Accurate Carbonate Pore System Characterization by Nuclear Magnetic Resonance and Micro-CT Techniques

Carbonate reservoir rocks usually have complex pore systems of broad size distributions, which determine many aspects of oil exploitation, from petrophysical properties to oil/water displacements. An accurate and complete description of these pore systems remains a challenge. A single technique often gives one measurement of complicated microscopic pore space. The new techniques (i.e., micro-CT and NMR) are utilized together with conventional methods (e.g., MICP, BET) to capture a more accurate and complete picture of pore structures. MICP measures the pore throat while the NMR T2 mainly measures the pore body. Micro-CT provides a 3D image of a limited sample size. Recently, NMR DDIF (decay due to diffusion in the internal field) for direct pore body size is extended from high to low magnetic field, which overcomes many limitations in pore system characterization. This study obtains pore throat size distributions from in-situ centrifuge capillary pressure and pore body size distributions from low field DDIF measurement and verifies them with micro-CT and BET/T2 in different types of carbonate rocks. The pore throat size distribution of the conventional sample is obtained from in-situ centrifuge capillary pressure. The major features of both macro and micro pore throat size distributions are captured. Pore size distributions are directly obtained from glass beads and carbonate rocks without calibration. Combined analysis of the pore size distribution from two methods reveals the underlying causes of their different petrophysical properties. The pore throat size distribution from in-situ centrifuge capillary pressure and pore size distribution from NMR DDIF can be employed to obtain a better understanding of conventional carbonate pore systems.

Modeling Cancer Immunoediting in Tumor Microenvironment with System Characterization through the Ising-model Hamiltonian

Background and Objective: Cancer Immunoediting (CI) describes the cellular-level interaction between tumor cells and the Immune System (IS) that takes place in the Tumor Micro-Environment (TME). CI is a highly dynamic and complex process comprising three distinct phases (Elimination, Equilibrium and Escape) wherein the IS can both protect against cancer development as well as, over time, promote the appearance of tumors with reduced immunogenicity. We present an agent-based model for the biological system in the TME, intended to simulate CI. Methods: Our model includes agents for tumor cells and for elements of the IS. The actions of these agents are governed by probabilistic rules, and agent recruitment (including cancer growth) is modeled via logistic functions. The system is formalized as an analogue of the Ising model from statistical mechanics to facilitate its analysis. The model was implemented in the Netlogo modeling environment and simulations were performed to verify, illustrate and characterize its operation. Results: Our model is capable of generating the three phases of CI; it requires only a couple of control parameters and is robust to these. We demonstrate how our simulated system can be characterized through the Ising-model energy function, or Hamiltonian, which captures the “energy” involved in the interaction between agents and presents it in clear and distinct patterns for the different phases of CI. Conclusions: The presented model is very flexible and robust, captures well the behaviors of the target system and can be easily extended to incorporate more variables such as those pertaining to different anti-cancer therapies. System characterization via the Ising-model Hamiltonian is a novel and powerful tool for a better understanding of CI and the development of more effective treatments.

Safe Operation Conditions of Electrical Power System Considering Power Balanceability Among Power Generators, Loads, and Storage Devices

The introduction of an energy storage system plays a vital role in the integration of renewable energy by keeping a stable operation and enhancing the flexibility of the power flow system, especially for an islanding microgrid which is not tied to a grid and for a self-contained microgrid which tries to stay independent from a grid as much as possible. To accommodate the effects of power fluctuations of distributed energy resources and power loads on power systems, a power flow assignment under power balance constraint is essential. However, due to power limitations of power devices, the capacity of storage devices, and power flow connections, the power balance may not be achieved. In this paper, we proposed a system characterization which describes the relation among power generators, power loads, power storage devices, and connections that must be satisfied for a system to operate by keeping SOC limitations of power storage devices. When we consider one power generator, one power load, and one power storage device connected at a single node, the generated energy by the generator minus the consumed energy by the load from some start time will increase/decrease the state of charge (SOC) for the storage device; hence, keeping SOC max/min limitations relies on whether the difference between the generated energy and the consumed energy stays within a certain range or not, which can be computed from the capacity Ess and other parameters. Our contribution in this paper is an extension and generalization of this observation to a system that consists of multiple fluctuating power generators, multiple fluctuating power loads, multiple storage devices, and connections that may not be a full connection between all devices. By carefully enumerating the connection-dependent flow paths of generated energy along the flow direction from generators to storages and loads, and enumerating the connection-dependent flow paths of consumed energy along the counter-flow direction from loads to storages and generators, we have formulated the increase/decrease of SOCs of storage devices caused by the imbalance between generated energy and consumed energy. Finally, considering the max/min limitations of SOCs and fluctuations of power generators and power loads, the conditions that the power generators and the power loads must have for SOCs of storage devices to maintain individual max/min limitations have been derived. The system characterization provides guidelines for a power flow system that can continue safe operation in the presence of power fluctuations. That is, in order for a system to have a feasible power flow assignment, there are the issues of how large the capacity of a power storage device should be, how large/small the maximum/minimum power/demand levels of the power generators and the power loads should be, and how the connection should be configured. Several examples using our system characterization are demonstrated to show the possible applications of our results.