large volume of free oil, and (2) clay particles existed within large interparticle pores alter pore throat size distribution, resulting in a slight increasing of adsorbed oil onto pore walls. The Well F39X1 is an important shale oil exploration well for Shahejie Formation. To reduce exploration risk and determine economic feasibility, knowledge of liquid hydrocarbon molecule microscopic storage mechanism is required so producible shale oil resources can be quantified. The investigation of oil-bearing shale mineralogy, organic geochemistry, and microstructures is an important step in better understanding of the pore network development and their related microscopic storage mechanism for oil in lacustrine shale. We suggested that siliceous Shahejie shales in Qikou sag with moderate TOC and suitable thermal maturity have well-developed pore networks and high residual hydrocarbons, which should be the important target for lacustrine shale oil exploration. Also, the well-developed mineral-related pore networks should be included in attempts to build realistic microscopic storage models of shales.
      原文链接:
    • NSTL