Production data analysis and forecasting: Bayesian probabilistic methods to quantify uncertainty, analyze production performance trends in stack

Adaveni, Naga Santhosh

Production data analysis and forecasting: Bayesian probabilistic methods to quantify uncertainty, analyze production performance trends in stack

dc.contributor.advisor	Razzaghi, Talayeh
dc.contributor.author	Adaveni, Naga Santhosh
dc.contributor.committeeMember	Nicholson, Charles D.
dc.contributor.committeeMember	Devegowda, Deepak
dc.date.accessioned	2020-12-21T18:21:20Z
dc.date.available	2020-12-21T18:21:20Z
dc.date.issued	2020-12-18
dc.date.manuscript	2020-12-16
dc.description.abstract	In this work, a Bayesian approach of probabilistic estimation for decline curve analysis in unconventional reservoirs is presented. The primary objectives of this study are the quantification of the uncertainty for production forecasting and do a parent-child analysis for wells from the same play. MCMC-based Metropolis algorithm is used for sampling from the proposal distributions to generate posterior distributions for the decline curve parameters. This sampling technique is applied for three models: Arps, Duong, and power law exponential models. Prior and likelihood distributions are established for the three models based on our understating of the data and the models. Forecast estimates are generated using multiple intervals of initial production data to understand how the sampling algorithm generates better estimates with increasing amount of training data. 282 oil and gas wells Meramec STACK unconventional play are used in this work to quantify the production forecasting uncertainty. Results show that the MCMC-based approach was able to establish uncertainty bounds, matching MAP estimates for cumulative production. Based on the amount of production data available and the nature of the flow, the model that fits best can vary. Using the estimated decline curve parameters, parent-child well comparison analysis is done to understand the changing production dynamics in the Meramec STACK play.	en_US
dc.identifier.uri	https://hdl.handle.net/11244/326667
dc.language	en_US	en_US
dc.rights	Attribution-NonCommercial-NoDerivatives 4.0 International	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/4.0/	*
dc.subject	Production Forecasting	en_US
dc.subject	Bayesian analysis	en_US
dc.subject	Decline curve analysis	en_US
dc.subject	Uncertainty estimates	en_US
dc.thesis.degree	Master of Science	en_US
dc.title	Production data analysis and forecasting: Bayesian probabilistic methods to quantify uncertainty, analyze production performance trends in stack	en_US
ou.group	Gallogly College of Engineering	en_US
shareok.orcid	0000-0003-0441-1256	en_US