[CCoE Notice] Dissertation Defense: Numerical Study and Production Forecasting of Unconventional Liquid-Rich Shale Reservoirs

[Grayson, Audrey A]

PhD DEFENSE STUDENT: Aaditya Khanal
DATE: Friday, November 18, 2016
TIME: 11:30 AM
PLACE: 1A, Energy Research Park (ERP), Room 191D
DISSERTATION CHAIR: Dr. John Lee
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TITLE:
Numerical Study and Production Forecasting of Unconventional Liquid-Rich Shale Reservoirs


Use of horizontal drilling and hydraulic fracturing of unconventional shale formations have changed the landscape of US oil and gas production. Accurate production performance evaluation and forecasting in shales during the early stages of development can play an important role in minimizing uncertainty.  Arps’ hyperbolic decline model and the modified Arps model are widely used to estimate ultimate recovery for both conventional and unconventional reservoirs. However, Arps’ model is applicable only to reservoirs in boundary-dominated flow, which appears only after a significant time in ultra-low permeability reservoirs.



In this study, reservoir simulation was used to identify the effect of several uncertain parameters and their interaction with each other on estimated ultimate recovery (EUR) of liquid rich shale (LRS) gas condensate reservoirs. Analytical tri-linear flow model, derived for a single-phase flow case, was modified for multiphase flow with simplifying assumptions. It was seen that gas condensate wells in shales exhibit a long transitional period between the end of linear flow and the start of boundary dominated flow. Pressure normalization was found to be an effective method to identify flow regimes in gas condensate reservoirs. Results showed that transient linear flow model with no modification for boundary-dominated flow overestimates the production forecast in almost all cases. Finally, compositional reservoir model was used to create several iterations of synthetic production histories from liquid rich shales (LRS) wells based on Monte Carlo simulation with predefined probability distributions. Cumulative gas, gas rate, and condensate-to-gas ratio (CGR) for the simulated cases were decomposed by principal component analysis (PCA) and were used to recreate the original data. The dataset was cross-validated to check its ability to predict the missing production data. This workflow was verified for field data from the Eagle Ford Shale.


Given the uncertainty in forecasts using traditional models, the work-flow presented in this study, which involves reservoir simulation and data-driven approach, improves the accuracy of production forecasts from new wells or wells with limited production history. This workflow demonstrated in this work can be readily automated to analyze a large set of production data from conventional and unconventional reservoirs.
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