bluebonnet

Scaling solutions for production analysis from unconventional oil and gas wells.

Installation

Run the command

bash pip install bluebonnet

This package works for Python versions 3.8-3.11. Dependencies are automatically installed by pip. They include standards of the Python scientific stack, including lmfit, matplotlib, numpy, pandas, and scipy.

Usage

bluebonnet has a collection of tools for performing reservoir simulation in tight oil and shale gas reservoirs. The main tools are:

1. fluids for modeling PVT and viscosity of oil, water, and gas;
2. flow for building physics-based production curves; and
3. forecast for fitting and forecasting unconventional production.

Examples can be found in the documentation.

Contributing

Interested in contributing? Check out the contributing guidelines to get started. Please note that this project is released with a Code of Conduct. By contributing to this project, you agree to abide by its terms.

Contributor Hall of Fame

Michael Marder

License

bluebonnet was created by Frank Male. It is licensed under the terms of the BSD 3-Clause license.

Credits

This work was funded in part by an ExxonMobil grant to the University of Texas at Austin, with Michael Marder as PI and Larry Lake as co-PI. The Physics-based scaling curve was developed for shale gas reservoirs by Patzek et al. (2013). It was extended to tight oil by Male (2019). It was extended to two-phase by Ruiz Maraggi et al. (2020). It was extended to include variable fracture face pressure by Ruiz Maraggi et al. (2021). In the future, it might be extended further.
bluebonnet was created with cookiecutter and the py-pkgs-cookiecutter template.

Bibliography

Papers developing or using this approach include:

1. Patzek, T. W., Male, F. and Marder, M., 2013. "Gas production in the Barnett Shale obeys a simple scaling theory," Proceedings of the National Academy of Science. https://doi.org/10.1073/pnas.1313380110
2. Patzek, T. W., Male, F. and Marder, M., 2014. "A simple model of gas production from hydrofractured horizontal wells in shales," AAPG Bulletin v. 98, no. 12. https://doi.org/10.1306/03241412125
3. Male, F., Islam, A.W., Patzek, T.W., Ikonnikova, S.A., Browning, J.R., and Marder, M.P., 2015. "Analysis of gas production from hydraulically fractured wells in the Haynesville shale using scaling methods." Journal of Unconventional Oil and Gas Resources. https://doi.org/10.1016/j.juogr.2015.03.001
4. Male, F., 2015. Application of a one dimensional nonlinear model to flow in hydrofractured shale gas wells using scaling solutions (Doctoral dissertation). https://repositories.lib.utexas.edu/handle/2152/46706
5. Eftekhari, B., Marder, M. and Patzek, T.W., 2018. Field data provide estimates of effective permeability, fracture spacing, well drainage area and incremental production in gas shales. Journal of Natural Gas Science and Engineering, 56, pp.141-151. https://doi.org/10.1016/j.jngse.2018.05.027
6. Male, F. 2019, "Assessing impact of uncertainties in decline curve analysis through hindcasting." Journal of Petroleum Science and Engineering, 172, 340-348. https://doi.org/10.1016/j.petrol.2018.09.072
7. Male, F. 2019, "Using a segregated flow model to forecast production of oil, gas, and water in shale oil wells." Journal of Petroleum Science and Engineering, 180, 48-61. https://doi.org/10.1016/j.petrol.2019.05.010
8. Patzek, T.W., Saputra, W., Kirati, W. and Marder, M., 2019. "Generalized extreme value statistics, physical scaling, and forecasts of gas production in the Barnett shale." Energy & fuels, 33(12), pp.12154-12169. https://doi.org/10.1021/acs.energyfuels.9b01385
9. Ruiz Maraggi, L.M., Lake, L.W. and Walsh, M.P., 2020. "A Two-Phase Non-Linear One-Dimensional Flow Model for Reserves Estimation in Tight Oil and Gas Condensate Reservoirs Using Scaling Principles." In SPE Latin American and Caribbean Petroleum Engineering Conference. OnePetro. https://doi.org/10.2118/199032-MS
10. Ruiz Maraggi, L.M., Lake, L.W. and Walsh, M.P., 2020. "A Bayesian Framework for Addressing the Uncertainty in Production Forecasts of Tight Oil Reservoirs Using a Physics-Based Two-Phase Flow Model." In SPE/AAPG/SEG Latin America Unconventional Resources Technology Conference. OnePetro. https://doi.org/10.15530/urtec-2020-10480
11. Maraggi, L.M.R., Lake, L.W. and Walsh, M.P., 2021. Deconvolution of Time-Varying Bottomhole Pressure Improves Rate-Time Models History Matches and Forecasts of Tight-Oil Wells Production. In SPE/AAPG/SEG Unconventional Resources Technology Conference. OnePetro.
12. Ruiz Maraggi, L.M., Lake, L.W., and Walsh. M.P., 2022 "Rate-Pseudopressure Deconvolution Enhances Rate-Time Models Production History Matches and Forecasts of Shale Gas Wells." Paper presented at the SPE Canadian Energy Technology Conference, Calgary, Alberta, Canada, March 2022. doi: https://doi.org/10.2118/208967-MS
13. Ruiz Maraggi, L.M., Lake, L.W. and Walsh, M.P., 2022. Deconvolution Overcomes the Limitations of Rate Normalization and Material Balance Time in Rate-Transient Analysis of Unconventional Reservoirs. In SPE Canadian Energy Technology Conference. OnePetro.
14. Male, F., Duncan, I.J., 2022, "The Paradox of Increasing Initial Oil Production but Faster Decline Rates in Fracking the Bakken Shale: Implications for Long Term Productivity of Tight Oil Plays," Journal of Petroleum Science and Engineering, https://doi.org/10.1016/j.petrol.2021.109406
15. Ruiz Maraggi, L.M., 2022. Production analysis and forecasting of shale reservoirs using simple mechanistic and statistical modeling (Doctoral dissertation). http://dx.doi.org/10.26153/tsw/42112