Improved Reservoir History Matching and Production Optimization with Tracer Data

Chen, Hsieh (Aramco Services Company: Aramco Research Center-Boston) | Poitzsch, Martin E. (Aramco Services Company: Aramco Research Center-Boston)



Interwell tracers have been shown to provide invaluable information about reservoir dynamics, well connectivity, and fluid flow allocations. However, tracer tests are often applied sporadically because their immediate returns of investments are not readily apparent to a resource-holder. Here, we rigorously demonstrate that tracer data can indeed improve reservoir history matching, and, more importantly, improve future production, using reservoir simulations on benchmark problems. Sensitivity studies and the limitations of tracer data are also provided.

The numerical experiments were divided in two sections. First, production data with or without tracer data from reference fields were collected for the first water flooding periods for history matching. Second, the history matched models from the first section were used for production optimization for the next water flooding periods. The ensemble smoother with multiple data assimilation (ES-MDA) was used for the history matching processes for the first part of the numerical experiments, and the modified robust ensemble-based optimization (EnOpt) was adopted to maximize the net present value (NPV) for the second part of the numerical experiments.

The three-dimensional channelized "Egg Model" was chosen as the initial benchmark problem. From the first part of the numerical experiments, using the same hyper-parameters, it was observed that history matching including tracer data resulted in a better match of the field production rates with smaller standard deviations. In addition, history matching including tracer data resulted in more distinct geological features when observing the history matched permeability maps. From the second part of the numerical experiments, we observed that the geological models history matched including tracer data resulted in better production optimization with higher NPV produced. In the specific case of the Egg Model, +4.3% increase of the NPV was observed.

To understand the sensitivity and the limitations of the tracer data, the same numerical experiments were performed on a library of reservoir models with different fracture patterns. After the history matching and production optimization simulations, we observed that including tracer data gave positive NPV increases ranging from +0.3% to +9.4% from 5 of the 7 test cases. It was observed that tracers were more effective for the non-homogeneously flooded reservoirs.

To the best of our knowledge, this paper is the first study that quantifies the benefits of tracers in the context of the improved production, measured in NPV. In a broader perspective, we believe this is the best way to test any new history matching algorithms or reservoir surveillance methods. In this work, we show that tracers can result in positive NPV in most situations, and oil producers using large-scale water flooding operations would benefit from performing more tracer tests in their operations.