Sweet spots in the shale reservoirs of the Lower Silurian Longmaxi Formation in Weiyuan 201 Block of Sichuan Basin were predicted quantitatively using seismic data and fuzzy optimization method. First, based on seismi...Sweet spots in the shale reservoirs of the Lower Silurian Longmaxi Formation in Weiyuan 201 Block of Sichuan Basin were predicted quantitatively using seismic data and fuzzy optimization method. First, based on seismic and rock physics analysis, the rock physics characteristics of the reservoirs were determined, and elastic parameters sensitive to shale reservoirs with high gas content were selected. Second, data volumes with high precision of the elastic parameters were obtained from pre-stack simultaneous inversion. The horizontal distribution of key parameters for shale gas evaluation were calculated based on the results of rock physics analysis. Then, the fuzzy evaluation equation was established by fuzzy optimization method with test and logging data of horizontal wells with similar operation conditions. key parameters affecting the productivity of horizontal wells were sorted out and the weights of them in the sweet spots quantitative prediction were worked out by fuzzy optimization to set up a sweet spots evaluation system. Three classes of shale gas reservoirs which including two kinds of sweet spots were predicted with the above procedure, and the sweet spots have been predicted quantitatively by combining the above prediction results with the testing production. The testing results of 7 verification wells proved the reliability of the prediction results.展开更多
Shale reservoirs are characterized by low porosity and strong anisotropy. Conventional geophysical methods are far from perfect when it comes to the prediction of shale sweet spot locations, and even less reliable whe...Shale reservoirs are characterized by low porosity and strong anisotropy. Conventional geophysical methods are far from perfect when it comes to the prediction of shale sweet spot locations, and even less reliable when attempting to delineate unconventional features of shale oil and gas. Based on some mathematical algorithms such as fuzzy mathematics, machine learning and multiple regression analysis, an effective workflow is proposed to allow intelligent prediction of sweet spots and comprehensive quantitative characterization of shale oil and gas reservoirs. This workflow can effectively combine multi-scale and multi-disciplinary data such as geology, well drilling, logging and seismic data. Following the maximum subordination and attribute optimization principle, we establish a machine learning model by adopting the support vector machine method to arrive at multi-attribute prediction of reservoir sweet spot location. Additionally, multiple regression analysis technology is applied to quantitatively predict a number of sweet spot attributes. The practical application of these methods to areas of interest shows high accuracy of sweet spot prediction, indicating that it is a good approach for describing the distribution of high-quality regions within shale reservoirs. Based on these sweet spot attributes, quantitative characterization of unconventional reservoirs can provide a reliable evaluation of shale reservoir potential.展开更多
The Chang-63 reservoir in the Huaqing area has widely developed tight sandstone ＂thick sand layers, but not reservoirs characterized by rich in oil＂, and it is thus necessary to further study its oil and gas enrichm...The Chang-63 reservoir in the Huaqing area has widely developed tight sandstone ＂thick sand layers, but not reservoirs characterized by rich in oil＂, and it is thus necessary to further study its oil and gas enrichment law. This study builds porosity and fracture development and evolution models in different deposition environments, through core observation, casting thin section, SEM, porosity and permeability analysis, burial history analysis, and ＂four-property-relationships＂ analysis.展开更多
To establish the relationship among reservoir characteristics and rock physical parameters,we construct the well-bore rock physical models firstly,considering the influence factors,such as mineral composition,shale co...To establish the relationship among reservoir characteristics and rock physical parameters,we construct the well-bore rock physical models firstly,considering the influence factors,such as mineral composition,shale content,porosity,fluid type and saturation.Then with analyzing the change rules of elastic parameters along with the above influence factors and the cross-plots among elastic parameters,the sensitive elastic parameters of tight sandstone reservoir are determined,and the rock physics template of sweet spot is constructed to guide pre-stack seismic inversion.The results show that velocity ratio and Poisson impedance are the most sensitive elastic parameters to indicate the lithologic and gas-bearing properties of sweet spot in tight sandstone reservoir.The high-quality sweet spot is characterized by the lower velocity ratio and Poisson impedance.Finally,the actual seismic data are selected to predict the sweet spots in tight sandstone gas reservoirs,so as to verify the validity of the rock physical simulation results.The significant consistency between the relative logging curves and inversion results in different wells implies that the utilization of well-bore rock physical simulation can guide the prediction of sweet spot in tight sandstone gas reservoirs.展开更多
南阳凹陷黑龙庙地区砂砾岩体规模小,多期砂体叠置,沉积期次划分难,非均质性强、甜点区难以预测。为此,利用可视化技术对三维地震资料进行精细构造解析与古构造恢复,研究砂砾岩体成因。在对5口井层序地层划分基础上,依据砂组-砂体的沉积...南阳凹陷黑龙庙地区砂砾岩体规模小,多期砂体叠置,沉积期次划分难,非均质性强、甜点区难以预测。为此,利用可视化技术对三维地震资料进行精细构造解析与古构造恢复,研究砂砾岩体成因。在对5口井层序地层划分基础上,依据砂组-砂体的沉积旋回性与地震反射结构,划分砂砾岩体沉积期次;通过岩石物理分析与正演模拟明确砂砾岩体地球物理响应特征。通过井点处扇根、扇中、扇端地震反射特征与相同层位其他点处的地震反射特征进行相关分析,确定扇根、扇中、扇端的平面分布;实测砂砾岩体岩性、物性参数与波阻抗属性,交汇分析确定有效区分砂砾岩与泥岩的波阻抗属性的值域。依据高精度反演得到的波阻抗数据体,在砂砾岩体储层波阻抗值域及顶底反射层位控制下,求出有效储层厚度与分布,圈定砂砾岩体储层甜点区的范围。以此为依据在其甜点区上部署的HL1井日产油5.46 m 3,预测结果与实钻井一致。展开更多
基金Supported by the China National Science and Technology Major Project(2017ZX05035-02)
文摘Sweet spots in the shale reservoirs of the Lower Silurian Longmaxi Formation in Weiyuan 201 Block of Sichuan Basin were predicted quantitatively using seismic data and fuzzy optimization method. First, based on seismic and rock physics analysis, the rock physics characteristics of the reservoirs were determined, and elastic parameters sensitive to shale reservoirs with high gas content were selected. Second, data volumes with high precision of the elastic parameters were obtained from pre-stack simultaneous inversion. The horizontal distribution of key parameters for shale gas evaluation were calculated based on the results of rock physics analysis. Then, the fuzzy evaluation equation was established by fuzzy optimization method with test and logging data of horizontal wells with similar operation conditions. key parameters affecting the productivity of horizontal wells were sorted out and the weights of them in the sweet spots quantitative prediction were worked out by fuzzy optimization to set up a sweet spots evaluation system. Three classes of shale gas reservoirs which including two kinds of sweet spots were predicted with the above procedure, and the sweet spots have been predicted quantitatively by combining the above prediction results with the testing production. The testing results of 7 verification wells proved the reliability of the prediction results.
