Cores, thin sections, and cathodoluminescence analysis were integrated to document the occurrence and petrology of dolomites, and their pore systems in the Cambrian of Tarim Basin, China. Depositional facies, pore types, and dolomitization processes of various dolostone reservoir types are determined. Six types of dolomite are recognized, including microbial dolomite, dolomicrite, fabric-retentive dolomite, fabric-obliterative dolomite, fine to medium crystalline dolomite cement, and saddle dolomite cement. Pore systems are dominantly vugs, anhydrite dissolution pores, intercrystalline pores, intercrystalline dissolution pores, fabric dissolution pores, and microfractures. Four porous dolostone reservoirs include sabkha dolostone, seepage-reflux dolostone, burial dolostone, and hydrothermal dolostone. Fractures are an important factor in enhancing reservoir quality in dolostone reservoirs.
Conventional wire-line logs and image logs are calibrated with cores and related thin sections. Sabkha dolostone reservoirs are characterized by dark and bright spots on the image logs. Seepage-reflux dolostone reservoirs are related to high-energy depositional facies and are characterized by low gamma-ray amplitude, increasing sonic transit time and neutron porosity but reducing bulk density values. Evident dark spots (vugs) are recognized on image logs, and all three porosity logs suggest relatively high reservoir quality in burial dolostone reservoirs. Hydrothermal dolostone reservoirs are recognized by high gamma-ray response caused by hydrothermal minerals (fluorite), and porosity curves indicate good reservoir quality, which is supported by dark spots (vugs) on the image logs. Rapid decrease in resistivity, increasing in sonic transit time values, and the dark sinusoidal waves on the image logs are typical of fractured dolostone reservoirs. The distribution of dolostone reservoirs in each well is predicted using a comprehensive analysis of conventional and image logs, and they are calibrated with oil test data. The research provides insights in the analysis of genetic model of deeply buried dolostone reservoirs and establishes the predictable model for reservoir quality in dolostones via well logs.