Abstract

There is an increasing need to produce unconventional reservoirs profitably with not as many new wells drilled to stem production decline. Hence, there is a need to scale up traditional reservoir modeling methods to the entire field for quantifying well performance. High fidelity physics-based models face a scalability challenge to extend to large well counts with rapid pace of operations. However, pure data-driven approaches face a challenge to represent essential physical elements, further compounded by lack of key operational parameters such as pressures across large number of wells. As a result, decline curve analysis is still a prevailing method for large scale evaluations, which are performed just based on the available well rates, but they do not consider routine pressure variations and operational constraints. Other widely used method is rate transient analysis (RTA) which requires identification of flow regimes and geometrical assumptions (well and fractures) which is an inherent limitation of RTA making it interpretive and not conducive to field-scale applications. Thus, a robust and consistent well performance analysis method is needed that can be applied at field scale to unlock significant production optimization opportunities with existing field infrastructure and investment.

Our formulation employs a reduced physics model that is based on identification of Dynamic Drainage Volume (DDV) using commonly measured data for most wells (namely, flowback data, daily production rates, and wellhead pressure) to calculate continuous reservoir pressure depletion, transient productivity index (PI) and dynamic inflow performance relationship (IPR). The key idea behind transient well performance (TWP) is that the drainage volume is increasing continuously with time, but the exact geometry is unknown. TWP extracts the true reservoir signal by eliminating the surface and wellbore operational impacts that can be used for robust well performance analysis and forecasting.

We applied the TWP method in multiple basins with large well counts (more than 1000 wells) producing under a variety of methods. In this paper, we present several case studies illustrating various production optimization opportunities and insights, focusing on naturally flowing and gas-lifted wells. The TWP method is shown to normalize reservoir and completion quality to extract valuable insights on effectiveness of well and completions design in the presence of varying geological and fluid properties. A concept of dynamic clustering was applied to identify correlations of key well performance drivers to TWP model outputs. Transient PI and dynamic IPR results provided valuable insights on how and when to select various artificial lift systems. During gas lift, we identified several wells that were over-injecting gas volumes at higher compressor discharge head, with line of sight to significant operational cost savings and reduced energy consumption.

The proposed methodology combines pragmatic use of physics and data-driven methods to solve a critical need for analyzing unconventional reservoirs and enabling intelligent operations. Field application of the novel DDV method on large well population has been quite successful in identifying various optimization opportunities that would not have been possible, timely, or repeatable with other traditional methods.

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