Energy Geoscience (Oct 2021)
Advances in sediment geochemistry and chemostratigraphy for reservoir characterization
Abstract
Sedimentary sequences preserve the records of changes in major controls of sedimentation namely, tectonics, climate, relative sea level and sediment production and preservation. The potential to characterize these changes in spatial and temporal scales has led to the development of the branch of chemostratigraphy. Chemostratigraphic study of sedimentary sequences commenced from recognizing identical/contrasting geochemical features across major geochronological boundaries, and evolved into one of the essential tools in exploration, characterization, and well development strategies. Chemostratigraphy incorporates applications on continuous, real-time geochemical mapping and direction of lateral drilling, and machine learning, among others. As the sedimentary systems operate on a variety of temporal scales that range from few hours (tidal cycles) to few tens of millions of years, within which many perturbations such as catastrophic and diagenetic events take place, that lead to unique geochemical signature which can be correlated at appropriate spatial and temporal scales. The application of chemostratigraphic technique in hydrocarbon exploration and reservoir characterization has gained momentum in recent years, particularly with the advent of developments in analytical instrumentation. This has also led to the integration of a variety of data from field sedimentary structures, mineralogy, major, trace and isotopic chemical compositions of whole rock, selected components of rocks, organic and inorganic components of oil and gas, etc., for reservoir characterization more accurately than ever. The geochemical fingerprinting of oil and gas reservoir components plays a major role in the identification of source rocks, discrimination of oil families, characterization of reservoir, source, and seal segments in petroleum systems. Future trends indicate the relevance and growing applications of machine learning techniques, artificial intelligence in real-time assessment, monitoring and planning of hydrocarbon exploration and production.
