Autor: |
Huan Liu, Lv Tan, Gang Wei, Jinxiu Xu, Yunjiang Cui, Xiaogang Liu, Radu Coman, Holger Tietjen |
Rok vydání: |
2017 |
Předmět: |
|
Zdroj: |
Day 1 Tue, October 17, 2017. |
DOI: |
10.2118/186206-ms |
Popis: |
Environmental concerns have resulted ina ban onnuclear logging-while-drilling (LWD) methods containing radioactive sources and of oil-based mud in Bohai Bay, China, which is an environmental sensitive area. To avoid the risk of a radioactive source falling into the wellbore and to keep the cost of the logging program low, nuclear magnetic resonance (NMR) LWD was used as a sourceless standalone measurement, primarily to estimate the porosity and to detect permeable intervals in the Jinzhouoilfield. This paper presents the potential and limitations of NMR-LWD in a challenging drilling environment. The resistivity of the drilling fluid was often below 0.02 ohm·m and the mud was often more than 30 K colder than the formation. These two conditions can lead to an underestimated porosity, if the NMR data is not properly corrected. Achieving rapid drilling progress was an additional goal. One borehole was drilled with a rate of penetrations (ROP) between 30 m/h and 50 m/h for analyzing the effect on data quality. The processed NMR data shows that the high ROP has no detrimental impact on the accuracy of NMR porosity results, but, as expected, the vertical resolution is directly related to the ROP. By applying the temperature and resistivity corrections, the standalone NMR-LWD measurement compares well with offset porosity based on density-neutron logging and provides reliable data for the total porosity and for all other formation evaluation applications based on NMR data. In Bohai Bay the corrected NMR data have been successfully used to accurately detect in real-time permeable intervals bearing light oil in a complex high-porosity shaly-sandstone multilayer reservoir as well as gas intervals. In addition, the NMR data have been used to quantify bound water and movable fluids and to predict the reservoir effectiveness and productivity. |
Databáze: |
OpenAIRE |
Externí odkaz: |
|