Efficient Particle Swarm Optimization of Well Placement to Enhance Oil Recovery Using a Novel Streamline-Based Objective Function

Experimental and Theoretical Determination of Diffusion Coefficients of CO2-Heavy Oil Systems by Coupling Heat and Mass Transfer

Robust optimization of the locations and types of multiple wells using CNN based proxy models

Impact of shale barriers on performance of SAGD and ES-SAGD — A review

Practical Data Mining and Artificial Neural Network Modeling for Steam-Assisted Gravity Drainage Production Analysis

Efficient Prediction of SAGD Productions Using Static Factor Clustering

Uncertainty Quantification Using Streamline Based Inversion and Distance Based Clustering

Uncertainty in oil sands reservoirs can be quantified by generating multiple realizations using geostatistical methods. However, it requires huge computing time to simulate all of the realizations. This article proposes a new approach for features modeling of oil sands reservoirs in metric space. As the first step, an area affected by the expansion of a steam chamber is set and converted to the polar coordinate system. The converted area is expressed as an image matrix consisting of 0 or 1 value. Then the matrix is transformed using two-dimensional discrete Fourier transform. Key features in the front columns and rows of the transformed matrix are extracted. These features in metric space are plotted using principal component analysis. Self-organizing map algorithm is used to select representative models of realizations for performing full flow simulations. In the result of grouping, each cluster group distributes separately in metric space according to reservoir productivity, but there are mixes of a sma...

Features Modeling of Oil Sands Reservoirs in Metric Space

Oil sands reservoir models have high uncertainty because of complex shale distribution and insufficient geophysical data. Therefore, reservoir behavior is predicted stochastically using lots of generation and flow simulation of feasible reservoir models. However, it takes huge time and cost to simulate all of reservoir models in order to understand its behaviors. The objective of this study is to map the relationship between oil sands reservoir models in metric space using streamline simulation. In order to understand structure of reservoir models visually, 100 reservoir models are generated by sequential indicator simulation. Before mapping, a distance, as a dissimilarity index is defined between models using streamline simulation. Since the production behavior of an oil sands reservoir is heavily affected by injected steam, streamline simulation is applied to predict the trend of the steam flow in an oil sands reservoir. After streamline simulation, the distance is calculated by comparing the r...

Metric Space Mapping of Oil Sands Reservoirs Using Streamline Simulation

In oil sands reservoirs, it is difficult to predict the production reliably because a steam chamber continues to expand with time and the affected area keeps changing. This article proposes a new approach for history matching and uncertainty analysis in oil sands reservoirs. The first step is to set an area affected by the expansion of the steam chamber. Then some features are extracted and plotted in metric space using 2D discrete Fourier transform and principal component analysis. Several realizations are selected based on the same interval sampling and ranked with similar levels in production curve. The sampling weights are assigned proportional to the rankings and one realization is selected accordingly. A flow simulation performs the nearest point (realization) around the selected one in metric space. The most dissimilar realization is replaced in the candidates. The previous step is repeated until the objective function meets a stop criterion. In the result of uncertainty analysis, it could lead to ...

Jeongwoo Jin

Papers

Efficient Prediction of SAGD Productions Using Static Factor Clustering

Uncertainty Quantification Using Streamline Based Inversion and Distance Based Clustering

Features Modeling of Oil Sands Reservoirs in Metric Space

Metric Space Mapping of Oil Sands Reservoirs Using Streamline Simulation

Uncertainty Analysis of Oil Sands Reservoirs Using Features in Metric Space