| Autoregressive and Machine Learning Driven Production Forecasting–Midland Basin Case Study I Gupta, O Samandarli, A Burks, V Jayaram, D McMaster, D Niederhut, ... Unconventional Resources Technology Conference, 26–28 July 2021, 3104-3117, 2021 | 17 | 2021 |
| GeoSHAP: A novel method of deriving rock quality index from machine learning models and principal components analysis T Cross, K Sathaye, K Darnell, J Ramey, K Crifasi, D Niederhut Unconventional Resources Technology Conference, 20–22 July 2020, 1056-1064, 2020 | 13 | 2020 |
| Predicting water production in the Williston basin using a machine learning model T Cross, K Sathaye, K Darnell, D Niederhut, K Crifasi Unconventional Resources Technology Conference, 20–22 July 2020, 3492-3503, 2020 | 10 | 2020 |
| Proceedings of the 18th Python in Science Conference S Hossain, C Calloway, D Lippa, D Niederhut, D Shupe SciPy, Austin, Texas, pp. 126–133,, 2019 | 6 | 2019 |
| Benchmarking operator performance in the Williston Basin using a predictive machine learning model T Cross, K Sathaye, K Darnell, D Niederhut, K Crifasi SPE/AAPG/SEG Unconventional Resources Technology Conference, D033S072R002, 2020 | 5 | 2020 |
| Deriving Time-Dependent Scaling Factors for Completions Parameters in the Williston Basin using a Multi-Target Machine Learning Model and Shapley Values T Cross, D Niederhut, K Sathaye, K Darnell, K Crifasi SPE/AAPG/SEG Unconventional Resources Technology Conference, D033S071R002, 2020 | 4 | 2020 |
| Gesture and the origins of language D Niederhut The Evolution Of Language, 266-273, 2012 | 4 | 2012 |
| Understanding the spacing, completions, and geological influences on decline rates and B values D Niederhut, A Cui, C Macalla, J Reed SPE/AAPG/SEG Unconventional Resources Technology Conference, D031S055R002, 2022 | 3 | 2022 |
| Predictive Modeling of Well Performance Using Learning and Time-Series Techniques J Ramey, DE Niederhut, KD Crifasi, KN Darnell US Patent App. 17/387,766, 2022 | 3 | 2022 |
| The impact of spacing and time on gas/oil ratio in the Permian Basin: A multi-target machine learning approach K Sathaye, T Cross, K Darnell, J Reed, J Ramey, D Niederhut Unconventional Resources Technology Conference, 20–22 July 2020, 914-916, 2020 | 3 | 2020 |
| niacin: A Python package for text data enrichment D Niederhut Journal of Open Source Software 5 (50), 2136, 2020 | 3 | 2020 |
| Monaco: A Monte Carlo Library for Performing Uncertainty and Sensitivity Analyses. WS Shambaugh, M Agarwal, C Calloway, D Niederhut, D Shupe SciPy, 244-250, 2022 | 2 | 2022 |
| Quantifying the Diminishing Impact of Completions Over Time Across the Bakken, Eagle Ford, and Wolfcamp Using a Multi-Target Machine Learning Model and SHAP Values T Cross, D Niederhut, A Cui, K Sathaye, J Chaplin SPE/AAPG/SEG Unconventional Resources Technology Conference, D021S048R003, 2021 | 2 | 2021 |
| The impact of interwell spacing over time—A machine learning approach K Sathaye, T Cross, K Darnell, J Reed, J Ramey, D Niederhut Unconventional Resources Technology Conference, 20–22 July 2020, 2962-2970, 2020 | 2 | 2020 |
| Software Transactional Memory in Pure Python. D Niederhut SciPy, 9-11, 2017 | 2 | 2017 |
| Beyond “neuroevidence” D Niederhut The past, present and future of language evolution research, Tokyo: EvoLang9 …, 2014 | 2 | 2014 |
| Emotion and the perception of biological motion D Niederhut | 2 | 2009 |
| Understanding the drivers of parent-child depletion: A machine learning approach D Niederhut, A Cui SPE/AAPG/SEG Unconventional Resources Technology Conference, D031S071R005, 2023 | 1 | 2023 |
| Decomposition of Publicly Reported Combined Hydrocarbon Streams Using Machine Learning in the Montney and Duvernay KN Darnell, K Crifasi, G Stotts, D Tsang, V Lavoie, T Cross, D Niederhut, ... SPE/AAPG/SEG Unconventional Resources Technology Conference, D033S084R002, 2020 | 1 | 2020 |
| Safe handling instructions for missing data. D Niederhut SciPy, 56-60, 2018 | 1 | 2018 |
