Effects of Process and Formulation Parameters on Submicron Polymeric Particles Produced by a Rapid Emulsion-Diffusion Method

Emulsification-diffusion method is often used to produce polymeric nanoparticles. However, their numerous and/or lengthy steps make it difficult to use widely. Thus, a modified method using solvent blends (miscible/partially miscible in water, 25–100%) as the organic phases to overcome these disadvantages and its design space were investigated. To further simplify the process, no organic/aqueous phase saturation and no water addition after the emulsification step were performed. Biodegradable (PLGA) or pH-sensitive (Eudragit® E100) nanoparticles were robustly produced using low/medium shear stirring adding dropwise the organic phase into the aqueous phase or vice versa. Several behaviors were also obtained: lowering the partially water-miscible solvent ratio relative to the organic phase or the poloxamer-407 concentration; or increasing the organic phase polarity or the polyvinyl alcohol concentration produced smaller particle sizes/polydispersity. Nanoparticle zeta potential increased as the water-miscible solvent ratio increased. Poloxamer-407 showed better performance to decrease the particle size (~50 nm) at low concentrations (≤1%, w/v) compared with polyvinyl alcohol at 1–5% (w/v), but higher concentrations produced bigger particles/polydispersity (≥600 nm). Most important, an inverse linear correlation to predict the particle size by determining the solubility parameter was found. A rapid method to broadly prepare nanoparticles using straightforward equipment is provided.

Intelligent Microfluidics Research on Relative Permeability Measurement and Prediction of Two-Phase Flow in Micropores

Relative permeability is a key index in resource exploitation, energy development, environmental monitoring, and other fields. However, the current determination methods of relative permeability are inefficient and invisible without considering wetting order and pore structure characteristics either. In this study, microfluidic experiments were designed for figuring out key factors impacting on the two-phase relative permeability. The optimized intelligent image recognition was established for saturation extraction. The deep learning was conducted for the prediction of two-phase permeability based on the inputs from microfluidic experiments and image recognition and optimized. Results revealed that phase saturation, wetting order, and pore topology were the key factors influencing the two-phase relative permeability, with the importance of 38.22%, 34.84%, and 26.94%, respectively. The deep learning-based relative permeability model performed well, with MSE < 0.05 and operational efficiency of 3 ms/epoch. Aiming at relative permeability model optimization, on the one hand, the dividing ratio of training set and testing set for flooding phase relative permeability prediction achieved the highest prediction accuracy at 7 : 3, while that for displaced phase was 6 : 4. On the other hand, tanh() activation function performed 40% more accurate than the sigmoid() activation function.

Case Studies of Digitalized Locate the Remaining Oil Workflows Powered by Hybrid Data & Physics Methods

Mature fields already account for about 70% of the hydrocarbon liquids produced globally. Since the average recovery factor for oil fields is 30 to 35%, there is substantial quantities of remaining oil at stake. Conventional simulation-based development planning approaches are well established, but their implementation on large, complex mature oil fields remains challenging given their resource, time, and cost intensity. In addition, increased attention towards reduce carbon emissions makes the case for alternative, computationally-light techniques, as part of a global digitalisation drive, leveraging modern analytics and machine learning methods. This work describes a modern digital workflow to identify and quantify by-passed oil targets. The workflow leverages an innovative hybrid physics-guided data-driven, which generates historical phase saturation maps, forecasts future fluid movements and locate infill opportunities. As deliverables, a fully probabilistic production forecast is obtained for each drilling location, as a function of the well type, its geometry, and position in the field. The new workflow can unlock remaining potential of mature fields in a shorter time-frame and generally very cost-effectively compared to the advanced dynamic reservoir modelling and history-match workflows. Over the last 5 years, this workflow has been applied to more than 30 mature oil fields in Europe, Africa, the Middle East, Asia, Australia, and New Zealand. Three case studies’ examples and application environments of applied digital workflow are described in this paper. This study demonstrates that it is now possible to deliver digitalized locating the remaining oil projects, capturing the full uncertainty ranges, including leveraging complex multi-vintage spatial 4D datasets, providing reliable non-simulation physics-compliant data-driven production forecasts within weeks.

Machine Learning for Capillary Pressure Estimation

Summary Capillary pressure plays an essential role in controlling multiphase flow in porous media and is often difficult to be estimated at subsurface conditions. The Leverett capillary pressure function J provides a convenient tool to address this shortcoming; however, its performance remains poor where there is a large scatter in the scaled data. Our aim, therefore, was to reduce the gaps between J curves and to develop a method that allows accurate scaling of capillary pressure. We developed two mathematical expressions based on permeability and porosity values of 214 rock samples taken from North America and the Middle East. Using the values as grouping features, we used pattern-recognition algorithms in machine learning to cluster the original data into different groups. In each wetting phase saturation, we were able to quantify the gaps between the J curves by determining the ratio of the maximum J to the minimum J. Graphical maps were developed to identify the corresponding group for a new rock sample after which the capillary pressure is estimated using the average J curve of the identified group and the permeability and porosity values of the rock sample. This method also provides better performance than the flow zone indicator (FZI) approach. The proposed technique was validated on six rock types and has successfully generated average capillary pressure curves that capture the trends and values of the experimentally measured data by mercury injection. Moreover, the proposed methodology in this study provides an advanced and a machine-learning-oriented approach for rock typing. In this paper, we provide a reliable and easy-to-use method for capillary pressure estimation in the absence of experimentally measured data by mercury injection.

