Automated Reservoir Model Calibration Applied to a Complex Multizone Heavy Oil Field

Abstract

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 210185, “Automated Reservoir Model Calibration for Field-Development-Plan Evaluation Under Subsurface Uncertainty Applied to a Complex Multizone Heavy Oil Field,” by Luis D. Mendoza, Atahualpa J. Villarroel, SPE, and Maria F. Hurtado, Schlumberger, et al. The paper has not been peer reviewed. _ Fully integrated reservoir modeling for field-development optimization under subsurface uncertainty has been a major challenge so far for Rubiales, a major heavy oil field in Colombia with original oil in place of greater than 5 billion STB. An automated reservoir characterization work flow was developed to generate multiple history-matched models on the field and well level. The developed methodology and work flow successfully delivered field-development evaluation under subsurface uncertainty. The work-flow design is applicable for other fields with similar characteristics and delivery objectives. Introduction The Rubiales field is in the southeast of Puerto Gaitán, approximately 310 km from Bogotá. It is the most important oil field in Colombia in terms of extension, original volumes, and production but also is one of the country’s most complex fields, with a variety of technical challenges. Lithologically, the field is an unconsolidated sandstone reservoir with stratigraphic complexity that includes a high degree of vertical and lateral heterogeneity. Additionally, it has an infinite aquifer with tilted oil/water contact (OWC) in combination with high permeability. The produced fluid is a heavy oil (15 °API) with high water cut (approximately 95%). The current field-development plan (FDP) is based on horizontal wells as infill drilling to maintain oil production levels. A solution was designed to be tested in a specific area of the field implementing digital integration technologies. This solution included the capture of geological heterogeneities in terms of lateral and vertical continuity of the sand bodies. This digital methodology allows understanding seal and OWC effects on water production using a high-resolution model combined with an automated calibration process based on actual production as evidence. Work-Flow Design History Matching. In this project, a typical assisted history-matching process was run initially that included sensitivity and uncertainty analyses followed by an optimization algorithm. Once the sensitivity analysis was complete, the most-influential parameters were identified. Then, some variables were disabled and the uncertainty process was run with a Monte Carlo sampling considering only those parameters considered significant for the objective response. Through this process, a case ensemble with a wide-spectrum result was obtained. The next step was to focus on the optimization that was designed to improve the objective function value that should be minimized by tuning the set of input parameters remaining as active influencers. An evolutionary algorithm was implemented for the optimization that operated with three phases. In the first phase, simulation cases were distributed randomly in the search region, providing an initial ensemble. During the second phase, the better simulation cases were retained and the average objective function value was reduced. In the final phase, only the best simulation cases survived. Even when a modern algorithm was applied, the matching results were not sufficient at the well level. However, this work flow provided a good base case and insights regarding reservoir response, which are the inputs for the next stage of history matching. The further development of the history-matching methodology is detailed in the complete paper.