渤中19-6构造复杂储层流体评价及产能预测
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摘要
渤中19-6构造的孔店组砂砾岩系及太古界变质岩系,由于储层岩性复杂、储层物性差异大,储层流体识别及油气藏类型判别困难,早期产能预测难度大。本文充分挖掘录井、测井参数在复杂储层流体评价及产能预测方面的优势:①应用皮克斯勒图版、异常倍数、Bar图分析法、Flair流体指数法、含烃丰度指数法及岩石力学参数泊松比及体积压缩系数交会图版、测压流体回归分析等技术结合进行地层流体性质识别;②利用井流物组分相态分析法、四组合参数法等方法进行油气藏流体类型判别;③针对低孔渗砂砾岩储层,依据静态的测井、录井、岩心分析等资料构建地层模型,根据动态的电缆地层测试及核磁共振资料获取油藏参数,据此建立了渤中19-6构造复杂储层数值模拟产能预测方法。实践表明,该组合方法能有效识别复杂储层内流体相变化特征,识别储层流体性质;能有效判别油气藏流体类型;在低孔渗砂砾岩储层,能较准确地进行产能预测,最终测试结论证实本方法的可靠性。
Kongdian Formation glutenite series and Archean metamorphic rock series in Bozhong 19-6 structure are characterized by complex reservoir lithologies and significant difference of reservoir physical properties, so it is difficult to identify the reservoir fluids,discriminate the oil and gas reservoir type and predict the early productivity. In this paper, the advantages of mud logging and logging parameters in the fluid evaluation and productivity prediction of complex reservoirs were tapped sufficiently. First, the properties of formation fluids were identified by combining Pixler chart, abnormal multiple, Bar diagram analysis method, Flair fluid index method, hydrocarbon abundance index method, rock mechanical parameter crossplot(Poisson's ratio and volume compressibility coefficient), and piezometric fluid regression analysis comprehensively. Second, the types of fluids in oil and gas reservoirs were discriminated by means of the phase state analysis method of components of fluids in well and the four-combined-parameter method. Third, as for low-porosity and low-permeability glutenite reservoirs, the stratigraphic model was established based on static data(e.g. logging, mud logging and core analysis), the oil reservoir parameters were calculated by using the dynamic data of wireline formation test and nuclear magnetic resonance(NMR), and accordingly the numerical simulation based productivity prediction method suitable for the complex reservoirs in Bozhong 19-6 structure was developed. Practice shows that by virtue of this combined method, the phase change characteristics of fluids in complex reservoirs can be identified effectively, the properties of reservoir fluids can be identified, and the types of fluids in oil and gas reservoirs can be discriminated. And when it is applied to low-porosity and low-permeability glutenite reservoirs, the productivity can be predicted accurately. It is verified from the final test conclusions that this combined method is reliable.
Kongdian Formation glutenite series and Archean metamorphic rock series in Bozhong 19-6 structure are characterized by complex reservoir lithologies and significant difference of reservoir physical properties, so it is difficult to identify the reservoir fluids,discriminate the oil and gas reservoir type and predict the early productivity. In this paper, the advantages of mud logging and logging parameters in the fluid evaluation and productivity prediction of complex reservoirs were tapped sufficiently. First, the properties of formation fluids were identified by combining Pixler chart, abnormal multiple, Bar diagram analysis method, Flair fluid index method, hydrocarbon abundance index method, rock mechanical parameter crossplot(Poisson's ratio and volume compressibility coefficient), and piezometric fluid regression analysis comprehensively. Second, the types of fluids in oil and gas reservoirs were discriminated by means of the phase state analysis method of components of fluids in well and the four-combined-parameter method. Third, as for low-porosity and low-permeability glutenite reservoirs, the stratigraphic model was established based on static data(e.g. logging, mud logging and core analysis), the oil reservoir parameters were calculated by using the dynamic data of wireline formation test and nuclear magnetic resonance(NMR), and accordingly the numerical simulation based productivity prediction method suitable for the complex reservoirs in Bozhong 19-6 structure was developed. Practice shows that by virtue of this combined method, the phase change characteristics of fluids in complex reservoirs can be identified effectively, the properties of reservoir fluids can be identified, and the types of fluids in oil and gas reservoirs can be discriminated. And when it is applied to low-porosity and low-permeability glutenite reservoirs, the productivity can be predicted accurately. It is verified from the final test conclusions that this combined method is reliable.
