An Ensemble-Based Infill Well Location Solution: A Cloud Native Application for Automating Well Placement and Evaluation

Abstract

Abstract Field development planning moves from using deterministically constructed reservoir models to stochastically generated ensembles of models to better capture subsurface uncertainties. This leads to a challenge on how to understand reservoir dynamics and extract insights because of the large volumes of data involved. The objective of the presented system is to demonstrate how cloud-based computing can help to derive actionable insights of this large volume of data through automation, machine learning, and elastic scaling. In this paper we present a cloud-native solution for optimizing and evaluating ensemble-based infill well locations. An opportunity index (OI) is derived from static and dynamic grid properties of reservoir simulation and a connected component search for every realization. Probability maps of OI are constructed to present the likelihood of high-OI areas from all simulations to propose infill well targets for the ensemble. Infill wells are automatically evaluated and ranked by ensemble field production increases. The workflow is deployed in a cloud-based system to leverage elastic scaling of compute resources to cope with the large volumes of data inherent in ensemble models. The result from deploying this new workflow in a mature field in the North Sea shows that optimization of infill well targets at the ensemble level increases the robustness of targets compared to the alternative of selecting one or a few realizations. When randomly selecting two deterministic cases to analyze infill well locations, we observe inconsistency in candidate well locations. In case A, little opportunity is shown in the northern part of the reservoir and no opportunity in the southwestern part, whereas the result from case B shows almost the opposite, having a large area of opportunity in the northern part and a small opportunity in the southwestern part. By applying the proposed solution, only focused areas of the northern part of the reservoir are suitable for candidate well locations. The study time of target identification and evaluation is significantly shortened. The cloud-based deployment removes the company's need to own and manage powerful computer and data-storage infrastructure. In summary, the solution improves the workflow efficiency and provides high-quality results for field development decision making.