Search

Summary Reaction kinetics between calcite and acid systems have been studied using the rotating disk apparatus (RDA). However, simplifying assumptions have been made to develop the current equations used to interpret RDA experiments to enable solving them analytically in contrast to using numerical methods. Previous work has revealed inadequacies of some of these assumptions, which necessitates the use of a computational fluid dynamics (CFD) model to investigate their impact on the RDA results. The objectives of the current work are to develop a calibrated CFD and proxy model to simulate the reaction in the RDA and use this model to estimate the diffusion coefficient and the reaction rate coefficient of the reaction in the RDA. The present work developed the first calibrated CFD model to determine the diffusion coefficient and the reaction rate coefficient in the RDA with minimum assumptions in the hydrochloric acid (HCl) carbonate reaction. More specifically, the model relaxes the constant fluid properties, infinite acting reactor boundaries, and constant reaction surface area assumptions. The proxy model obtained results in reduced computational time with minimal compromise on accuracy. Finally, the proposed model showed an improvement of 63% in predicting the reaction kinetics between calcite and HCl compared to traditional methods.

We combine in situ environmental transmission electron microscopy (E-TEM) with automated image processing and statistical machine learning to uniquely formulate interpretable mathematical models an...

SummaryDesigning matrix-acid stimulation treatments in vuggy and naturally fractured carbonate reservoirs is a challenging problem in the petroleum industry. It is often difficult to physically model this process, and current mathematical models do not consider vugs or fractures. There is a significant gap in the literature for models that design and evaluate matrix-acid stimulation in vuggy and naturally fractured carbonate reservoirs. The objective of this work is to develop a new model to simulate matrix acidizing under field conditions in vuggy and naturally fractured carbonates.To obtain accurate and reliable simulation parameters, acidizing coreflood experiments were modeled using a reactive-flow simulator. A 3D radial field-scale model was used to study the flow of acid in the presence of vugs (pore spaces that are significantly larger than grains) and natural fractures (breaks in the reservoir that were formed naturally by tectonic events). The vugs’ size and distribution effects on acid propagation were studied under field conditions. The fracture length, conductivity, and orientation, and the number of fractures in the formation, were studied by the radial model. The results of the numerical simulation were used to construct Gaussian-process (GP)-based surrogate models for predicting acid propagation in vuggy and naturally fractured carbonates.Finally, the acid propagation in vuggy/naturally fractured carbonates was evaluated, as well.The simulation results of vuggy carbonates show that the presence of vugs in carbonates results in faster and deeper acid propagation in the formation when compared with homogeneous reservoirs at injection velocities lower than 8×10–4 m/s. Results also revealed that the size and density of the vugs have a significant impact on acid consumption and the overall performance of the acid treatment. The output of the fracture model illustrates that under field conditions, fracture orientations do not affect the acid-propagation velocity. The acid does not touch all of the fractures around the well. The GP model predictions have an accuracy of approximately 90% for both vuggy and naturally fractured cases. The vuggy/naturally fractured model simulations reveal that fractures are the main reason behind the fast acid propagation in these highly heterogeneous reservoirs.

Accurate short-term forecasts are indispensable for the integration of wind energy in power grids. On a wind farm, local wind conditions exhibit sizeable variations at a fine temporal resolution. Existing statistical models may capture the in-sample variations in wind behavior, but are often shortsighted to those occurring in the near future, that is, in the forecast horizon. The calibrated regime-switching method proposed in this paper introduces an action of regime dependent calibration on the predictand (here the wind speed variable), which helps correct the bias resulting from out-of-sample variations in wind behavior. This is achieved by modeling the calibration as a function of two elements: the wind regime at the time of the forecast (and the calibration is therefore regime dependent), and the runlength, which is the time elapsed since the last observed regime change. In addition to regime-switching dynamics, the proposed model also accounts for other features of wind fields: spatio-temporal dependencies, transport effect of wind and nonstationarity. Using one year of turbine-specific wind data, we show that the calibrated regime-switching method can offer a wide margin of improvement over existing forecasting methods in terms of both wind speed and power.

