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Production Trend Analytics: Utilizing Big Data to Minimize Geoscientist Capital Input

Abstract

Identifying trends in horizontal targets can predict the economic limits of a field as well as help identify potential prospects. In the evaluation of acreage, it is often important to make decisions in short time periods and with data from various sources. This study utilizes multiple large, publically available datasets to identify production data from within a zone of interest without evaluating logs on a well by well basis. This “quick look” technique can be utilized to identify trends while minimizing geoscientist capital investment. The Pronghorn Member of the Bakken Formation was previously assigned to the underlying Three Forks Formation and has a history of inconsistent nomenclature. To separate wells producing from the Pronghorn top picks were used to delineate the surfaces of the Pronghorn and Three Forks. These surfaces were converted to 3D point clouds and adjusted to account for the expected effective height of fracture treatment. The adjusted Pronghorn and Three Forks 3D point clouds provide the upper and lower cutoffs of the zone to be evaluated. Well deviation survey points were compared to the 3D point cloud with a nearest neighbor search to evaluate if they fell within the zone of interest. If sufficient deviation survey points were identified to be within the 3D point cloud, the lateral was confirmed to have landed in the zone of interest. Production data was normalized for lateral length then assigned to the wells landing in the zone of interest. Production trends for the Pronghorn could then be evaluated. This technique can be applied to other horizontal targets in large petroleum systems to evaluate production trends in a time efficient manner providing a competitive edge in evaluating acreage. The decrease in geoscientist capital needed to separate production data for a horizontal target, provided by this technique, can also lead to more profitable operations while freeing staff to perform other tasks.