Development of a Thermal Stability Method for Phase Appearance and Disappearance Handling in Thermal Compositional Simulators

Thermal compositional simulators rely heavily on multicomponent, multiphase flash calculations for a variety of reasons, including reservoir and wellbore initialization, phase appearance and disappearance, and property calculation. In a mass variable formulation, an isenthalpic flash is used for phase split computation, phase saturation update, component mole fraction update in different phases, and temperatures. A natural variable formulation utilizes an isothermal flash mainly for phase appearance and disappearance as well as computation of component mole fractions in appearing phases. Multiphase multicomponent isothermal flash calculations cannot be performed in narrow boiling systems which are very common in the simulation of thermal EOR operations such as Steam-Assisted Gravity Drainage (SAGD) or Steam Flooding (SF). In a narrow boiling point system, pressure and temperature are not linearly independent, and an isothermal flash will fail. In addition, flash calculations are computationally expensive, and reservoir simulators use different techniques to perform them as little as possible. A new thermal stability check has been developed that can be used in thermal compositional simulators and replaces an isothermal flash calculation. The new stability check quickly determines the phase state of a fluid sample and can be used as an initial guess for mole fraction of a phase appearing in the next simulation cycle. In this method, primary variables of the simulator are used as input for the stability check immediately after the nonlinear solver update so that computation of global mole fractions is not required. The new stability check can also be used in separator and isenthalpic flash calculations to determine the phase state of a fluid. An algorithm is provided, covering all different transitions of phase states in a thermal compositional simulator. The proposed algorithm is significantly faster than a flash calculation and saves simulation time spent in this calculation, hence the overall speed up is case dependent. The new stability check is simple, computationally inexpensive, and robust. It can be used for multicomponent and single-component systems, and we tested it rigorously against real field and synthetic models. The new thermal stability check always predicts the number of phase states correctly and never fails. In this paper, we demonstrate a thermal compositional simulation that is run without performing a single flash calculation.

Lipid Polymorphism of the Subchloroplast—Granum and Stroma Thylakoid Membrane–Particles. II. Structure and Functions

In Part I, by using 31P-NMR spectroscopy, we have shown that isolated granum and stroma thylakoid membranes (TMs), in addition to the bilayer, display two isotropic phases and an inverted hexagonal (HII) phase; saturation transfer experiments and selective effects of lipase and thermal treatments have shown that these phases arise from distinct, yet interconnectable structural entities. To obtain information on the functional roles and origin of the different lipid phases, here we performed spectroscopic measurements and inspected the ultrastructure of these TM fragments. Circular dichroism, 77 K fluorescence emission spectroscopy, and variable chlorophyll-a fluorescence measurements revealed only minor lipase- or thermally induced changes in the photosynthetic machinery. Electrochromic absorbance transients showed that the TM fragments were re-sealed, and the vesicles largely retained their impermeabilities after lipase treatments—in line with the low susceptibility of the bilayer against the same treatment, as reflected by our 31P-NMR spectroscopy. Signatures of HII-phase could not be discerned with small-angle X-ray scattering—but traces of HII structures, without long-range order, were found by freeze-fracture electron microscopy (FF-EM) and cryo-electron tomography (CET). EM and CET images also revealed the presence of small vesicles and fusion of membrane particles, which might account for one of the isotropic phases. Interaction of VDE (violaxanthin de-epoxidase, detected by Western blot technique in both membrane fragments) with TM lipids might account for the other isotropic phase. In general, non-bilayer lipids are proposed to play role in the self-assembly of the highly organized yet dynamic TM network in chloroplasts.

PERMEABILITY MODELS FOR TWO-PHASE FLOW IN FRACTAL POROUS-FRACTURE MEDIA WITH THE TRANSFER OF FLUIDS FROM POROUS MATRIX TO FRACTURE

Study of transport mechanism of two-phase flow through porous-fracture media is of considerable importance to deeply understand geologic behaviors. In this work, to consider the transfer of fluids, the analytical models of dimensionless relative permeabilities for the wetting and non-wetting phases flow are proposed based on the fractal geometry theory for porous media. The proposed models are expressed as functions of micro-structural parameters of the porous matrix and fracture, such as the fractal dimension ([Formula: see text] for pore area, the fractal dimensions [Formula: see text] for wetting phase and for non-wetting phase, porosity ([Formula: see text], the total saturations ([Formula: see text], the porous matrix saturation ([Formula: see text] of the wetting and non-wetting phases, fractal dimension ([Formula: see text] for tortuosity of tortuous capillaries, as well as the ratio ([Formula: see text] of the maximum pore size in porous matrix to fracture aperture. The ratio ([Formula: see text] has a significant impact on the relative permeabilities and total saturations of wetting phases. The results reveal that the flow contribution of wetting phase from the porous matrix to both the seepage behavior of the fracture and total wetting phase saturation can be neglected as [Formula: see text]. The models may shed light on the fundamental mechanisms of the wetting and non-wetting phase flow in porous-fracture media with fluid transfer.