Streamlining the Well Location Optimization Process - An Automated Approach Applied to a Large Onshore Carbonate Field

Infill drilling is a proved strategy to improve hydrocarbon recovery from reservoirs to increase production and maximize field value. Infill drilling projects address the following questions: 1) Where should the wells be drilled? 2) What should be their optimum trajectories? 3) What are the realistic ranges of incremental production of the infill wells? Answering these questions is important yet challenging as it requires the evaluation of multiple scenarios which is laborious and time intensive. This study presents an integrated workflow that allows the optimization of drilling locations using an automated approach that comprises cutting-edge optimization algorithms coupled to reservoir simulation. This workflow concurrently evaluates multiple scenarios until they are narrowed down to an optimum range according to pre-set objectives and honoring pre-established well design constraints. The simultaneous nature of the workflow makes it possible to differentiate between acceleration and real incremental recovery linked to proposed locations. In addition, the technology enables the optimization of all the elements that are relevant to the selection of drilling candidates, such as location, trajectory, inclination, and perforation interval. The well location optimization workflow was applied to a real carbonate large field; heavily faulted; with a well count of +400 active wells and subject to waterflooding. Hence the need for an automated way of finding new optimal drilling locations enabling testing of many locations. Also due to the significant full field model size; sector modelling capability was used such that the optimization, i.e. running many scenarios; could be carried out across smaller scale models within a reasonable time frame. Using powerful hardware and a fully parallelized simulation engine were also important elements in allowing the efficient evaluation of ranges of possible solutions while getting deeper insights into the field and wells responses. As a result of the study, 8 out of the original 9 well locations were moved to more optimal locations. The proposed optimized locations generate an incremental oil recovery increase of more than 70% compared to the original location (pre-optimization). In addition, the project was completed within 2 weeks of equivalent computational time which is a significant acceleration compared to a manual approach of running optimization on a full field model and it is significantly more straight forward than the conventional location selection process. The novelty of the project is introduced by customized python scripts. These scripts allow to achieve practical ways for placing the well locations to explore the solution space and at the same time, honor well design constraints, such as maximum well length, maximum step-out from the surface well-pad, and well perforation interval. Such in-built flexibility combined with automation and highly advanced optimization algorithms helped to achieve the project goals much easier and faster.