Chaodong Yang

Simulation and Optimization of Trapping Processes for CO2 Storage in Saline Aquifers

This paper describes the modelling of the main processes for CO 2 trapping in saline aquifers, namely solubility trapping, residual gas trapping and mineral trapping. Several important aspects of CO 2 storage are presented. It has been found that the total amount of CO 2 trapped as a soluble component, and as residual gas, can be enhanced by injecting brine above the CO 2 injector. An optimization technique is used to adjust the location and rate of the brine injector to maximize the total amount of CO 2 trapping. The security of the trapping process is then evaluated by taking into account the leakage of mobile CO 2 through the caprock. For long-term CO 2 storage, the conversion of CO 2 into minerals is found to depend on the pre-existing minerals in the aquifer that provide the necessary ions for the reactions to occur.

Economic Optimization and Uncertainty Assessment of Commercial SAGD Operations

As SAGD is being increasingly used as a commercial technology to recover heavy oil and bitumen, it is essential to determine the most economical operating conditions for a SAGD operation by reservoir simulation. Furthermore, to support the decision-making process of a SAGD project, it is also important to quantitatively assess the uncertainty of its economic forecasts. In this paper, the application of global optimization, experimental design, response surface generation and Monte Carlo simulation techniques in the workflow of SAGD simulation studies were demonstrated with a real field case example. The field case is an infill SAGD project with two planned SAGD well pairs and eight existing primary production wells which have 5 years of primary production. A bottomwater zone is also present. Three major steps of the workflow are: 1) history matching primary production data; 2) optimizing SAGD performance; and 3) quantifying uncertainty of the SAGD forecasts. Firstly, experimental design and DECE (Designed Exploration and Controlled Evolution) optimization methods were used to achieve a faster and better history match than the traditional manual history match. Secondly, SAGD performance was optimized by adjusting the steam injection rate and producer liquid withdrawal rate during different SAGD operation periods. Finally, experimental design and response surface generation techniques were applied to build a polynomial response surface through which the net present value (NPV) of the SAGD project is correlated with uncertain parameters and a SAGD design parameter. Monte Carlo simulation was then performed to quantify the uncertainty of SAGD forecasts in terms of cumulative probability distribution of the NPV at different values of the SAGD design parameter. The results show that the economics of this project are improved considerably through optimization. The optimum operating conditions obtained use a high initial steam rate and high production rate to develop the steam chamber. After the instantaneous steam-oil ratio reaches a certain value, both steam rate and production rate are lowered to prevent steam breakthrough to the bottomwater. The uncertainty of the project NPV was assessed, taking into consideration the uncertainties in high temperature relative permeability endpoints and the variation of the SAGD design parameter.

Artificial Neural Network Modeling and Forecasting of Oil Reservoir Performance

A detailed uncertainty analysis on numerical flow simulation models preserving a robust and reliable model for an oil and gas reservoir is often deterministic, cumbersome, and expensive (manpower and time-consuming). Presence of a high-dimensional data space consisting of a large number of operational and geological parameters impedes practical decision-making and future performance prediction for oil and gas recovery processes. Thus, the rise of uncertainty-based reservoir development scenarios has provoked reservoir engineers to look for alternative modeling techniques that are capable of being reevaluated numerous times to examine the impact of specific variables or probing a range of scenarios on production profiles. This study reviews simulation data-driven proxy models that can be used instead of an actual reservoir simulator by using data interpolation in a high-dimensional parameter space.