Abstract: Systems and methods for generating digital gamma-ray logs for target wells based on combined physics and machine learning model using real-time information (e.g., drilling parameters, survey data, gamma-ray logs, and so forth) obtained from offset wells analogous to the subject well in terms of gamma-ray readings. The systems and methods may provide solutions that may lower the cost of Measuring While Drilling (MWD) and/or Logging While Drilling (LWD) process and facilitate the users (e.g., drillers, geoscientists, and so forth) to make enhanced data driven decisions.

Abstract: An apparatus and method utilize a trained machine learning model to synthesize formation evaluation data such as formation tops and LWD logs. In some instances, the synthesis of formation evaluation data may further be based upon drilling mechanics data collected during drilling, thus effectively enabling formation evaluation data to be synthesized primarily based upon surface measurements collected in real time, and in many cases without the need for collecting downhole measurements during drilling. In addition, in some instances, a machine learning model implemented as a generative adversarial network (GAN) may be used to synthesize formation evaluation data, with drilling mechanics data collected during drilling also used in some instances.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media for dynamically utilizing offset drill-well data generated within a threshold geographic area to determine formation-top trends and identify formation-top depths at a subject drill-well site. To do so, in some embodiments, the disclosed systems estimate a variogram for observed formation-top depths of a subset of offset drill-wells, and, in turn, map a predicted response from the estimated variogram. For example, using weighted combinations (e.g., with Kriging weights) of the formation-top depths of the subset of offset drill-wells, the disclosed systems can map a continuous surface of a formation and identify a top-depth thereof. Moreover, the disclosed system can do so for multiple formations at the subject drill-well site, and (in real-time in response to a user input) provide for display at a client device, the associated formation-top depths, various predicted drilling events and/or predicted drilling metrics.