Laws and principles governing fluid flow in porous media.

Currently, it is impossible to imagine the petroleum industry without utilising numerical simulation methods for reservoir analysis and production. Investigating the petroleum reservoir performance calls for a comprehensive understanding of the physical processes that govern flow transport in porous media. Irrespective of the goals of the analysis whether pertaining to core investigations, reservoir simulation and planning of enhanced recovery methods, flow processes obey the same physical laws. In this paper, the basic laws and principles describing fluid flow and flow patterns manifesting in subsurface porous media are discussed. Appreciating the applicability and limitations of the physical laws allows us to develop and employ modelling methods for flow behaviour analysis and reservoir performance prediction which is the main goal of petroleum engineering. Concluding, we present the black-oil model, the compositional model and the complex models as applied to petroleum field applications.

New insights from shale gas production at the microscopic scale.

Until recently, natural gas encountered in tight shales, which provided the source and seal of the gas, was considered uneconomical to produce. Although unconventional formations may be as porous as other reservoir rocks, their exceedingly small pore sizes and low permeability render them resistant to gas movement. Considering their importance to gas transport, we outline the characteristics of shale rocks, the mechanisms of Fickian and Knudsen diffusion as well as Klinkenberg's permeability. Given the challenges in unlocking natural gas from tight formations, various techniques such as the generation of artificial fractures and the introduction of pressurised fluids are detailed. To identify the parameters which govern natural gas production, we propose a computational porous rock model inspired from an actual image of a shale formation. The solution of the conservation of mass, momentum and energy equations appear to adequately capture the physics of gas transport at the microscopic level. Permitting the comparison between numerical and analytical gas velocity results, the validation framework we developed, demonstrates good agreement of numerical with theoretical findings. Gas pressure and velocity results point to the importance of pore throats, shale permeability and pressure maintenance in dislodging gas from the shale formations.

World's largest natural gas leak from nord stream pipeline estimated at 478,000 tonnes.

Methane is a potent heat trapping gas believed to account for 30% of the observed global warming to-date. At a capacity of 110 bcm/year, the Nord Stream (NS) pipeline corridor measuring 1,153mm in internal diameter and stretching 1,224km from Russia to Germany is the biggest in the world. The explosions that NS sustained in September, 2022, in the Baltic Sea, have unleashed the largest single methane gas source in recent memory. Over the course of 7 days, our transient multiphase pipeline model has estimated that the gas leaks from 3 lines pumped 478,000 tonnes of methane into the atmosphere. A range of pipeline shut-in pressures as a function of leakage time deduced an envelope of gas volume that matched the timeline of observed outflows. Interestingly, the methane gas that escaped from the damaged threads amounted to the CO2 equivalent emitted by concrete sufficient to build about 27 Burj Khalifa towers.

Deploying deep learning to estimate the abundance of marine debris from video footage.

The insatiable desire of society for plastic goods has led to synthetic materials becoming omnipresent in the marine environment. In attempting to address the problem of plastic pollution, we propose an image classifier based on the YOLOv5 deep learning tool that is able to classify and localize marine debris and marine life in images and video recordings. Utilizing the region of interest line and the centroid tracking counting methods, the image classifier was able to count marine debris and fish displayed in video footage. Results revealed that, with a counting accuracy of 79 %, the centroid tracking method proved more efficient thanks to its ability to trace the geometric center of the bounding box of detected marine litter. Remarkably, the proposed method achieved a mean average precision of 89.4 % when validated on nine categories of objects. Finally, its impact can be enhanced substantially if integrated into other surveying methods or applications.

A new paradigm for estimating the prevalence of plastic litter in the marine environment.

The intelligent method proposed herein is formulated on a deep learning technique which can identify, localise and map the shape of plastic debris in the marine environment. Utilising images depicting plastic litter from six beaches in Cyprus, the developed tool pointed to a plastic litter density of 0.035 items/m2. Extrapolated to the entire shorelines of the island, the intelligent approach estimated about 66,000 plastic articles weighting a total of ≈1000 kg. Besides deducing the plastic litter density, the dimensions of all documented plastic litter were determined with the aid of the OpenCV Contours image processing tool. Results revealed that the dominant object length ranged between 10 and 30 cm which is in agreement with the length of common plastic litter often spoiling these coastlines. Concluding, only in-situ visual scan sample surveys and no manual collection means were used to predict the density and the dimensions of the plastic litter.

