谭忠健, 胡云, 张国强, 李鸿儒, 刘坤, 杨保健. 渤中19-6构造复杂储层流体评价及产能预测[J]. 石油钻采工艺, 2018, 40(6): 764-774. DOI: 10.13639/j.odpt.2018.06.013
引用本文: 谭忠健, 胡云, 张国强, 李鸿儒, 刘坤, 杨保健. 渤中19-6构造复杂储层流体评价及产能预测[J]. 石油钻采工艺, 2018, 40(6): 764-774. DOI: 10.13639/j.odpt.2018.06.013
TAN Zhongjian, HU Yun, ZHANG Guoqiang, LI Hongru, LIU Kun, YANG Baojian. Fluid evaluation and productivity prediction on complex reservoirs in Bozhong 19-6 structure[J]. Oil Drilling & Production Technology, 2018, 40(6): 764-774. DOI: 10.13639/j.odpt.2018.06.013
Citation: TAN Zhongjian, HU Yun, ZHANG Guoqiang, LI Hongru, LIU Kun, YANG Baojian. Fluid evaluation and productivity prediction on complex reservoirs in Bozhong 19-6 structure[J]. Oil Drilling & Production Technology, 2018, 40(6): 764-774. DOI: 10.13639/j.odpt.2018.06.013

渤中19-6构造复杂储层流体评价及产能预测

Fluid evaluation and productivity prediction on complex reservoirs in Bozhong 19-6 structure

  • 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。

     

    Abstract: 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.

     

/

返回文章
返回