# Unconventional Wells Interference: Supervised Machine Learning for Detecting Fracture Hits
## 4 Conclusions  
The case study reported here utilized data records for three out of the ninety-six pads available in the industrial partner's oil field. The goal was to develop an effective ML-based workflow and illustrate that a frac-hit event can be probabilistically recognized by pre-trained ML models. The predictions of the trained ML models agreed with the observations at the neighboring wells. The workflow was designed to recognize two types of frac hits, intra-pad and inter-pad frac hits. The first type of frac hits, corresponding to intra-pad well interference, is mainly due to relative offset between batches of fracking and production intervals, or the \"batch\" effect. There were two batches observed at Pad 137. The high frac-hit probability instances, with impacts on the early-batch production wells, were attributed to the late-batch fracking wells within the same pad. Inter-pad frac hits were also identified here, with interferences between Pads 133, 137, and 138. The methodology developed in this study provided a robust approach to tracking down frac hits based on pressure and production data. The innovative workflow produced a valuable tool for frac-hit diagnostics. Ultimately, the frac-hit event detection and diagnostics can make it possible to find optimal solutions for well completion design, spacing and infill, production management, and wellbore protection. The input data can be continuously streamed from the field into a ML-based workflow to enable the process to provide rapid insights into the well operations and support real-time decision-making such as drilling operations planning, shut-in control, and stimulation management.  
Conceptualization, V.R.; methodology, G.L.; software, X.W.; validation, X.W., G.L. and V.R.; formal analysis, X.W.; investigation, X.W.; resources, V.R.; data curation, X.W.; writing--original draft preparation, G.L. and V.R.; writing--review and editing, V.R.; visualization, X.W. and V.R.; supervision, V.R.; project administration, V.R.; funding acquisition, V.R. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Restrictions apply to the availability of these data. Data were obtained from our industrial partner (a major shale-field operator in the Permian Basin, located in West Texas, United States) and are available at the NETL EDX with the permission of the industrial partner.  
The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.  
This report was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor any agency thereof, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof.