Automated Drilling Variances Detection Through Smart Alarms System

In a drive to enhance drilling operational awareness, the Real-Time Operations Center (RTOC) has developed a State-of-the-Art event detection algorithm that consistently highlights the deviations of critical parameters by actively comparing real-time values against comprehensive physical models and alerting the users through a dashboard. The process relies on different levels of frequency and severity in order to detect events at their onset and prevent developing into a situation that compromises the operations. The first pillar of the solution consists of deterministic modelling of the expected values for a series of parameters in order to provide the basis for comparison and diagnostics. The main parameters sought to be modelled consist of the Standpipe Pressure, the Rotary Torque and the Hook load, which respectively are generated through individual methods taking into consideration actual conditions as well as relevant contextual data to ensure accuracy. The second pillar of the solution consists of visual alerts, triggered and displayed on a dashboard based on frequency and severity levels, as percentage of deviation from accepted operational envelope. The solution has been initially implemented during drilling operations where different issues were expected to take place, finding that whenever such occurrences took place, the algorithms were able to signal potential events in most of the cases. Some challenges were observed mainly due to sensor calibration and behavior since the expected model values not necessarily match reality, including residual pressure when the pumps are off or when the string is set on slips but the hook load values still present some variance. Also, it has been observed during transient periods where flow and rotation are changed drastically, that the stabilization to a steady state present with high variance, which has demanded the introduction of further logics within the algorithms to account for these effects and avoid the generation of false indications of issues. The solution has given encouraging results thus far in signaling different dysfunctions on the drilling process without the need of immediate human interpretation of data, which has allowed to move forward in the digitalization of operations, not only by timely signaling the onset of issues, but as well by providing the basis to further develop real time diagnosis of the problems to accelerate their resolution. The conception of the event detection based on deterministic real time analysis of individual channels against robust physical models from the existing digital twin solution has proven an immediate asset for operations on its own. By providing clear signaling of issues, while providing a solid framework to ultimately develop a diagnostic solution to translate a potential event into a proactive approach to support decision making process.

Storm surge hazard over Bengal delta: A probabilistic-deterministic modelling approach

Storm-surge induced coastal inundation constitutes a substantial threat to lives and properties along the vast coastline of the Bengal delta. Some of the deadliest cyclones in history made landfall in the Bengal delta region claiming more than half a million lives over the last five decades. Complex hydrodynamics and observational constraints have hindered the understanding of the risk of storm surge flooding of this low-lying (less than 5 m above mean sea level), densely populated (> 150M) mega-delta. Here, we generated and analysed a storm surge database derived from a large ensemble of 3600 statistically and physically consistent synthetic storm events and a high-resolution storm surge modelling system. The storm surge modelling system is developed based on a custom high-accuracy regional bathymetry enabling us to estimate the surges with high-confidence. From the storm surge dataset, we performed a robust probabilistic estimate of the storm surge extremes. Our ensemble estimate shows that there is a diverse range of water level extremes along the coast and the estuaries of the Bengal delta, with well-defined regional patterns. We confirm that the risk of inland storm surge flooding at a given return period is firmly controlled by the presence of coastal embankments and their height. We also conclude that about 10 % of the coastal population is living under the exposure of a 50-year return period inundation under current climate scenarios. In the face of ongoing climate change, which is likely to worsen the future storm surge hazard, we expect our flood maps to provide relevant information for coastal infrastructure engineering, risk zoning, resource allocation, as well as future research planning.

Memory shapes microbial populations

Correct decision making is fundamental for all living organisms to thrive under environmental changes. The patterns of environmental variation and the quality of available information define the most favourable strategy among multiple options, from randomly adopting a phenotypic state to sensing and reacting to environmental cues. Cellular memory—the ability to track and condition the time to switch to a different phenotypic state—can help withstand environmental fluctuations. How does memory manifest itself in unicellular organisms? We describe the population-wide consequences of phenotypic memory in microbes through a combination of deterministic modelling and stochastic simulations. Moving beyond binary switching models, our work highlights the need to consider a broader range of switching behaviours when describing microbial adaptive strategies. We show that memory in individual cells generates patterns at the population level coherent with overshoots and non-exponential lag times distributions experimentally observed in phenotypically heterogeneous populations. We emphasise the implications of our work in understanding antibiotic tolerance and, in general, bacterial survival under fluctuating environments.

Dynamics of Tuberculosis (TB) with Drug Resistance to First-Line Treatment and Leaky Vaccination: A Deterministic Modelling Perspective

A deterministic model was formulated and employed in the analysis of the dynamics of tuberculosis with a keen emphasis on vaccination and drug resistance as the first line of treatment. It was assumed that some of the susceptible population were vaccinated but with temporal immunity. This is due to the fact that vaccines do not confer permanent immunity. Moreover, part of the infected individual after treatment grows resistance to the drug. Infective immigrants were also considered to be part of the population. The basic reproductive number for the model is estimated using the next-generation matrix method. The equilibrium points of the TB model and their local and global stability were determined. It was established that if the basic reproductive number was less than unity R 0 < 1 , then the disease free equilibrium is stable and unstable if R 0 > 1 . Furthermore, we investigated the optimal prevention, treatment, and vaccination as control measures for the disease. As the objective functional was optimised, there have been a significant reduction in the number of infections and an increase in the number of recovery. The best control measure in combating tuberculosis infections is prevention and vaccination of the susceptible population.

