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Turbidite reservoir identification technology based on prestack multi-parameter sensitivity factor fusion |
SHANG Wei( ), ZHANG Yun-Yin, KONG Xing-Wu, LIU Feng |
Geophysical Research Institute of Shengli Oilfield Company,SINOPEC,Dongying 257000,China |
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Abstract Turbidite reservoirs have always been an important exploration type in the Jiyang depression.After years of exploration and development,the turbidites are mainly of the heterogeneous isomorphic type.The sandstone reservoirs of this type of turbidites have similar velocity,density,and seismic waveforms to those of non-reservoirs and thus are difficult to identify using conventional seismic attributes and poststack impedance.Therefore,a reservoir description method based on prestack multi-parameter sensitivity factor fusion was established.This method mainly included three steps.Firstly,major factors affecting the accuracy of shear wave estimation were analyzed,and then the multi-mineral-component shear wave prediction technology based on a modified xu-white model was established to improve the accuracy of shear wave prediction and lay a foundation for the accurate prediction of elastic parameters. Secondly,a quantitative evaluation method of sensitivity factors was proposed based on reflection coefficient ratios to obtain three sensitive elastic parameters,namely Murho,Lambrho,and POIS.The fusion index F of sensitivity factors was constructed by using the three elastic parameters.The purpose is to reduce the strong multiplicity of solutions of a single parameter and accurately identify rock properties.Thirdly,the prestack inversion technology was used for the inversion of sensitive elastic parameters.The three sensitivity parameters of sandstone information were fused using the fusion model of the RGB primary color information to realize a fine-scale prediction of lithology.This method was applied to the exploration of a deep-water turbidite reservoir around well-Tuo-71 in the Jiyang depression.The distribution of deep-water turbidite fan reservoirs in the study area was accurately predicted.The coincidence degree between the prediction results and the actual drilling reached 85%,indicating the improved accuracy of reservoir identification and description.The results of this study have contributed to an interpreted favorable sand body area of 9.5 km2 and the deployment of more than 10 exploration and development wells.Among these wells,five have yielded industrial oil flow after competition and being put into operation,and their new production capacity is expected to be 2×104 t。
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Received: 24 August 2021
Published: 17 August 2022
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| 矿物成分 | 孔隙 度φ | 含水饱 和度Sw | 灰岩孔 隙纵横 比αca | 砂岩孔 隙纵横 比αsa | 泥岩孔 隙纵横 比αsh | 速度 | 密度 ρ/(g·cm-3) | 灰岩 含量Vca | 砂岩 含量Vsa | 泥质 含量Vsh | 纵波速度 vp/(m·s-1) | 横波速度 vs/(m·s-1) | 参数值 | 0.4 | 0.3 | 0.3 | 0.2 | 0.5 | 0.12 | 0.05 | 0.09 | 2742 | 1651.5 | 2.307 |
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Model trial data
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矿物成分 | 孔隙度φ | 流体替换 | 孔隙纵横比 | Vca | Vsa | Vsh | Sw | Sg | So | αca | αsa | αsh | 0~0.6 | 0.3 | 1-Vca-Vsa | 0.1~0.5 | 0.1~1 | 0 | 1-Sw-Sg | 0.02~0.2 | 0.06~0.12 | 0.03~0.07 |
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Parameter setting of shear wave prediction
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The effect of limy content on P-wave velocity,S-wave velocity and density
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Effect of porosity on P-wave velocity,S-wave velocity and density
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Influence of water saturation on P-wave velocity,S-wave velocity and density
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Flow chart of petrophysical modeling
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The map of predicted shear wave in Y926-x1 well
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序号 | 岩性 因子 | 砂岩 | 泥岩 | 砂岩—泥岩 识别因子 | 灰质泥岩 | 砂岩—灰质岩 识别因子 | 1 | 纵波速度vp/(m·s-1) | 3.80×103 | 2.90×103 | 0.134 | 3.10×103 | 0.101 | 2 | 横波速度vs/(m·s-1) | 1.70×103 | 1.50×103 | 0.063 | 1.60×103 | 0.030 | 3 | 密度ρ/(g·cm-3) | 2.50×103 | 2.27×103 | 0.048 | 2.40×103 | 0.020 | 4 | 纵波阻抗Zp/[(kg·m-3)(m·s-1)] | 9.50×106 | 6.58×106 | 0.181 | 7.44×106 | 0.122 | 5 | 横波阻抗Zs/[(kg·m-3)(m·s-1)] | 4.25×106 | 3.41×106 | 0.110 | 3.84×106 | 0.051 | 6 | 纵横波速度比vp/vs | 2.24 | 1.93 | 0.072 | 1.94 | 0.071 | 7 | 泊松比σ | 3.75×10-1 | 3.17×10-1 | 0.083 | 3.18×10-1 | 0.081 | 8 | 体积模量K/MPa | 3.61×1010 | 1.91×1010 | 0.308 | 2.77×1010 | 0.220 | 9 | 剪切阻抗μ/(Pa·kg·m-3) | 7.23×109 | 5.11×109 | 0.172 | 6.14×109 | 0.081 | 10 | 拉梅阻抗λρ/(kg2·m-4·s-2) | 7.22×1013 | 3.17×1013 | 0.389 | 4.06×1013 | 0.280 | 11 | 剪切阻抗μρ/(Pa·kg·m-3) | 1.81×1013 | 1.16×1013 | 0.218 | 1.47×1013 | 0.101 | 12 | 杨氏模量E/(N·m-2) | 1.99×1010 | 1.35×1010 | 0.192 | 1.62×1010 | 0.102 | 13 | 拉梅系数λ | 3.61×1010 | 1.91×1010 | 0.308 | 2.31×1010 | 0.220 |
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13 Kinds of lithological identification factors
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参数 | 砂岩 | 泥岩 | R | 灰质泥岩 | R | 体积模量K/MPa | 3.61×1010 | 1.91×1010 | 0.308 | 2.31×1010 | 0.220 | 纵波阻抗Zp/[(kg·m-3)·(m·s-1)] | 9.50×106 | 6.58×106 | 0.181 | 7.44×106 | 0.122 | 拉梅阻抗λρ/(kg2·m-4·s-2) | 7.22×1013 | 3.17×1013 | 0.389 | 4.06×1013 | 0.280 | 岩性信息融合指数F | 1.23 | 2.92×10-1 | 0.616 | 5.17×10-1 | 0.408 |
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Three sensitive elastic parameter profiles a—longitudinal wave impedance;b—bulk modulus profile;c—Lame impedance profile
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Comparison of profile effect
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Comparison of profile effect
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Favorable reservoir prediction diagram
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