products
Applications
wirepedia — An encyclopedia built by eavesdropping on humans
Libraries
niacin — Data enrichment and negative sampling
george-lucas — watch "A New Hope" in your terminal
tram — threadsafe objects in Pure Python
Data
MODIS (2km) Cloudless Composite — Cloudless global surface reflectance from 2017
https://doi.org/10.5281/zenodo.1287839
Primate Energetics — Daily energy use of 26 genera of primates
https://doi.org/10.5281/zenodo.33599
Patents
D. Niederhut, D. Szafranski, and T. Gilbertson. Inference Models for Well Production with Limited Training Data, Utility patent submitted April 2024. US20240263555A1
J. Ramey, D. Niederhut, K. Crifasi, and K. Darnell. Predictive modeling of well performance using learning and time-series techniques, Utility patent submitted July 2021. US20220083873A1
Articles
A. Toure, K. Sathaye, and D. Niederhut. Assessing the Impact of Prior Depletion on Future Inventory in the Delaware Basin. SPE/AAPG/SEG Unconventional Resources Technology Conference, 2026.
B. Davis, A. Qualls, D. Niederhut, and K. Sathaye. Designing Depletion Features for Parent-Child Modeling: Comparing Time, Distance, and Volume-Distance Formulations in Machine-Learning Workflows. SPE/AAPG/SEG Unconventional Resources Technology Conference, 2026.
D. Niederhut and G. Quintero. Can Transfer Learning be Used to Forecast Production in Frontier Basins? A Case Study from the Powder River Basin. SPE/AAPG/SEG Unconventional Resources Technology Conference, 2025. https://doi.org/10.15530/urtec-2025-4264880
K. Sathaye, D. Niederhut, and A. Cui. Describing Well Spacing: Dimensionality at Work. SPE/AAPG/SEG Unconventional Resources Technology Conference, 2025. https://doi.org/10.15530/urtec-2025-4264080
B. Davis and D. Niederhut. Cross-Basin Analysis of Production Drivers: Insight from Machine Learning Applied to 100,000 Unconventional Wells. SPE/AAPG/SEG Unconventional Resources Technology Conference, 2025. https://doi.org/10.15530/urtec-2025-4214380
K. Sathaye and D. Niederhut. When Numbers Lie: The Need for Causal Modeling in Well Forecasting. SPE/AAPG/SEG Unconventional Resources Technology Conference, 2025. https://doi.org/10.15530/urtec-2025-4264381
S. Flem, G. Berns, B. Inglis, D. Niederhut, E. Montie, T. Deacon, K. Miller, P. Tyack, and P. Cook. Lateralized cerebellar connectivity differentiates auditory pathways in echolocating and non-echolocating whales. PLOS One, 2025. https://doi.org/10.1371/journal.pone.0323617
A. Cui, D. Niederhut, and B. Davis. Forecasting Production Loss for Delayed Secondary Bench Development in the Midland Basin. SPE/AAPG/SEG Unconventional Resources Technology Conference, 2024. https://doi.org/10.15530/urtec-2024-4037004
D. Niederhut and A. Cui. Understanding the Drivers of Parent-Child Depletion: A Machine Learning Approach. SPE/AAPG/SEG Unconventional Resources Technology Conference, 2023. https://doi.org/10.15530/urtec-2023-3862321
A. Cui, T. Gilbertson, and D. Niederhut. Revealing the Production Drivers for Refracs in the Williston Basin. SPE/AAPG/SEG Unconventional Resources Technology Conference, 2023. https://doi.org/10.15530/urtec-2023-3864951
T. Cross, K. Long, D. Niederhut, and A. Cui. How Does the Impact of Completions Change Over the Life of a Well? A Comparison Across the Major Us Unconventional Plays Using Machine Learning. SPE/AAPG/SEG Unconventional Resources Technology Conference, 2022. https://doi.org/10.15530/urtec-2022-3723930
D. Niederhut, A. Cui, C. Macalla, and J Reed. Understanding the spacing, completions, and geological influences on decline rates and B values. SPE/AAPG/SEG Unconventional Resources Technology Conference, 2022. https://doi.org/10.15530/urtec-2022-3723711
I. Gupta, O. Samandarli, A. Burks, V. Jayaram, D. McMaster, D. Niederhut, and T. Cross. Autoregressive and Machine Learning Driven Production Forecasting - Midland Basin Case Study. American Association of Petroleum Geologists, 2021.
T. Cross, D. Niederhut, A. Cui, K. Sathaye, and J. Chaplin. 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. American Association of Petroleum Geologists, 2021.
K. Sathaye, T. Cross, K. Darnell, J. Reed, J. Ramey, and D. Niederhut. The Impact of Spacing and Time on Gas/Oil Ratio in the Permian Basin: A Multi-Target Machine Learning Approach. 2020.
