Advanced Mud Gas Logging in Combination With Wireline Formation Testing and Geochemical Fingerprinting for an Improved Understanding of Reservoir Architecture

摘要

Understanding reservoir architecture is one of the critical factors in successful appraisal and development of any oil/gas field. Increasing drilling costs and environmental challenges has led to the development of alternative appraisal approaches that compliment the more costly and traditional methods such as the drill stem or production test. Therefore, the challenge of obtaining sufficient reservoir and fluid data from complex wells, in short time frames and at manageable costs, has pushed game changing improvements to both mud logging and wire line testing technologies. In the past, interpretation of wireline formation testing data (e.g., static pressure gradient analysis and downhole fluid analyses), PVT and geochemical fingerprinting have provided major inputs for reservoir modeling related to reservoir continuity. However, none of these tools or data sets provide hydrocarbon distributions or fingerprints during the drilling process of a given well; only after the well has reached final depth and LWD and e-logs have been obtained can one begin to speculate on such matters as reservoir continuity. Until recently, standard mud gas logging technologies did not offer the resolution and sensitivity necessary to provide quantitative hydrocarbon description. However, recent developments in advanced mud gas extractors and improved analytical detectors have increased the sensitivity, reliability and accuracy of mud gas fingerprinting. Thus, during the drilling of a given well, mud gas fluid fingerprints, or fluid facies, can be correlated within a given well and mapped to subsurface structural features such as faults and seals or different hydrocarbon bearing intervals. By providing this during the drilling process, anomalies observed in these fluid facies logs can be investigated in more detail during formation evaluation, sample collection, and geochemical fingerprinting techniques. In addition, by systematically combining and integrating these fundamentally different data streams, a much more robust picture of the reservoir fluid distribution becomes clear.