Integrating Core Data and Wireline Geochemical Data for Formation Evaluation and Characterization of Shale Gas Reservoirs

Abstract

AbstractWhen core data, such as mineralogy, porosity, total organic carbon, and elastic properties, are available from a large multiwell database, it is possible to extract meaningful statistical relationships between the data. These relationships can then be applied in other wells with limited or no core data. X-ray diffraction (XRD) mineralogical data is unique in that it can readily be transformed to synthetic wireline geochemical elemental data, such as the weight fractions, aluminum, silicon, and calcium. This transformation establishes a relationship between the mineralogical data and the synthetic wireline geochemical data, which can be applied to "real wireline" geochemical data to select an appropriate set of minerals for prediction from the wireline geochemical data. Statistical optimization based approaches are generally used to perform this prediction of mineralogy from wireline data.The most difficult aspect of predicting mineralogy from wireline geochemical data is the selection of the proper mineral model. The proposed approach assists the analyst in the selection of the proper model. In addition, this paper shows how core XRD mineralogical data can be used to establish constraints between the predicted mineralogy, such as percent clay fraction illite greater than 60%. An accurate prediction of mineralogy from wireline geochemical data can be transformed to an accurate prediction of grain density, enabling an accurate prediction of total porosity (corrected for kerogen effects).The workflow described in this paper provides an effective approach for establishing and predicting mineralogy, grain density, and porosity from wireline geochemical data. This paper presents results for a Haynesville shale example. Finally, the proposed approach helps to maximize the value of the core data for shale gas plays.