# Unconventional Wells Interference: Supervised Machine Learning for Detecting Fracture Hits
## Abbreviations
Model1 \"model_1\" Layer (type) Output Shape Param # Connected to ================================================================ input_1 (InputLayer) [(None, 1)] 0 repeat_vector (RepeatVector) (None, 1000, 1) 0 input_1[0][0] input_2 (InputLayer) [(None, 1000, 13)] 0 concatenate (Concatenate) (None, 1000, 14) 0 repeat_vector[0][0] input_2[0][0]  
lstm (L5 TH) (None, 1000, 32) 6016 concatenate[0][0]  
batch_normalization (BatchNorm (None, 1000, 32) 120 1 stm[0][0] time_distributed (TimoDistribt (None, 1000, 64) 2112 batch_normalization[0][0] batch_normalization_1 (BatchNorm (None, 1000, 64) 256 time_distributed[0][0][0] time_distributed_1 (TimeoDistribt (None, 1000, 128) 8320 batch_normalization_1[0][0] batch_normalization_2 (BatchNorm (None, 1000, 128) 512 time_distributed_1[0][0] time_distributed_2 (TimoDistrib (None, 1000, 512) 66,048 batch_normalization_2[0][0] batch_normalization_3 (BatchNorm (None, 1000, 512) 2048 time_distributed_2[0][0] time_distributed_3 (TimeoDistribt (None, 1000, 256) 131,328 batch_normalization_3[0][0] batch_normalization_4 (BatchNorm (None, 1000, 256) 1024 time_distributed_3[0][0] time_distributed_4 (TimeoDistribt (None, 1000, 64) 16,448 batch_normalization_4[0][0] batch_normalization_5 (BatchNorm (None, 1000, 64) 256 time_distributed_4[0][0] time_distributed_5 (TimoDistrib (None, 1000, 16) 1040 batch_normalization_5[0][0] batch_normalization_6 (BatchNorm (None, 1000, 16) 64 time_distributed_5[0][0] time_distributed_6 (TimeoDistrib (None, 1000, 1) 17 batch_normalization_6[0][0] total_params: 235,617 trainable params: 233,473  
Figure A3: Parameter details for the model in Figure A2. * [PERSON] and [PERSON] (2019) [PERSON]; [PERSON] Horizontal Well Frac-Driven Interactions: Types, Consequences, and Damage Mitigation. _J. Pet. Technol._**2019**, 71, 45-47. [CrossRef]
* [PERSON] et al. (2017) [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON] Impact of Well Spacing and Interference on Production Performance in Unconventional Reservoirs, Permian Basin. In Proceedings of the SPE/AAPG/SEG Unconventional Resources Technology Conference, Austin, TX, USA, 24-26 July 2017. [CrossRef]
* [PERSON] et al. (2018) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Production Performance of Infill Horizontal Wells vs. Pre-Existing Wells in the Major US Unconventional Basins. In Proceedings of the SPE Hydraulic Fracturing Technology Conference and Exhibition, The Woodlands, TX, USA, 23-25 January 2018. [CrossRef]
* [PERSON] (2022) [PERSON] _Shale Analytics. Final Report for MLASS DOE-NETL Project_; Technical Report; West Virginia University: Morgantown, WV, USA, 2022. * [PERSON] et al. (2018) [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON] Investigation of Production-Induced Stress Changes for Infill-Well Stimulation in Eagle Ford Shale. _SPE J._**2018**, 23, 1372-1388. [CrossRef]
* [PERSON] et al. (2021) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] The Influence of Development Target Depletion on Stress Evolution and Infill Drilling of Upside Target in the Permian Basin. _SPE Resero. Eval. Eng._**2021**, 24, 570-589. [CrossRef]
* [PERSON] and [PERSON] (2023) [PERSON]; [PERSON] Case Study: Frac-Hit Occurrence Prediction Using AI & ML. _Progress Petrochem. Sci._**2023**, 5, 548-556. [CrossRef]
* [PERSON] et al. (2021) [PERSON]; [PERSON]; [PERSON]; [PERSON] Development of machine learning models for full field Reservoir Characterization. Oral Presentation at the AGU Fall Meeting, New Orleans, LA, USA, 13-17 December 2021. Available online: [[https://www.osti.gov/servlets/purl/1846178](https://www.osti.gov/servlets/purl/1846178)]([https://www.osti.gov/servlets/purl/1846178](https://www.osti.gov/servlets/purl/1846178)) (accessed on 25 February 2023). **Disclaimer/Publisher's Note:** The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s).