Streamline Assisted Ensemble Kalman Filter - Formulation and Field Application

The goal of any data assimilation or history matching algorithm is to enable better reservoir management decisions through the construction of reliable reservoir performance models and the assessment of the underlying uncertainties. A considerable body of research work and enhanced computational capabilities have led to an increased application of robust and efficient history matching algorithms to condition reservoir models to dynamic data. Moreover, there has been a shift towards generating multiple plausible reservoir models in recognition of the significance of the associated uncertainties. This provides for uncertainty analysis in reservoir performance forecasts, enabling better management decisions for reservoir development. Additionally, the increased deployment of permanent well sensors and downhole monitors has led to an increasing interest in maintaining 'live' models that are current and consistent with historical observations. One such data assimilation approach that has gained popularity in the recent past is the Ensemble Kalman Filter (EnKF) (Evensen 2003). It is a Monte Carlo approach to generate a suite of plausible subsurface models conditioned to previously obtained measurements. One advantage of the EnKF is its ability to integrate different types of data at different scales thereby allowing for a framework where all available dynamic data is simultaneously or sequentially utilized to improve estimates of the reservoir model parameters. Of particular interest is the use of partitioning tracer data to infer the location and distribution of target un-swept oil. Due to the difficulty in differentiating the relative effects of spatial variations in fractional flow and fluid saturations and partitioning coefficients on the tracer response, interpretation of partitioning tracer responses is particularly challenging in the presence of mobile oil saturations. The purpose of this research is to improve the performance of the EnKF in parameter estimation for reservoir characterization studies without the use of a large ensemble size so as to keep the algorithm efficient and computationally inexpensive for large, field-scale models. To achieve this, we propose the use of streamline-derived information to mitigate problems associated with the use of the EnKF with small sample sizes and non-linear dynamics in non-Gaussian settings. Following this, we present the application of the EnKF for interpretation of partitioning tracer tests specifically to obtain improved estimates of the spatial distribution of target oil

An assessment of subsea production systems

The decreasing gap between technology and the its applicability in the oil industry has led to a rapid development of deepwater resources. Beginning with larger fields where the chances of economic success are high, to marginal fields where project economics becomes a more critical parameter, the petroleum industry has come a long way. However, the ever growing water depths and harsher environments being encountered are presently posing challenges to subsea production. Being able to develop a field and then proceeding to ensure flow for the life of the field comprises many situations where the production equipment can fail and falter or through external factors, be deemed unavailable. Some of the areas where most of the current developments in subsea production are being seen are in subsea processing, flow assurance, long term well monitoring and intervention technologies areas that pose some of the biggest challenges to smooth operation in the deepwater environment. This research highlights the challenges to overcome in subsea production and well systems and details the advances in technology to mitigate those problems. The emphasis for this part of the research is on multiphase pumping, subsea processing, flow assurance, sustained casing pressure problems and well intervention. Furthermore, most operators realize a reduced ultimate recovery from subsea reservoirs owing to the higher backpressure imposed by longer flowlines and taller risers. This study investigates the reasons for this by developing a global energy balance and detailing measures to improve production rates and ultimate recoveries. The conclusions from this energy balance are validated by simulating a deepwater field under various subsea production scenarios

Streamline Assisted Ensemble Kalman Filter - Formulation and Field Application

The goal of any data assimilation or history matching algorithm is to enable better reservoir management decisions through the construction of reliable reservoir performance models and the assessment of the underlying uncertainties. A considerable body of research work and enhanced computational capabilities have led to an increased application of robust and efficient history matching algorithms to condition reservoir models to dynamic data. Moreover, there has been a shift towards generating multiple plausible reservoir models in recognition of the significance of the associated uncertainties. This provides for uncertainty analysis in reservoir performance forecasts, enabling better management decisions for reservoir development. Additionally, the increased deployment of permanent well sensors and downhole monitors has led to an increasing interest in maintaining 'live' models that are current and consistent with historical observations. One such data assimilation approach that has gained popularity in the recent past is the Ensemble Kalman Filter (EnKF) (Evensen 2003). It is a Monte Carlo approach to generate a suite of plausible subsurface models conditioned to previously obtained measurements. One advantage of the EnKF is its ability to integrate different types of data at different scales thereby allowing for a framework where all available dynamic data is simultaneously or sequentially utilized to improve estimates of the reservoir model parameters. Of particular interest is the use of partitioning tracer data to infer the location and distribution of target un-swept oil. Due to the difficulty in differentiating the relative effects of spatial variations in fractional flow and fluid saturations and partitioning coefficients on the tracer response, interpretation of partitioning tracer responses is particularly challenging in the presence of mobile oil saturations. The purpose of this research is to improve the performance of the EnKF in parameter estimation for reservoir characterization studies without the use of a large ensemble size so as to keep the algorithm efficient and computationally inexpensive for large, field-scale models. To achieve this, we propose the use of streamline-derived information to mitigate problems associated with the use of the EnKF with small sample sizes and non-linear dynamics in non-Gaussian settings. Following this, we present the application of the EnKF for interpretation of partitioning tracer tests specifically to obtain improved estimates of the spatial distribution of target oil

Effective permeability upscaling from heterogenous to homogenous porous media

An effective method to upscale permeability is presented to represent a heterogeneous reservoir with homogeneous permeability and porosity values. As a result, there is no need to deal with dual-porosity or dual-permeability models in reservoir simulations. Thus, the required CPU time for reservoir production and flow simulations is reduced significantly