Traditional DOF solutions use a web portal or intranet dashboard as the primary means of data access and collaboration. For an engineer these portals are useful to help identify field problems, reduce the amount of time spent looking for data and enable quicker analysis of issues. However, these platforms do not offer the rich two-way communication environment that could enable real-time interaction between engineers, systems, and support functions. Web portals are generally loosely coupled to modelling and visualization solutions which together can adversely affect performance of the solution. This paper presents how a new approach was tried, tested and proven to offer a transformation in the way we interact with well production, model based analytics and collaborate with others. This paper provides a case study documenting how a foundational Digital Oilfield (DOF) platform was extended to include model based workflows and multi channel mobile device integration.

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Economic pressure to improve production efficiency in unconventional reservoirs has met a stiff challenge to scale up traditional reservoir modeling methods to the entire field for quantifying well performance. The main reasons are lack of availability of key reservoir and well parameters and difficulty to setup and maintain models because of the large well count and rapid pace of operations. As a result, decline curve analysis is still the prevailing method for large scale evaluations, which does not consider routine pressure variations and operational constraints. Analytical rate transient (RTA) models warrant identification of flow regimes and geometrical assumptions (well and fractures) to apply discrete analytical models for various flow segments. This inherent limitation of RTA makes it interpretive and not conducive to fieldscale application, besides often lacking necessary inputs for all wells. It is desirable to have better understanding through a robust and consistent well performance analysis method at field scale to unlock significant production optimization opportunities with existing field infrastructure and investment. We have applied a reduced physics formulation based on Dynamic Drainage Volume (DDV) using commonly measured data for most wells (namely, flowback data, daily production rates, and wellhead pressure) to calculate continuous pressure depletion, transient productivity index (PI) and inflow performance relationship (IPR). This transient well performance (TWP) method eliminates the surface and wellbore operational impacts to extract the true reservoir signal 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, focusing on naturally flowing and gas-lifted wells. The fluid properties and bottomhole pressure estimated using data-driven methods for all wells provided excellent match with blind data (PVT lab reports and downhole gauge data). The TWP method normalizes reservoir and completion quality to extract valuable insights on effectiveness of well and completions design in the presence of varying geological and fluid properties. The 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. 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.