Using Gradients to Refine Parameterisation in Field-Case History Match Projects

Extending the life of the Johnston field requires an ability to produce reliable forecasts of the effects of reservoir interventions. Reliable forecasts need an effective, robust and accurate history match. The goal of this paper is to evaluate the relative performance of three different global assisted history-matching algorithms. We describe the implementation of an integrated parameterization and optimization method, which was tested on the Brugge synthetic model (SPE benchmark case study), and the results based on the best selected method are applied to the Johnston gas field in the Southern North Sea.There are two key components to assisted history matching: (i) choice of the parameterization, and (ii) selection and performance of the optimization algorithm. In this paper, three parameterization methods are tested (including use of gradients) on three global history-matching algorithms: Particle Swarm Optimization (PSO), Evolutionary Algorithm (EA) and Differential Evolution (DE). A combination of two different algorithms was also examined.We assessed the algorithm efficiency based on the lowest achieved objective function and the time taken to converge to the lowest value; the quality of the parameterization was examined based on the lowest objective value, the best history match and the consistency of geological parameters used for the history match.The results show that the effectiveness of each optimization algorithm is dependent on the parameterization method. When comparing parameterization methods, the Around the Median method seems to give the best results in terms of lowest misfit, and best history match when using the same history matching algorithm for the Brugge model example. When comparing across algorithms the DE method performs better than the rest. An iterative combination of algorithms is seen to be the best option and assists in a further minimization of the objective function. The effect of parameterization on match quality is also proved to be important. In some cases, an iterative process can be effective, where the history match is further improved by adding additional parameters or zoning existing parameters.This paper stresses and illustrates the importance of the choice of parameterization methods, while many papers focus only on the performances of the minimization algorithm. It also combines gradientbased parameterization method with global optimization method.

show abstract

Reservoir Simulation History Matching and Waterflooding Performance Forecast for a Large Sandstone Reservoir in Middle East

Song

et al. 2017

Day 3 Wed, April 26, 2017

View full text Add to dashboard Cite

For the same reservoir geological model, different simulation models could be obtained through history match calibration by different engineers. In other words, there are certain uncertainties within the calibrated simulation models. So how to reduce the uncertainty during history matching is still a problem. This paper presents how to quantify the waterflooding uncertainty and reasonably forecast waterflooding performance through reservoir simulation for a large multi-layered sandstone reservoir in Middle East. Based on the results of detailed geological characterization, dynamic description and parameter uncertainty analysis, three fine geological models are built, which stand for upside model, expected model and downside model. Then parameter sensitivity on waterflooding performance is conducted based on sector model simulation, and waterflooding development strategies are also optimized. The results are firstly applied for the three full field model calibration by reservoir history matching. After that the calibration models are used for waterflooding performance forecast and the performance uncertainty are also quantified. Take a large multi-layered sandstone reservoir in Middle East as an example. The MU reservoir is still under primary depletion with only 5% recovery currently, and waterflooding is urgent. Firstly, sector models are used for parameter sensitivity analysis and development strategy optimization. Then based on parameter sensitivity analysis results, the three full-field model are all calibrated with good history match. And different model calibration need different parameter adjustment, such as downside model has stronger aquifer support than that of upside model and expected model because of the poorer connectivity. So different models show different characteristics, such as most of west-south flank of MU reservoir are flooded for Downside model, while only some local area and some zones are water flooded for Expected and Upside model. Then waterflooding performance of different development plans are forecasted and compared based on the optimum development strategies and calibrated models. Furthermore, the performance uncertainty are quantified based on the three models. This paper offers a methodology and a case study of reservoir simulation history matching in order to reduce uncertainty and better waterflooding performance forecast for a large sandstone reservoir in Middle East. Comparison of the three models can help to better understand the reservoir. And the results are valuable for the following development options decision making. It also provides a reference for performance forecast and uncertainty analysis of similar reservoirs.

show abstract

Using Gradients to Refine Parameterisation in Field-Case History Match Projects

Cited by 3 publications

References 14 publications

Choosing a Parameterization

Choosing a Parameterization

Comparative Performance of History Matching Algorithms Using Diverse Parameterization Methods: Application to a Synthetic and North Sea Cases

Reservoir Simulation History Matching and Waterflooding Performance Forecast for a Large Sandstone Reservoir in Middle East

Contact Info

Product

Resources

About