Numerical Study and Production Forecasting of Unconventional Liquid-Rich Shale Reservoirs with Multi-Fractured Horizontal Wells



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Accurate production performance evaluation and forecasting in shales during the early stages of development can play an important role in minimizing uncertainties associated with unconventional reservoirs. The liquid-rich shale reservoirs, due to their complex rock and fluid behaviors, require different analysis than the conventional reservoirs. In this work, we considered both reservoir simulation and analytical models to forecast the production from liquid rich shales. We modified the tri-linear flow model derived for single phase flow to use it for multiphase flow with some simplifying assumptions. We were able to validate the results obtained from the analytical model with errors of less than 10% when used to forecast liquid rich shale volatile oil reservoirs. Additionally, reservoir simulation was used to identify the effect of several parameters on the estimated ultimate recovery (EUR) of gas condensate reservoirs. Fracture half-length, permeability, and fracture spacing was identified to be the most important parameters for maximizing the cumulative gas production. It was also seen that the interaction of different parameters with each or their combined effect was important in optimizing the final EUR for oil and gas. We also identified the effect of fluid composition on well-spacing in the Eagle Ford Shale. For critical fluids, liquid dropout and condensate banking had a huge impact on the final production. It was seen that gas condensate wells in shales exhibit a long transition 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 a gas condensate reservoir. Results also showed that transient linear flow model with no modification for boundary-dominated flow overestimates the production in almost all cases. Finally, compositional reservoir model has been 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 into principal component (PC) scores and coefficients. The dataset was cross-validated to check its ability to predict the missing production data based on PC scores and coefficients of the limited production data. Principal component analysis was further applied to the field data from several wells from Eagle Ford shale. Two to three PCs were required to recreate the initial data with reasonable accuracy depending on the quality of the input data. During the validation step, we observed that some of the wells exhibited significant error which could be attributed to significantly different production profiles of those wells compared to the other wells. For simulated data, four PCs were enough to yield the prediction with average errors of 0.16%, 0% and 0.77% respectively for gas rate, cumulative gas and CGR respectively. For field data, three PC yielded the best prediction with average error of 1.63% and 2.98% for gas rate and oil rate respectively. This shows that multivariate statistics and data driven methods can be used as an important approach to complement existing tools like reservoir simulation and decline curve analysis to perform production data analysis. We recognize that even more rapid approximate methods will be required for routine analysis. Understanding the limitations of different approximate methods and application of methods to overcome these limitations in given circumstances should lead to optimal use of these methods.



Production forecasting, Data driven, Principal component analysis, Arps, Reservoir Simulation