Abstract
The Vaca Muerta formation in Argentina is emerging as one of the most promising resources of shale oil/gas plays in the world. At the current well drilling pace, challenges in streamlining data acquisition, production analysis and forecasting for executing timely and reliable reserves and resource estimations will be an overarching theme in the forthcoming years. In this work, we demonstrate that field operation decision cycles can be improved by establishing a workflow that automatically integrates the gathering of reservoir and production data with fast forecasting AI models.
We created a data platform that regularly extracts geological, drilling, completion and production data from multiple open data sources in Argentina. Data cleansing and consolidation are done via the integration of fast cross-platform database services and natural language processing algorithms. A set of AI algorithms adapted to best capture engineering judgment are employed for identifying multiple flow regimes and selecting the most suitable decline curve models to perform production forecasting and EUR estimation. Based on conceptual models generated from minimum available data, a coupled flow-geomechanics simulator is used to forecast production in other field areas where no well information is available. New data is assimilated as it becomes available improving the reliability of the fast forecasting algorithm.
In a matter of minutes, we are able to achieve high forecasting accuracy and reserves estimation in the Vaca Muerta formation for over eight hundred wells. This workflow can be executed on a regular basis or as soon as new data becomes available. A moderate number of high-fidelity simulations based on coupled flow and geomechanics allows for inferring production scenarios where there is an absence of data capturing space and time. With this approach, engineers and managers are able to quickly examine a feasible set of viable in-fill scenarios. The autonomous integration of data and proper combination of AI approaches with high-resolution physics-based models enable opportunities to reduce operational costs and improving production efficiencies.
The integration of physics-based simulations with AI as a cost/effective workflow on a business relevant shale formation such as Vaca Muerta seems to be lacking in current literature. With the proposed solution, engineers should be able to focus more on business strategy rather than on manually performing time-consuming data wrangling and modeling tasks.
