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Unconventional reservoirs require advanced technologies such as horizontal well placement and hydraulic fracturing to be successfully exploited at economic rates. In this context, static and dynamic reservoir quality (RQ) concepts are introduced. Static RQ or standard RQ comprises a set of petrophysical parameters that describe formation tendency for development. Dynamic RQ or completion quality is defined by a set of geomechanical parameters that estimate formation tendency to be fractured. The convergence of static and dynamic RQs allows for evaluating the production potential of a field; particularly, productive sweet spots are located in those intervals in which good static and dynamic RQs are detected. We have developed a workflow to identify producible intervals in unconventional reservoirs by means of lithologic and geomechanical facies classification. Starting from core data, a clustering technique is used to create a set of lithologic facies that are then extended to the logged interval and characterized in terms of static RQ. The same approach is used to classify the logged interval with a set of geomechanical facies in which dynamic RQ is estimated. The integration of lithologic and geomechanical facies leads to sweet spot identification. Workflow application to available data from the Barnett Shale Formation allows us to classify the logged interval with four log facies (LF) and five geomechanical facies (GF) and to identify productive sweet spots in the upper and middle Lower Barnett. Eventually, LF and GF are linked to seismic facies probability volumes and Young’s modulus from elastic inversion of surface seismic. Seismic-driven geostatistical realization of LF and GF provides static and dynamic RQs volumes that are combined into volumes of productive and nonproductive facies.
Unconventional reservoirs require advanced technologies such as horizontal well placement and hydraulic fracturing to be successfully exploited at economic rates. In this context, static and dynamic reservoir quality (RQ) concepts are introduced. Static RQ or standard RQ comprises a set of petrophysical parameters that describe formation tendency for development. Dynamic RQ or completion quality is defined by a set of geomechanical parameters that estimate formation tendency to be fractured. The convergence of static and dynamic RQs allows for evaluating the production potential of a field; particularly, productive sweet spots are located in those intervals in which good static and dynamic RQs are detected. We have developed a workflow to identify producible intervals in unconventional reservoirs by means of lithologic and geomechanical facies classification. Starting from core data, a clustering technique is used to create a set of lithologic facies that are then extended to the logged interval and characterized in terms of static RQ. The same approach is used to classify the logged interval with a set of geomechanical facies in which dynamic RQ is estimated. The integration of lithologic and geomechanical facies leads to sweet spot identification. Workflow application to available data from the Barnett Shale Formation allows us to classify the logged interval with four log facies (LF) and five geomechanical facies (GF) and to identify productive sweet spots in the upper and middle Lower Barnett. Eventually, LF and GF are linked to seismic facies probability volumes and Young’s modulus from elastic inversion of surface seismic. Seismic-driven geostatistical realization of LF and GF provides static and dynamic RQs volumes that are combined into volumes of productive and nonproductive facies.
Shale gas reservoir quality evaluation is a technical challenge as conventional log interpretation methods and even core measurements may be inadequate or inaccurate. Only through a robust integration of core and logs data, a reliable estimation of the gas in place can be obtained and the interval with the best petrophysical and geomechanical characteristics (sweet spot) identified. Total GIIP estimation is derived from core and log analyses and can be complemented by measuring desorbed gas from selected core samples. Total gas content is the combination of measured and estimated gas components: lost gas, desorbed gas and residual gas. Lost gas is the most critical component being usually extrapolated from desorbed data to time zero using linear and/or polynomial curve fitting. Total gas can now be directly measured by using a new controlled pressure coring technology able to capture a full sized core and retain all the hydrocarbons contained in the rock, eliminating gasses and/or liquids lost during conventional coring techniques. The characterization of the lower Barnett Shale described in this paper was achieved by coring the whole sequence: several samples from conventional cores were taken for desorption analysis and three pressure cores were placed to target the main shale facies identified from the integrated reservoir model. Core gas data from both desorption and pressure coring were integrated for a more reliable total gas estimation; consistent relationships among handling time, gas content and method used for deriving lost gas were also observed. Obtained total gas values were in good agreement with calculated gas volumes from logs. In addition, the combined application of pressure coring and desorption analysis in the Barnett Shale has allowed to improve well site procedures for optimal core data acquisition and to define the best approach for a robust shale gas evaluation.
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