Abstract

Unconventional field development and well performance analysis encompass multiple disciplines and large data sets. Even when seismic and other data sets are not available, geologists can build geocellular models to determine factors that improve operational efficiency by incorporating well log, geosteering, stratigraphic, structural, completion, and production data. I have developed a methodology to integrate these data sets from vertical and horizontal wells to build a sequence stratigraphic and structurally framed geocellular model for an unconventional Marcellus Formation field in the Appalachian Basin, USA. The model would benefit from additional data sets to perform a rigorous investigation of performance drivers. However, the presented methodology emphasizes the value of constructing geocellular models for fields with sparse data by building a geologically detailed model in a field area without seismic and core data. I used third-order stratigraphic sequences interpreted from vertical wells and geosteering data to define model layers and then incorporate completion treating pressures and proppant delivered per stage into the model. These data were upscaled and geostatistically distributed throughout the model to visualize completion trends. Based on these results, I conclude that geologic structure and treating pressures coincide, as treating pressures increase with stage proximity to a left-lateral strike-slip fault, and completion trends vary among third-order systems tracts. Mapped completion issues are further emphasized by areas with higher model proppant values, and all treating pressure and proppant realizations for each systems tract have the greatest variance away from data points. Similar models can be built to further understand any global unconventional play, even when data are sparse, and, by doing so, geologists and engineers can (1) predict completion trends based on geology, (2) optimize efficiency in the planning and operational phases of field development, and (3) foster supportive relationships within integrated subsurface teams.

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