Susan L. Herron

New Method for Determining Mineralogy and Matrix Properties From Elemental Chemistry Measured by Gamma Ray Spectroscopy Logging Tools

Methods for predicting mineralogy from logging tool measurements have been an active area of research for several decades. In spite of these efforts, methods for predicting quantitative mineralogy including clay types from well logging data were not fully achieved. The introduction of geochemical logging tools in the 1980s offered promise; however, early versions of geochemical logging tools did not measure elemental chemistry with enough accuracy and precision to enable reliable and quantitative determination of mineralogy. Recent advances in geochemical logging tool technology now enable accurate and robust measurements of the chemical elemental concentrations that are needed to determine continuous quantitative and detailed logs of mineralogy. This paper presents a novel approach for determining more accurate, detailed mineralogy from an elemental spectroscopy logging tool. This work was made possible by three recent developments: the introduction of a new high-performance neutron-induced gamma ray spectroscopy logging tool, a new high-quality research database consisting of chemistry and mineralogy measured on cores acquired worldwide from conventional and unconventional reservoirs, and a new model-independent inversion method that overcomes the limitations of previous model-dependent methods. The model-independent inversion makes use of the database which includes clean sands, shaly sands, shales, carbonates, and complex mixed lithologies. The database contains laboratory measurements of dry-weight elemental chemistry and mineralogy measured by transmission Fourier transform infrared spectroscopy. The database is used to derive a model-independent mapping function that accurately represents the complex functional relationship between the elemental concentrations and the mineral concentrations. Once the mapping function is determined from the database, it can be used to predict quantitative mineralogy from elemental concentrations derived from the logging tool measurements. Unlike previous inversion methods, the model-independent mapping function does not have any adjustable parameters or require any user inputs such as mineral properties or endpoints. The mapping function is used to predict continuous logs of matrix densities plus concentrations of 14 minerals (i.e., illite, smectite, kaolinite, chlorite, quartz, calcite, dolomite, ankerite, plagioclase, orthoclase, mica, pyrite, siderite, and anhydrite) from eight dry-weight elemental concentrations derived from the logging tool. The new method has been applied to well log data acquired worldwide in numerous conventional and unconventional reservoirs having a wide variety of complex mineralogies. The predicted mineralogies and matrix densities are generally found to be in good quantitative agreement with core-derived mineralogies and matrix densities.

Past atmospheric environments revealed by polar ice core studies

ABSTRACT The polar ice sheets of Greenland and Antarctica are unique sedimentary environments which offer valuable information on the climate and atmospheric environment of the past. The prevailing low temperatures, lack of mixing of strata, and relatively high accumulation rates result in high quality records which may be resolved in great detail, frequently at the scale of seasons of the year. Deformation of ice at great depth reduces the resolution but extends the available time span to hundreds of thousands of years. Climatic and environmental information is obtained from ice cores penetrating the ice sheets and is derived from the composition of the ice itself, as well as from chemical impurities and entrapped air bubbles. Ice core signals can be related to climatic variables such as mean annual temperature, summer warmth, and past precipitation rates; past environmental effects that can be detected from ice core data include volcanic eruptions, changes in solar activity, and the impact of mans acti...