Locating Infill Targets of an Offshore Field Using an Ensemble Based Integrated Uncertainty Centric Modeling Approach
Authors: Sadigov Subhi , Bahjam Siti , Lackner Alf Sebastian 
Published: September 2022

Abstract
The aim of the study is to demonstrate the value of an integrated ensemble-based modeling approach for improving reservoir management of a mature offshore field located on the Norwegian Continental Shelf. Automated workflows are created to include subsurface uncertainties from both static and dynamic domains within a short reservoir modeling timeline. Potential infill targets for producers are located and evaluated efficiently as a result of implementing the proposed methodology.
For ensemble-based methodologies, an ensemble of equiprobable reservoir models is created with the guidance of a multi-disciplinary team to represent realistic reservoir uncertainties. Automated workflows are established to capture and propagate subsurface uncertainties spanning from grid structure creation, petrophysical modeling, and dynamic modeling while honoring all firm well data. These equiprobable models are then conditioned to the historic production data using an iterative ensemble-based data assimilation algorithm. The proposed method supports the conditioning of a large number of reservoir parameters in a consistent manner on both a local level (e.g., facies and petrophysical properties) and a global level (e.g., aquifer size and relative permeability curves). The conditioned ensemble is then used for robust forecasting studies for making important reservoir management decisions under uncertainty.
As a result of the proposed methodology, an ensemble of history-matched reservoir models is created in a remarkably short modeling time. Analysis of the updates made during the data assimilation process provides crucial insights into the reservoir such as the connectivity between the existing wells, the communication between different segments of the field, and critical flow dynamics affecting drainage decisions. Such insights would not have been obtained through just a traditional HM exercise where just one reservoir model was tuned to the dynamic data, and where all trust was put into that single model. Several potential infill targets for producer wells are identified by using the proposed integrated approach and ranked based on their potential risk level and added value. The forecasting studies carried out to evaluate the value of each of these targets capture the subsurface uncertainties on the dynamic response, which enables asset teams to make informed field development decisions by quantifying and ranking alternatives. The next steps will be augmenting the workflow to improve the back-produced injected water rates and modifying the base grid structure. This should allow for even better control of the formation water breakthrough timing in certain parts of the reservoir.
This paper proposes a systematic and integrated workflow using an ensemble-based method for solving reservoir modeling challenges. The automated nature of the methodology significantly reduces the reservoir modeling timeline and enables it to be utilized on a wide range of hydrocarbon assets to maximize recovery. The infill targets that were identified through the proposed approach are very similar to the targets identified through a traditional thickness map approach. However, the results are obtained in a shorter time span and with a better grasp of critical uncertainties using this integrated method compared to traditional methods.