The calibration of computer models using physical experimental data has received a compelling interest in the last decade. Recently, multiple works have addressed the functional calibration of computer models, where the calibration parameters are functions of the observable inputs rather than taking a set of fixed values as traditionally treated in the literature. While much of the recent works on functional calibration was focused on estimation, the issue of sequential design for functional calibration still presents itself as an open question. Addressing the sequential design issue is thus the focus of this paper. We investigate different sequential design approaches and show that the simple separate design approach has its merit in practical use when designing for functional calibration. Analysis is carried out on multiple simulated and real world examples.

A growing research trend in additive manufacturing (AM) calls for layerwise anomaly detection as a step toward enabling real-time process control, in contrast to ex situ or postprocess testing and characterization. We propose a method for layerwise anomaly detection during laser powder-bed fusion (L-PBF) metal AM. The method uses high-speed thermal imaging to capture melt pool temperature and is composed of the following four-step anomaly detection procedure: (1) using the captured thermal images, a process signature of a just-fabricated layer is generated. Next, a signature difference is obtained by subtracting the process signature of that particular layer from a prespecified reference signature, (2) a screening step selects potential regions of interests (ROIs) within the layer that are likely to contain process anomalies, hence reducing the computational burden associated with analyzing the full layer data, (3) the spatial dependence of these ROIs is modeled using a Gaussian process model, and then pixels with statistically significant deviations are flagged, and (4) using the quantity and the spatial pattern of the flagged pixels as predictors, a classifier is trained and implemented to determine whether the process is in- or out-of-control. We validate the proposed method using a case study on a commercial L-PBF system custom-instrumented with a dual-wavelength imaging pyrometer for capturing the thermal images during fabrication.

Reaction kinetics between calcite and acid systems has been studied using the rotating disk apparatus (RDA). However, simplifying assumptions have been made to develop the current equations used to interpret RDA experiments to enable solving them analytically in contrast to using numerical methods. Experimental results revealed inadequacy of some of these assumptions, which necessitates the use of a computational fluid dynamics (CFD) model to investigate their impact on the RDA results. The objectives of the current work are threefold: (1) develop a CFD model to simulate the reaction in the RDA, (2) Identify the error associated with the assumptions in the original equations, and (3) develop a proxy model from the results that can accurately represent the reaction in the RDA. In developing the CFD model, the averaged-continuum approach was used to simulate the chemical reaction on the disk surface. Both Newtonian and non-Newtonian fluids were studied to investigate the adequacy of the equations’ assumptions. To validate the model, simulations were compared with experimental results. Experiments were run at 0.25, 0.5, 1, and 1.25M HCl with marble using the RDA at 250°F. Rotation speeds of 200, 400, 600, and 1,000 rpm were tested at each acid concentration. The diffusion coefficient was then calculated. Parameters of the CFD model were then adjusted to match the rock dissolved throughout the RDA experiments. The rock dissolved in the disk from the CFD model matched the results from the RDA experiments. The transition from mass-transfer to the kinetics-limited reaction behavior was captured by the CFD model. The velocity and viscosity profiles for both Newtonian and non-Newtonian fluids showed the effect of the container's boundaries on the flow. Results indicate that this effect is pronounced in the case of Newtonian fluids at high rotational speeds. Moreover, the impact of varying viscosities in the case of non-Newtonian fluids resulted in errors in estimating the reaction kinetics. Finally, a proxy model was obtained to reduce the computational time involved in accurately simulating the experiments. The present work developed the first CFD model to accurately evaluate reaction kinetics and diffusion coefficient in the RDA with minimum assumptions. More specifically, the model relaxes the infinite acting, constant fluid properties, and constant reaction surface area assumptions. Finally, the proxy model obtained results in reduced computational time with minimal compromise on accuracy.