Heavy oil resources are becoming increasingly important for the global oil supply, and consequently there has been renewed interest in techniques for extracting heavy oil. Among these, in-situ combustion (ISC) has tremendous potential for late-stage heavy oil fields, as well as high viscosity, very deep, or other unconventional reservoirs. A critical step in evaluating the use of ISC in a potential project is developing an accurate chemical reaction model to employ for larger-scale simulations. Such models can be difficult to calibrate, however, that in turn can lead to large errors in upscaled simulations. Data-driven models of ISC kinetics overcome these issues by foregoing the calibration step and predicting kinetics directly from laboratory data. In this work, we introduce the Non-Arrhenius Machine Learning Approach (NAMLA). NAMLA is a machine learning-based method for predicting O2 consumption in heavy oil combustion directly from ramped temperature oxidation (RTO) experimental data. Our model treats the O2 consumption as a function of only temperature and total O2 conversion and uses a locally-weighted linear regression model to predict the conversion rate at a query point. We apply this method to simulated and experimental data from heavy oil samples and compare its ability to predict O2 consumption curves with a previously proposed interpolation-based method. Results show that the presented method has better performance than previously proposed interpolation models when the available experimental data is very sparse or the query point lies outside the range of RTO experiments in the dataset. When available data is sufficiently dense or the query point is within the range of RTO curves in the training set, then linear interpolation has comparable or better accuracy than the proposed method. The biggest advantage of the proposed method is that it is able to compute confidence intervals for experimentally measured or estimated O2 consumption curves. We believe that future methods will be able to use the efficiency and accuracy of interpolation-based methods with the statistical properties of the proposed machine learning approach to better characterize and predict heavy oil combustion.
The description of chemical kinetics is very import to the simulation of reactive transport for enhanced oil recovery (EOR). Characterizing petroleum ignition is especially important for simulation and prediction of In-Situ Combustion (ISC). In order to model crude oil oxidation reactions accurately, an experimental workflow is introduced to obtain kinetic parameters for ISC chemical reaction models. An optimization algorithm assists to match the reaction model parameters to the experimental results, and this validated model is used to predict ignition of crude oil in porous media. Apparent activation energy is estimated from ramped temperature oxidation experiments under several heating rates, including 1.5, 2.0, 2.5, 3.0, 5, 10, 15, and 20 °C/min. These experiments are separated into a small heating rates group (1.5, 2.0, 2.5, 3.0 °/min) and large heating rates (5, 10, 15, 20 °/min). The results show that experiments with small heating rates capture the details of reaction kinetics such that the estimated activation energy is more accurate, with the validated simulation model able to make accurate predictions for this particular crude oil. After matching the kinetics parameters, we predict the ignition conditions as a function of the air flow rates and the heat loss rates. The ignition envelope indicates that the window for air flow rates to ignite the oil decreases if the heat loss rate is high. Greater heat losses require more thermal energy to be released from the reaction to overcome losses and for ignition to occur. This leads to a narrower range of ignition air flow rates due to convective heat transfer. The uncertainty quantification results provide a confidence region for the ignition envelope impacted by the threshold temperature of the ignition criterion. The novelty of this work is the description of optimized combustion reaction models with rigorous experimental verification and uncertainty quantification for reactive transport simulations.
In-situ combustion (ISC) is a technology used for enhanced oil recovery for heavy oil reservoirs. In two ISC field pilots conducted in 1970s to 1980s in Canada, 10-20% mole fraction of hydrogen (H2) was produced accidentally. This presents a potential opportunity for petroleum industry to contribute to the energy transition by producing hydrogen directly from petroleum reservoirs. However, most ISC experiments have reported no or negligible hydrogen production, and the reason remains unclear. To address this issue, this study focuses on hydrogen generation from bitumen through in-situ combustion gasification (ISCG) at a laboratory scale. CMG was used to simulate the ISCG process in a combustion tube. Kinetics from previous ISC experiments and reactions for hydrogen generation were incorporated in the models. Heavy oil, oxygen, and water were simultaneously injected into the tube at a certain temperature. The ranges of key parameters were varied and analyzed for their impact on hydrogen generation. The study found that maintaining a temperature above 400 °C is essential for hydrogen generation, with higher temperatures yielding higher hydrogen mole fractions. A maximum of 28% hydrogen mole fraction was obtained at a water-oxygen ratio of 0.0018:0.9882 (volume ratio at ambient conditions) and a temperature about 735 °C. Higher oxygen content was found to be favorable for hydrogen generation by achieving a higher temperature, while increasing nitrogen from 0 to 78% led to a decrease in hydrogen mole fraction from 28% to 0.07%. Hydrogen generation is dominated by coke gasification and water-gas shift reactions at low and high temperatures, respectively. This research provides valuable insights into the key parameters affecting hydrogen generation from bitumen at a lab scale. The potential for petroleum industry to contribute to energy transition through large-scale, low-cost hydrogen production from reservoirs is significant.
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