Mapping the Barnett Shale Gas With Probabilistic Physics-Based Decline Curve Models and the Development of a Localized Prior Distribution
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Copyright 2018, Unconventional Resources Technology Conference (URTeC). Decline curves are the simplest type of model to forecast production from oil and gas reservoirs. Based on a selected decline model and observed production data, a trend is projected to predict future well performance and reserves. Despite capturing general trends, these models are not sufficient at describing the underlying physics of complex multiphase porous media flow phenomena and at explaining variations in production due to changes in operational conditions. The application of these models within a Bayesian framework is a feasible alternative to mitigate this issue and obtain more robust forecasts by considering a range of possible results. However, one important aspect that conditions the production forecasts and their uncertainty is the design of a suitable prior distribution, which can be subjective.To address the aforementioned issue, this paper presents a workflow for the development of a localized prior distribution for new wells drilled in shale formations which combines production data from preexisting surrounding wells and geospatial data, specifically well surface and bottom coordinates. This workflow aims to establish engineering criteria to reduce the subjectivity in the design of a prior distribution, reducing and reliably quantifying the uncertainty while assuming spatial continuity of decline curve parameters. A case study of 898 gas wells in the Barnett shale is presented, and several maps are generated for analysis of important properties to be considered during field development.
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Proceedings of the 6th Unconventional Resources Technology Conference
