Improved reservoir forecasting through natural and injected tracer modeling

Natural tracers (geochemical and isotope variation of reservoir fluids) and ordinary tracers are underexploited as a source of data to understand oil reservoirs, and should be used in a better way e.g. to locate and quantify bypassed and un-produced oil v olumes in the reservoirs. The project will address this challenge by bringing together geochemical, tracer and modelling competence at IRIS, Texas A&M University and IFE (project leader). The principal idea is to improve reservoir models and forecasts by including natural as well as injected tracers, in Ensemble Kalman Filter (EnKF) technologies, a mathematical tool that optimizes the solution using an ensemble of possible scenarios. The main R&D challenges that must be addressed in the project are: ** Development of an adequate tracer tool. **Adaptation of EnKF tool to account for natural and injected tracers. **Method development to verify and improve reservoir models and forecasts by natural and injected tracers. **Method development to identif y and quantify unproduced oil. The main deliverable from the project is a method (including two software tools) for reservoir forecasting that combines ordinary production data (well pressure, gas and fluid rates) with ordinary tracers and a portfolio of qualified and tested natural tracers. Improvements of reservoir models and forecasts resulting from the project will result in significant saving costs for oil companies in connection with planning for enhanced oil recovery (EOR/IOR) projects. Some of the geochemical data are recorded on a routinely basis for other purposes (e.g. scale monitoring) and can therefore yield an additional gain at a small cost. The project will train one Post Doc fellow, educate one PhD fellow and offer summer-jobs to MSc students. It involves international collaboration with Texas A&M University. The budget is 14.477 MNoK over 3 1/2 years and will be financed by RCN (52%) and four oil companies (48%).