Understanding well production performance in unconventional reservoirs is fundamental for achieving optimal field development and maximizing value. It is desired to have a robust and scalable method for quantifying well productivity, which can be applied in a practical manner to all wells, overcoming the limitations of decline curve analysis (not representative of changing operating conditions) and analytical and numerical models (based on either simplifying assumptions, manual interpretation or not suitable for large scale usage).

We propose a reduced physics formulation to use routinely measured data for most wells - namely, production rates, flowing pressure, and fluid properties. As a hybrid method, it is data-centric but rooted on physics-based principles and captures the dynamic evolution of the system by estimating transient productivity index. By incorporating pressure in the analysis, it goes beyond limitations of rate-time methods by handling changing operating conditions. The transient productivity index provides a better indicator for well performance analysis and production forecasting. Further, this hybrid method provides a fast, practical, consistent, reliable, and scalable method to analyze every well in the field to support the pace of operations and data-driven decision-making.

The method combines the concepts of diffusive time of flight and material balance to estimate the dynamic drainage volume under transient flow and calculate the average reservoir pressure decline during the well primary depletion. For multiphase flow in the reservoir at conditions below saturation pressure, dependence of fluid properties with pressure are handled, representing both liquid and gas fluid types. The calculations are implemented through a novel optimization algorithm after applying appropriate data pre-processing and cleansing techniques. The resulting drainage volume (and associated instantaneous recovery ratio) can be correlated with rock quality and completion attributes and is used as a metric to rank and compare wells. Also, the productivity index can be directly estimated and used as a key metric representing the true well production potential, by normalizing changing operating conditions. A multiphase production forecast is generated, incorporating the effects of PVT changes and pressure depletion. Finally, the well inflow performance relationship (IPR) is estimated for any time step, capturing well deliverability, and enabling well production optimization. The complete workflow has been successfully implemented in an automated way at full field scale.

Our methodology overcomes common challenges encountered for analyzing well performance in unconventional reservoirs that are traditionally based on rate decline-based methods, thus normalizing the effect of surface operations on well performance. It also builds upon our previous work, generalizing the methodology to include gas and saturated oil reservoirs (compressible fluids), as well as incorporating the effects of variable total compressibility and pressure-dependent PVT properties.

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