引文
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[2]何羽飞,万金彬,刘淼,程道解,邢军,黄娅.基于测井多参数的复杂储层岩性综合识别[J].测井技术,2015,39(1):48-51.HE Yufei,WAN Jinbin,LIU Miao,CHENG Daojie,XING Jun,HUANG Ya.Complex reservoirs lithology recognition based on multi-parameter[J].Well Logging Technology,2015,39(1):48-51.
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[4]朱如凯,金旭,王晓琦,刘晓丹,李建明,孙亮,吴松涛,苏玲,焦航,崔景伟.复杂储层多尺度数字岩石评价[J].地球科学,2018,43(5):1773-1782.ZHU Rukai,JIN Xu,WANG Xiaoqi,LIU Xiaodan,LIJianming,SUN Liang,WU Songtao,SU Ling,JIAOHang,CUI Jingwei.Multi-Scale Digital Rock Evaluation on Complex Reservoir[J].Earth Science,2018,43(5):1773-1782.
[5]朱建敏,贾楠,杨贯虹,刘学,穆朋飞.渤海复杂薄互沉积储层地震定量预测方法及应用--以垦利X油田沙河街组储层为例[J].复杂油气藏,2018,11(1):18-22.ZHU Jianmin,JIA Nan,YANG Guanhong,LIU Xue,MU Pengfei.Quantitative seismic prediction method of complex thin reservoirs in the Bohai Sea:a case study of Shahejie Formation in Kenli X Oilfield[J].Complex Hydrocarbon Reservoirs,2018,11(1):18-22.
[6]童凯军,程奇,聂玲玲,房娜.变质岩潜山储集层有效性评价[J].石油与天然气地质,2015,36(5):780-787.TONG Kaijun,ChENG Qi,NIE Lingling,FANG Na.Evaluation of effectiveness of metamorphosed basement buried hill reservoirs[J].Oil&Gas Geology,2015,36(5):780-787.
[7]赵贤正,才博,金凤鸣,罗宁,王霞,何春明.富油凹陷二次勘探复杂储层油气藏改造模式--以冀中坳陷、二连盆地为例[J].石油钻采工艺,2016,38(6):823-831.ZHAO Xianzheng,CAI Bo,JIN Fengming,LUO Ning,WANG Xia,HE Chunming.Stimulation mode of complex oil and gas reservoirs in oil-rich sag during the secondary exploration:the case study on Jizhong Depression and Erlian Basin[J].Oil Drilling&Production Technology,2016,38(6):823-831.
[8]司马立强,吴丰,马建海,房国庆,于航.利用测录井资料定量计算复杂油气水系统的气油比--以柴达木盆地英东油气田为例[J].天然气工业,2014,34(7):34-40.SIMA Liqiang,WU Feng,MA Jianhai,FANG Guoqing,YU Hang.Quantitative calculation of GOR of a complex oil-gas-water system with logging data:A case study from the Yingdong oil/gas field,Qaidm Basin[J].Natural Gas Industry,2014,34(7):34-40.
[9]蒋云箭.裂缝-孔洞型储层测井流体识别新方法[J].测井技术,2017,41(6):676-679.JIANG Yunjian.New method of fluid type identification for fracture-vug type reservoirs[J].Well Logging Technology,2017,41(6):676-679.
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[12]李闯.火山岩储层天然气产能预测方法研究[J].测井技术,2014,38(6):722-726,744.LI Chuang.On forecast method for volcanics gas production capactiy using logging data[J].Well Logging Technology,2014,38(6):722-726,744.
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[14]李戈理.测井-测试综合评价方法及产能预测[J].测井技术,2015,39(6):798-801.LI Geli.Comprehensive evaluation methodology based on imaging log&fracturing test data and productivity prediction[J].Well Logging Technology,2015,39(6):798-801.
[15]薛永安,李慧勇.渤海海域深层太古界变质岩潜山大型凝析气田的发现及其地质意义[J].中国海上油气,2018,30(3):1-9.XUE Yongan,LI Huiyong.Large condensate gas field in deep Archean metamorphic buried hill in Bohai sea:discovery and geological significance[J].China Offshore Oil and Gas,2018,30(3):1-9.
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