K. Sathaye, T. Cross, K. Darnell, J. Reed, J. Ramey, and D. Niederhut. The Impact of Interwell Spacing Over Time A Machine Learning Approach. 2020.
D. Niederhut. niacin: A python package for text data enrichment. Journal of Open Source Software, 5(50):2136, 2020. https://doi.org/10.21105/joss.02136
code available here.
K. N. Darnell, K. Crifasi, G. Stotts, D. Tsang, V. Lavoie, T. Cross, D. Niederhut, A. Ramey, and K. Sathaye. Decomposition of Publicly Reported Combined Hydrocarbon Streams Using Machine Learning in the Montney and Duvernay. 2020.
T. Cross, K. Sathaye, K. Darnell, J. Ramey, K. Crifasi, and D. Niederhut. GeoSHAP: A Novel Method of Deriving Rock Quality Index from Machine Learning Models and Principal Components Analysis. 2020.
T. Cross, K. Sathaye, K. Darnell, D. Niederhut, and K. Crifasi. Predicting Water Production in the Williston Basin Using a Machine Learning Model. 2020.
T. Cross, K. Sathaye, K. Darnell, D. Niederhut, and K. Crifasi. Benchmarking Operator Performance in the Williston Basin using a Predictive Machine Learning Model. 2020.
T. Cross, J. Reed, K. Sathaye, K. Darnell, K. Crifasi, and D. Niederhut. Evaluating the Impact of Precision Targeting on Production in the Midland Basin Using Machine Learning Algorithms. 2020.
T. Cross, D. Niederhut, K. Sathaye, K. Darnell, and K. Crifasi. Deriving Time-Dependent Scaling Factors for Completions Parameters in the Williston Basin using a Multi-Target Machine Learning Model and Shapley Values. 2020.
D. Niederhut. Safe handling instructions for missing data. In F. Akici, D. Lippa, D. Niederhut, and M. Pacer, editors, Proceedings of the 17th Python in Science Conference, pages 56–50, 2018. https://doi.org/10.25080/Majora-4af1f417-008.
code and data available here.
D. Niederhut. Semantic bleaching not observed in synchronic test. In C. Cuskley et al., editors, The Evolution of Language: Proceedings of the 12th International Conference. NCU Press, 2018. https://doi.org/10.12775/3991-1.082.
code and data available here.
D. Niederhut. Software transactional memory in pure python. In K. Huff, D. Lippa, D. Niederhut, and M. Pacer, editors, Proceedings of the 16th Python in Science Conference, pages 9–11, 2017. https://doi.org/10.25080/shinma-7f4c6e7-002
code available here.
D. Niederhut. Quantifying the semantic value of words. In S. Roberts et al., editors, The Evolution of Language: Proceedings of the 11th international conference. World Scientific, Hackensack, 2016.
code and data available here.
D. Niederhut. The phonatory culture hypothesis. In E. Cartmill et al., editors, The Evolution of Language: Proceedings of the 10th international conference. World Scientific, Hackensack, 2014.
D. Niederhut. Beyond "neuroevidence". In L. McCrohon et al., editors, The Past, Present, and Future of Evolution of Language Research. World Scientific, Hackensack, 2014.
D. Niederhut. Gesture and the origin of language. In T. Scott-Phillips et al., editors, The Evolution of Language: Proceedings of the 9th international conference. World Scientific, Hackensack, 2012.
Edited Volumes
M. Agarwal, C. Calloway, and D. Niederhut, editors. Proceedings of the 22nd Python in Science Conference, 2023. https://doi.org/10.25080/gerudo-f2bc6f59-039
M. Agarwal, C. Calloway, D. Niederhut, and D. Shupe, editors. Proceedings of the 21st Python in Science Conference, 2022. https://doi.org/10.25080/majora-212e5952-046
M. Agarwal, C. Calloway, D. Niederhut, and D. Shupe, editors. Proceedings of the 20th Python in Science Conference, 2021. https://doi.org/10.25080/majora-1b6fd038-02b
M. Agarwal, C. Calloway, D. Niederhut, and D. Shupe, editors. Proceedings of the 19th Python in Science Conference, 2020. https://doi.org/10.25080/Majora-342d178e-02b
C. Calloway, D. Lippa, D. Niederhut, and D. Shupe, editors. Proceedings of the 18th Python in Science Conference, 2019. https://doi.org/10.25080/Majora-7ddc1dd1-026
F. Akici, D. Lippa, D. Niederhut, and M. Pacer, editors. Proceedings of the 17th Python in Science Conference, 2018. https://doi.org/10.25080/Majora-4af1f417-018.
K. Huff, D. Lippa, D. Niederhut, and M. Pacer, editors. Proceedings of the 16th Python in Science Conference, 2017. https://doi.org/10.25080/shinma-7f4c6e7-000.