基金supported by National Science and Technology Major Project (No. 2017ZX05049002)NSFC and Sinopec Joint Key Project (U1663207)the National Key Basic Research Program of China (973 Program No. 2014CB239104)
文摘Shale reservoirs are characterized by low porosity and strong anisotropy. Conventional geophysical methods are far from perfect when it comes to the prediction of shale sweet spot locations, and even less reliable when attempting to delineate unconventional features of shale oil and gas. Based on some mathematical algorithms such as fuzzy mathematics, machine learning and multiple regression analysis, an effective workflow is proposed to allow intelligent prediction of sweet spots and comprehensive quantitative characterization of shale oil and gas reservoirs. This workflow can effectively combine multi-scale and multi-disciplinary data such as geology, well drilling, logging and seismic data. Following the maximum subordination and attribute optimization principle, we establish a machine learning model by adopting the support vector machine method to arrive at multi-attribute prediction of reservoir sweet spot location. Additionally, multiple regression analysis technology is applied to quantitatively predict a number of sweet spot attributes. The practical application of these methods to areas of interest shows high accuracy of sweet spot prediction, indicating that it is a good approach for describing the distribution of high-quality regions within shale reservoirs. Based on these sweet spot attributes, quantitative characterization of unconventional reservoirs can provide a reliable evaluation of shale reservoir potential.
文摘The Chang-63 reservoir in the Huaqing area has widely developed tight sandstone ＂thick sand layers, but not reservoirs characterized by rich in oil＂, and it is thus necessary to further study its oil and gas enrichment law. This study builds porosity and fracture development and evolution models in different deposition environments, through core observation, casting thin section, SEM, porosity and permeability analysis, burial history analysis, and ＂four-property-relationships＂ analysis.
基金supported by the National Key R&D Program of China(Grant No.2018YFC1405900)the Major Projects of National Science and Technology(Grant Nos.2016ZX05011-002,2016ZX05027-002-005)+3 种基金the National Natural Science Foundation of China(Grant No.41806073)the Natural Science Foundation of Shandong Province(Grant No.ZR2017BD014)Shandong Provincial Key Laboratory of Depositional Mineralization and Sedimentary Minerals,Shandong University of Science and Technology(Grant No.DMSM2017042)the Fundamental Research Funds for the Central Universities(Grant No.201964016)
文摘To establish the relationship among reservoir characteristics and rock physical parameters,we construct the well-bore rock physical models firstly,considering the influence factors,such as mineral composition,shale content,porosity,fluid type and saturation.Then with analyzing the change rules of elastic parameters along with the above influence factors and the cross-plots among elastic parameters,the sensitive elastic parameters of tight sandstone reservoir are determined,and the rock physics template of sweet spot is constructed to guide pre-stack seismic inversion.The results show that velocity ratio and Poisson impedance are the most sensitive elastic parameters to indicate the lithologic and gas-bearing properties of sweet spot in tight sandstone reservoir.The high-quality sweet spot is characterized by the lower velocity ratio and Poisson impedance.Finally,the actual seismic data are selected to predict the sweet spots in tight sandstone gas reservoirs,so as to verify the validity of the rock physical simulation results.The significant consistency between the relative logging curves and inversion results in different wells implies that the utilization of well-bore rock physical simulation can guide the prediction of sweet spot in tight sandstone gas reservoirs.
文摘南阳凹陷黑龙庙地区砂砾岩体规模小,多期砂体叠置,沉积期次划分难,非均质性强、甜点区难以预测。为此,利用可视化技术对三维地震资料进行精细构造解析与古构造恢复,研究砂砾岩体成因。在对5口井层序地层划分基础上,依据砂组-砂体的沉积旋回性与地震反射结构,划分砂砾岩体沉积期次;通过岩石物理分析与正演模拟明确砂砾岩体地球物理响应特征。通过井点处扇根、扇中、扇端地震反射特征与相同层位其他点处的地震反射特征进行相关分析,确定扇根、扇中、扇端的平面分布;实测砂砾岩体岩性、物性参数与波阻抗属性,交汇分析确定有效区分砂砾岩与泥岩的波阻抗属性的值域。依据高精度反演得到的波阻抗数据体,在砂砾岩体储层波阻抗值域及顶底反射层位控制下,求出有效储层厚度与分布,圈定砂砾岩体储层甜点区的范围。以此为依据在其甜点区上部署的HL1井日产油5.46 m 3,预测结果与实钻井一致。
