Application of Surrogate Reservoir Model (SRM) to an Onshore Green Field in Saudi Arabia; Case Study

Abstract

Abstract Application of the Surrogate Reservoir Model (SRM) to an onshore green field in Saudi Arabia is the subject of this paper. SRM is a recently introduced technology that is used to tap into the unrealized potential of the reservoir simulation models. High computational cost and long processing time of reservoir simulation models limit our ability to perform comprehensive sensitivity analysis, quantify uncertainties and risks associated with the geologic and operational parameters or to evaluate a large set of scenarios for development of green fields. SRM accurately replicates the results of a numerical simulation model with very low computational cost and low turnaround period and allows for extended study of reservoir behavior and potentials. SRM represents the application of artificial intelligence and data mining to reservoir simulation and modeling. In this paper, development and the results of the SRM for an onshore green field in Saudi Arabia is presented. A reservoir simulation model has been developed for this green field using Saudi Aramco's in-house POWERS™ simulator. The geological model that serves as the foundation of the simulation model is developed using an analogy that incorporates limited measured data augmented with information from similar fields producing from the same formations. The reservoir simulation model consists of 1.4 million active grid blocks, including 40 vertical production wells and 22 vertical water injection wells. Steps involved in developing the SRM are identifying the number of runs that are required for the development of the SRM, making the runs, extracting static and dynamic data from the simulation runs to develop the necessary spatio-temporal dataset, identifying the key performance indicators (KPIs) that rank the influence of different reservoir characteristics on the oil and gas production in the field, training and matching the results of the simulation model, and finally validating the performance of the SRM using a blind simulation run. SRM for this reservoir is then used to perform sensitivity analysis as well as quantification of uncertainties associated with the geological model. These analyses that require thousands of simulation runs were performed using the SRM in minutes.