Data Management and Quality Control of Dipmeter and Borehole Image Log Data
Carmen García-Carballido, Jeannette Boon, Nancy Tso, 2010. "Data Management and Quality Control of Dipmeter and Borehole Image Log Data", Dipmeter and Borehole Image Log Technology, M. Pöppelreiter, C. García-Carballido, M. Kraaijveld
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Numerous dipmeter and borehole image log data sets have been acquired over the years and are being used to build subsurface models. Dealing with dipmeter and image log data remains a niche skill within the petroleum industry, and because these are not conventional log data sets, they tend to be neglected in the way data are stored and quality controlled. A variety of wireline and logging-while-drilling tools exist, and each logging run contains a variety of curves with tool-specific mnemonics. For a particular data set, there may be several tens of curves from the raw data set and hundreds from the processed and interpreted data sets. Data quality control (QC) is an essential procedure that has to be conducted to assure dipmeter and image log data integrity in the subsurface models. Data QC should be performed iteratively during data acquisition, data management, processing, and interpretation. This chapter presents standard and globally applicable corporate guidelines for data management and data QC of dipmeter and image log data sets.
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Borehole imaging is among the fastest and most accurate methods for collecting high resolution subsurface data. Recent breakthroughs in acquisition, tool design, and modeling software provide real-time subsurface images of incredible detail, from the drill bit straight to a workstation. Associated interpretation workflows offer the high level of detail that is needed to make operational decision and to increase the predictability of subsurface models. Many exploration and production companies have acquired a wealth of dipmeter and image log data. The data are readily available and provide, for example the orientation of fractures and fluvial channels in space. Further applications of borehole imaging technology include matrix and fracture characterization, pore-type partitioning, geosteering, and in-situ stress determination. Exciting new applications are found in enhanced oil recovery, carbon dioxide sequestration, and geothermal projects. In addition, borehole image data are paramount to unlocking unconventional plays such as shale gas and coal-bed methane. AAPG Memoir 92 portrays key applications of dipmeter and image log data across the exploration and production life cycle. (Continued)