Application of Machine Learning in a Giant Mature Reservoir to Speed-Up Infill Prospects Screening, Optimize Field Development and Improve the Ultimate Recovery Factor

Abstract

In giant reservoirs, production sustainability strongly depends on the identification of opportunities for infill drilling. This paper presents the use of Machine Learning to speed-up and improve the efficiency of the evaluation of future infill wells, in an effort to optimize field development of a Giant Mature reservoir Onshore Abu Dhabi. In the mature giant carbonate reservoir studied, more than 420 wells are already drilled with consistent spacing but with varying orientations. This paper illustrates some examples of settings that are difficult to assess without geometric calculations, leading to time-consuming opportunity identification and classification. The minimum set of input for the program includes existing wells trajectories, faults polygons, contact, and production data. Users can define the minimum drainage area for each well, maturity criteria and drain length. For each subsurface target identified, a polygon and simulation input are generated. The Python program is developed and run on an in-house platform and solve the future wells positioning in three main steps: (1) Geometric screening and identification of locations with required spacing, (2) Analysis of nearby well performance, (3) automatic generation of simulation input for evaluation of the subsurface target.