ABSTPACT Traditionally, air requirements during an air drilling operation involve the utilization of the Angel's chart(s). The values from the mentioned chart(s) are generally 20-25% below the acutal field's requirements/needs. Recent studies in this area have provided more reliable results, however, the tedious calculations, the lack of proper charts, and the absence of the user-friendly programs have prevented their practical applications. The authors of this paper have developed a simplified model that takes into account both the material balance and momentum balance for the annulus. Air and cuttings have been treated independently in these balances. Multiphase momentum balance equation is adopted. This model will also accommodate the air lifting capacity in the annulus to be the dominant factor in deciding the air requirements.

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This project used predictive analytics and machine learning-based modeling to detect drilling anomalies, namely stuck pipe events. Analysis focused on historical drilling data and real-time operational data to address the limitations of physics-based modeling. This project was designed to enable drilling crews to minimize downtime and non-productive time through real-time anomaly management. The solution used data science techniques to overcome data consistency/quality issues and flag drilling anomalies leading to a stuck pipe event. Predictive machine learning models were deployed across seven wells in different fields. The models analyzed both historical and real-time data across various data channels to identify anomalies (difficulties that impact non-productive time). The modeling approach mimicked the behavior of drillers using surface parameters. Small deviations from normal behavior were identified based on combinations of surface parameters, and automated machine learning was used to accelerate and optimize the modeling process. The output was a risk score that flags deviations in rig surface parameters. During the development phase, multiple data science approaches were attempted to monitor the overall health of the drilling process. They analyzed both historical and real-time data from torque, hole depth and deviation, standpipe pressure, and various other data channels. The models detected drilling anomalies with a harmonic model accuracy of 80% and produced valid alerts on 96% of stuck pipe and tight hole events. The average forewarning was two hours. This allowed personnel ample time to make corrections before stuck pipe events could occur. This also enabled the drilling operator to save the company upwards of millions of dollars in drilling costs and downtime. This project introduced novel data aggregation and deep learning-based normal behavior modeling methods. It demonstrates the benefits of adopting predictive analytics and machine learning in drilling operations. The approach enabled operators to mitigate data issues and demonstrate real-time, high-frequency and high-accuracy predictions. As a result, the operator was able to significantly reduce non-productive time.

Drilling fluid losses while drilling a mature cretaceous limestone reservoir unit (Formation A) has been worsening over years with reservoir depletion and lack of pressure support. New drilling methods were needed to eliminate or reduce total losses and the associated non-productive time with them. Nitrified Managed Pressure Drilling proposed to help in mitigating losses and reducing non-productive-time. This paper explains the challenge, details the solution that was proposed to tackle, and discusses the results of the application. Nitrified Managed Pressure Drilling (MPD) decreases the Equivalent Circulation Density (ECD) below the lowest possible static mud weight (water) and at the same time deals safely with any unintended hydrocarbon influxes while drilling the reservoir 6″ hole section. The well data was analysed and modelled with different Nitrogen pumping rates and Surface Back Pressure (SBP) to determine the best rates that a mitigates losses but at the same time prevent hydrocarbon influxes. A closed-Loop drilling system proposed utilizing rotating control device, a separation package, and locally produced membrane Nitrogen allowed to manage the annular hydraulic pressure profile accordingly and mitigate the total losses scenario eliminating the wait on water time Rigorous planning and disciplined execution have led to safe and successful conclusion with no QHSE issues encountered. The designed Nitrified Managed Pressure Drilling solution succeeded in preventing the drilling fluid losses in the reservoir section by reducing the overbalance pressure of the drilling mud from 700 psi to 250 psi, which resulted in the elimination of 3 days of the rig's non-productive-time related to waiting on water. The closed-loop system coupled with a precise data acquisition and monitoring system has helped in maintaining a slight overbalance condition over the reservoir preventing any unintended hydrocarbon influxes to the surface. The lessons learned captured from this operation have contributed to the optimization of the Nitrified MPD in (Formation A) and to the overall MPD implementation in ADNOC fields. This paper displays the first application of nitrified managed pressure drilling in the United Arab Emirates. The equipment design and planning have accounted for many different scenarios, as this type of drilling technology enables more precise wellbore pressure management with less interruptions to drilling ahead

Scientific evidence predicts significant climate changes in the future and the world faces warmer days with extreme precipitation. The purpose of this study is to investigate the effect of predicted climate changes until 2100 on the hygrothermal behavior of well-insulated residential timber buildings are being constructed recently in Germany according to the new low and zero energy standards. Considering the facts that wood and timber constructions are sensitive to moisture and moisture changes and these buildings should have a service life of 50 to 100 years, the hygrothermal performance of several exterior wall assemblies has been analyzed by means of coupled heat and moisture transfer numerical model in this period. Predicted climate data -by the end of the century- for several cities in different Germany’s hygrothermal regions, extracted from climate service centers’ databases and applied as an outdoor condition. The article discusses the response of a number of buildings envelops exposed to these exterior environments. Accordingly, critical aspects of moisture performance regarding the risk of mold growth and fungal decay were in each assembly analyzed. In this regard, relevant solutions were suggested aiming construction of more resilient timber structures to climate changes.