An intelligent way for discerning plastics at the shorelines and the seas.

Irrespective of how plastics litter the coastline or enter the sea, they pose a major threat to birds and marine life alike. In this study, an artificial intelligence tool was used to create an image classifier based on a convolutional neural network architecture that utilises the bottleneck method. The trained bottleneck method classifier was able to categorise plastics encountered either at the shoreline or floating at the sea surface into eight distinct classes, namely, plastic bags, bottles, buckets, food wrappings, straws, derelict nets, fish, and other objects. Discerning objects with a success rate of 90%, the proposed deep learning approach constitutes a leap towards the smart identification of plastics at the coastline and the sea. Training and testing loss and accuracy results for a range of epochs and batch sizes have lent credibility to the proposed method. Results originating from a resolution sensitivity analysis demonstrated that the prediction technique retains its ability to correctly identify plastics even when image resolution was downsized by 75%. Intelligent tools, such as the one suggested here, can replace manual sorting of macroplastics from human operators revealing, for the first time, the true scale of the amount of plastic polluting our beaches and the seas.

Identifying floating plastic marine debris using a deep learning approach.

Estimating the volume of macro-plastics which dot the world's oceans is one of the most pressing environmental concerns of our time. Prevailing methods for determining the amount of floating plastic debris, usually conducted manually, are time demanding and rather limited in coverage. With the aid of deep learning, herein, we propose a fast, scalable, and potentially cost-effective method for automatically identifying floating marine plastics. When trained on three categories of plastic marine litter, that is, bottles, buckets, and straws, the classifier was able to successfully recognize the preceding floating objects at a success rate of ≈ 86%. Apparently, the high level of accuracy and efficiency of the developed machine learning tool constitutes a leap towards unraveling the true scale of floating plastics.

Reproducing the Hemoglobin Saturation Profile, a Marker of the Blood Oxygenation Level Dependent (BOLD) fMRI Effect, at the Microscopic Level.

The advent of functional MRI in the mid-1990s has catalyzed progress pertaining to scientific discoveries in neuroscience. With the prospect of elucidating the physiological aspect of the Blood Oxygenation Level Dependent (BOLD) effect we present a computational capillary-tissue system capable of mapping venous hemoglobin saturation- a marker of the BOLD hemodynamic response. Free and facilitated diffusion and convection for hemoglobin and oxygen are considered in the radial and axial directions. Hemoglobin reaction kinetics are governed by the oxyhemoglobin dissociation curve. Brain activation, mimicked by dynamic transitions in cerebral blood velocity (CBv) and oxidative metabolism (CMRO2), is simulated by normalized changes in m = (ΔCBv/CBv)/(ΔCMRO2/CMRO2) of values 2, 3 and 4. Venous hemoglobin saturation profiles and peak oxygenation results, for m = 2, based upon a 50% and a 25% increase in CBv and CMRO2, respectively, lie within physiological limits exhibiting excellent correlation with the BOLD signal, for short-duration stimuli. Our analysis suggests basal CBv and CMRO2 values of 0.6 mm/s and 200 µmol/100g/min. Coupled CBv and CMRO2 responses, for m = 3 and m = 4, overestimate peak hemoglobin saturation, confirming the system's responsiveness to changes in hematocrit, CBv and CMRO2. Finally, factoring in neurovascular effects, we show that no initial dip will be observed unless there is a time delay in the onset of increased CBv relative to CMRO2.

Cerebral oxygenation and optimal vascular brain organization.

The cerebral vascular network has evolved in such a way so as to minimize transport time and energy expenditure. This is accomplished by a subtle combination of the optimal arrangement of arteries, arterioles and capillaries and the transport mechanisms of convection and diffusion. Elucidating the interaction between cerebral vascular architectonics and the latter physical mechanisms can catalyse progress in treating cerebral pathologies such as stroke, brain tumours, dementia and targeted drug delivery. Here, we show that brain microvascular organization is predicated on commensurate intracapillary oxygen convection and parenchymal diffusion times. Cross-species grey matter results for the rat, cat, rabbit and human reveal very good correlation between the cerebral capillary and tissue mean axial oxygen convective and diffusion time intervals. These findings agree with the constructal principle.