Local turbulent kinetic energy modelling based on Lagrangian stochastic approach in CFD and application to wind energy

&lt;p&gt;In order to better integrate the underlying meteorological processes with the developing technologies within wind energy industry, acquiring relevant statistical information of air motion at a local place, and quantifying the subsequent uncertainty of involved parameters in the models, are fundamental tasks. Special emphasis should be made on the growing interest in energy production forecasting and modelling for wind energy developments that rises the issue of accounting for the uncertain nature of the local forecast. Taking this into consideration, we present the construction of an original stochastic model for the instantaneous turbulent kinetic energy at a given point of a flow, and we validate estimator methods on this model with observational data examples from annual historic of a 10 Hz anemometer wind measurements. &lt;br&gt;More precisely, starting from the viewpoint of Lagrangian modelling of the wind in the boundary layer, we establish a mathematical link between 3D+time computational fluid dynamics (CDF) models for turbulent near-wall flows and stochastic time series models by deriving a family of mean-field dynamics featuring the square norm of the turbulent velocity. Then, by approximating at equilibrium the characteristic nonlinear terms of the dynamics, we recover the so called Cox-Ingersoll-Ross stochastic model, which was previously suggested in the literature for modelling wind speed. Remarkably, our stochastic model for the instantaneous turbulent kinetic energy is parametrised by physical constants in CFD, which provides a more direct link between the stochastic nature of the underlying processes and the classical physics behind these phenomena. Nevertheless, these physical parameters may vary with the flow characteristics and situations, so we consider it relevant to adjust their values while constructing the forecasts. Such tuning of the physical parameters was previously proposed in the literature from a deterministic modelling context with RANS equations. We then propose a two-step procedure for the calibration of the parameters: a training stage where we construct a priori distribution for the parameter vector using direct methods and wind measurements, and a stage of refinement of the uncertainty distribution using Bayesian inference combined with Markov Chain Monte Carlo sample techniques. In particular, we show the accuracy of the calibration method and the performance of the calibrated model in predicting the wind distribution through the quantification of uncertainty.&lt;/p&gt;

Comparison of Various Growth Curve Models in Characterizing and Predicting Water Table Change after Intensive Mine Dewatering Is Discontinued in an East Central European Karstic Area

The modeling of karst water level fluctuations is a crucial task in the water resource management of vulnerable karstic areas. In the Transdanubian Range (East Central Europe, Hungary), from 1950 to 1990, coal and bauxite mining were carried out, with large amounts of karst water being extracted, thus lowering the water table by amounts ranging between 10 and 100 m. Since the cessation of mining activities in the early 1990s, the volume of natural recharge has exceeded the amount of dewatering, and the system has begun to return to its original undisturbed state. This apparently welcome development does, however, bring economic and technical engineering problems. The estimation and prediction of such water level changes is often tackled via the use of deterministic approaches, however, in the present case, it is also addressed with an alternative approach using trend estimation to monthly water level data from 107 karst water wells over the period 1990–2017. To approximate the change in karst water levels, (i) growth curve models were fitted to the monthly data, allowing the estimation of karst water levels, at least as far as 2030. Similarly, this was also done with (ii) deterministic modelling in order to describe the recovery process up to 2030. Specifically, measured and predicted values for karst water level were used to derive interpolated (kriged) maps to compare the forecasting power of the two approaches. Comparing the results of the trend analysis with those of the traditional deterministic modelling results, it is apparent that the two approaches predict similar spatial distribution of water levels, but slightly different future water level values.

Deterministic modelling of seismic waves in the Urgent Computing context: progress towards a short-term assessment of seismic hazard

&lt;p&gt;Seismic wave propagation is currently computationally prohibitive at high frequencies relevant for earthquake engineering or for civil protection purposes (up to 10 Hz). Developments of computational high-performance computing (HPC) infrastructures, however, will render routine executions of high-frequency simulations possible, enabling new approaches to assess seismic hazard - such as Seismic Urgent Computing (UC) in the immediate aftermath of an earthquake. The high spatial resolution of near-real time synthetic wavefields could complement existing live data records where dense seismic networks are present or provide an alternative to live data in regions with low coverage. However, time to solution for local near-field simulations accounting for frequencies above 1 Hz, as well as availability of substantial computational resources pose significant challenges that are incompatible with the requirements of decision makers. Moreover, the simulations require fine tuning of the parameters, as uncertainties in the underlying velocity model and in earthquake source information translate into uncertainties in final results. Estimating such uncertainties on ground motion proxies is non-trivial from a scientific standpoint, especially for the higher frequencies that remain an uncharted territory. In this talk we wish to address some of these key challenges and present our progress in the design and development of a prototype of a Seismic UC service. In the long run, we hope to demonstrate that deterministic modelling of ground motions can indeed in the future contribute to the short-term assessment of seismic hazard. &amp;#160;&lt;/p&gt;

Hybrid machine learning process improves operations in South Australian trial

TRILITY has developed a production cloud data management solution, KOIOS DatalytiX to provide hybrid machine learning applications and deterministic modelling tools to its operations, asset management, and engineering teams. Two optimiser applications for chemical coagulant prediction and filtration performance were deployed in the Riverland, South Australia at 10 conventional water treatment plants in 2018, to full production in 2019. One year later the impacts on the optimisation behaviour of operations teams, process performance and trihalomethane (THM) reduction outcomes are discussed.