Package: bluebonnet
bluebonnetScaling solutions for production analysis from unconventional oil and gas wells.InstallationRun the commandpipinstallbluebonnetThis package works for Python versions 3.8-3.11. Dependencies are automatically
installed bypip. They include standards of the Python scientific stack,
includinglmfit,matplotlib,numpy,pandas, andscipy.Usagebluebonnethas a collection of tools for performing reservoir simulation in
tight oil and shale gas reservoirs. The main tools are:fluidsfor modeling PVT and viscosity of oil, water, and gas;flowfor building physics-based production curves; andforecastfor fitting and forecasting unconventional production.Examples can be found inthe documentation.ContributingInterested in contributing? Check out thecontributing guidelinesto 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 FameMichael MarderLicensebluebonnetwas created by Frank Male. It is licensed under the terms of the
BSD 3-Clause license.CreditsThis 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.bluebonnetwas created withcookiecutterand thepy-pkgs-cookiecuttertemplate.BibliographyPapers developing or using this approach include: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.1313380110Patzek, 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/10.1306/03241412125Male, 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.2015.03.001Male, 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/2152/46706Eftekhari, 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.2018.05.027Male, 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.2018.09.072Male, 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.2019.05.010Patzek, 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.9b01385Ruiz 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-MSRuiz 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-2020-10480Maraggi, 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.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-MSRuiz 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.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.2021.109406Ruiz 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