In-situ Combustion Simulation from Laboratory to Field Scale

In-situ combustion simulation from laboratory to field scale has always been challenging, due to difficulties in deciding the reaction model and Arrhenius kinetics parameters, together with erroneous results observed in simulations when using large-sized grid blocks. We present a workflow of successful simulation of heavy oil in-situ combustion process from laboratory to field scale. We choose the ongoing PetroChina Liaohe D block in-situ combustion project as a case of study. First, we conduct kinetic cell (ramped temperature oxidation) experiments, establish a suitable kinetic reaction model, and perform corresponding history match to obtain Arrhenius kinetics parameters. Second, combustion tube experiments are conducted and history matched to further determine other simulation parameters and to determine the fuel amount per unit reservoir volume. Third, we upscale the Arrhenius kinetics to the upscaled reaction model for field-scale simulations. The upscaled reaction model shows consistent results with different grid sizes. Finally, field-scale simulation forecast is conducted for the D block in-situ combustion process using computationally affordable grid sizes. In conclusion, this work demonstrates the practical workflow for predictive simulation of in-situ combustion from laboratory to field scale for a major project in China.

A Data Driven Artificial Intelligence Framework for Hydrogen Production Optimization in Waterflooded Hydrocarbon Reservoir

Hydrogen has become a very promising green energy source that can be easily stored and transported, and it has the potential to be utilized in a variety of applications. Hydrogen, as a power source, has the benefits of being easily transportable and stored over long periods of times, and does not lead to any carbon emissions related to the utilization of the power source. Thermal EOR methods are among the most commonly used recovery methods. They involve the introduction of thermal energy or heat into the reservoir to raise the temperature of the oil and reduce its viscosity. The heat makes the oil mobile and assists in moving it towards the producer wells. The heat can be added externally by injecting a hot fluid such as steam or hot water into the formations, or it can be generated internally through in-situ combustion by burning the oil in depleted gas or waterflooded reservoirs using air or oxygen. This method is an attractive alternative to produce cost-efficiently significant amounts of hydrogen from these depleted or waterflooded reservoirs. A major challenge is to optimize injection of air/oxygen to maximize hydrogen production via ensuring that the in-situ combustion sufficiently supports the breakdown of water into hydrogen molecules. In-situ combustion or fireflood is a method consisting of volumes of air or oxygen injected into a well and ignited. A burning zone is propagated through the reservoir from the injection well to the producing wells. The in-situ combustion creates a bank of steam, gas from the combustion process, and evaporated hydrocarbons that drive the reservoir oil into the producing wells. There are three types of in-situ combustion processes: dry forward, dry reverse and wet forward combustion. In a dry forward process only air is injected and the combustion front moves from the injector to the producer. The wet forward injection is the same process where air and water are injected either simultaneously or alternating. Artificial intelligence (AI) practices have allowed to significantly improve optimization of reservoir production, based on observations in the near wellbore reservoir layers. This work utilizes a data-driven physics-inspired AI model for the optimization of hydrogen recovery via the injection of oxygen, where the injection and production parameters are optimized, minimizing oxygen injection while maximizing hydrogen production and recovery. Multiple physical and data-driven models and their parameters are optimized based on observations with the objective to determine the best sustainable combination. The framework was examined on a synthetic reservoir model with multiple injector and producing wells. Historical injection and production were available for a time period of three years for various oxygen injection and hydrogen production levels. Various time-series deep learning network models were investigated, with random forest time series models incorporating a modified mass balance – reaction kinetics model for in-situ combustion performing most effectively. A robust global optimization approach, based on an artificial intelligence genetic optimization, allows for simultaneously optimization of an injection pattern and uncertainty quantification. Results indicate potential for significant reduction in required oxygen injection volumes, while maximizing hydrogen recovery. This work represents a first and innovative approach to enhance hydrogen recovery from waterflooded reservoirs via oxygen injection. The data-driven physics inspired AI genetic optimization framework allows to optimize oxygen injection while maximizing hydrogen production.

Use of two vertical injectors in place of a horizontal injector to improve the efficiency and stability of THAI in situ combustion process for producing heavy oils

The current commercial technologies used to produce heavy oils and bitumen are carbon-, energy-, and wastewater-intensive. These make them to be out of line with the global efforts of decarbonisation. Alternative processes such as the toe-to-heel air injection (THAI) that works as an in situ combustion process that uses horizontal producer well to recover partially upgraded oil from heavy oils and bitumen reservoirs are needed. However, THAI is yet to be technically and economically well proven despite pilot and semi-commercial operations. Some studies concluded using field data that THAI is a low-oil-production-rate process. However, no study has thoroughly investigated the simultaneous effects of start-up methods and wells configuration on both the short and long terms stability, sustainability, and profitability of the process. Using THAI validated model, three models having a horizontal producer well arranged in staggered line drive with the injector wells are simulated using CMG STARS. Model A has two vertical injectors via which steam was used for pre-ignition heating, and models B and C each has a horizontal injector via which electrical heater and steam were respectively used for pre-ignition heating. It is found that during start-up, ultimately, steam injection instead of electrical heating should be used for the pre-ignition heating. Clearly, it is shown that model A has higher oil production rates after the increase in air flux and also has a higher cumulative oil recovery of 2350 cm3 which is greater than those of models B and C by 9.6% and 4.3% respectively. Thus, it can be concluded that for long-term projects, model A settings and wells configuration should be used. Although it is now discovered that the peak temperature cannot in all settings tell how healthy a combustion front is, it has revealed that model A does indeed have far more stable, safer, and efficient combustion front burning quality and propagation due to the maintenance of very high peak temperatures of mostly greater than 600 °C and very low concentrations of produced oxygen of lower than 0.4 mol% compared to up to 2.75 mol% in model C and 1 mol% in model B. Conclusively, since drilling of, and achieving uniform air distribution in horizontal injector (HI) well in actual field reservoir are costly and impracticable at the moment, and that electrical heating will require unphysically long time before mobilised fluids reach the HP well as heat transfer is mainly by conduction, these findings have shown decisively that the easy-and-cheaper-to-drill two vertical injector wells configured in a staggered line drive pattern with the horizontal producer should be used, and steam is thus to be used for pre-ignition heating.