Taft College

Abstract The studied case addresses oil production decline in a field characterized by complex geology and challenging waterflood monitoring. A notable example is the oil rim reservoir, where water mobility significantly exceeds oil mobility due to high relative permeability. Despite low drawdown values at horizontal producers, partial aquifer support, and existing pressure maintenance facilities, oil production rates decline sharply. The objective of the study was to reconstruct the history, pace, and future trends of formation pressure decline and to propose waterflood optimization strategies. A machine learning-based approach was applied to design these activities and enhance oil recovery. Conventional production history was supplemented with deconvolved cross-well interference signals derived from long-term pressure records collected by permanent downhole gauges. Modern dynamic data interpretation techniques were used to assess dynamic skin factors and pressure depletion patterns. In addition, reservoir permeability and coning gradients were calculated, and open-hole logs were reinterpreted to identify geological features influencing well performance. Machine learning models trained on historical data uncovered subtle reservoir behavior and supported decision-making in areas such as waterflood monitoring, stimulation potential, and pressure maintenance. The study made extensive use of AI for well and reservoir analysis, contributing to the development of new methodologies for extracting true mechanical skin factor in composite environments, predicting water encroachment based on skin factor dynamics, and calculating optimal drawdowns for horizontal producers. Cross-well pressure transient analysis confirmed that the pay zone is connected to an aquifer—even during early production stages when water had not yet reached the wells. A large aquifer at the base and edges of the pay zone creates a composite system that can give the illusion of high skin around producers. This effect was carefully accounted for when calculating the true mechanical skin factor, which remained elevated—indicating potential for near-wellbore stimulation. A decreasing skin factor over time was found to be a reliable indicator of approaching water encroachment. This insight enabled preemptive measures, such as redistributing injection volumes, to delay water breakthrough and reduce the risk of increased operating costs or expensive remediation. The study introduced an empirical method to calculate optimal drawdown pressure values for each well individually and recommended redirecting injection efforts toward zones with more rapid pressure decline. Most of the proposed activities—including adjustments to injection and production targets, as well as targeted reservoir stimulations—were successfully implemented in the field. The observed production increase aligned closely with the study's forecasts, confirming the effectiveness of the proposed approach.

This chapter presents some of the possibilities and approaches that are used in the application of AI (artificial intelligence) and AR (augmented reality) in the new learning environments. AI will add another dimension to distance learning or eLearning that in some cases already includes AR (augmented reality) virtual learning environments. Because of this advent in available technology and the impact it will have on learning, assessment of newly structured parameters and their impact on student outcomes is crucial when measuring student learning. For some of us there might be a concern about the domination of AI as seen in the movie The Terminator, but we can take ease in the notion that it is not only AI versus humans. A new version of human augmented intelligence (HI) is being developed as we speak.

Surveys were sent to a large representative sample of public school principals in the United States asking if they had received leadership coaching. Comparison of responses to actual numbers of principals indicates that the sample represents the first national study of principal leadership coaching. Results indicate that approximately 50% of all principals have received or currently receive coaching. In general, this same percentage applies to most demographic categories. Respondents also rated components found to be of consequence to coaching success and these were compared with characteristics of the coaching relationship. Respondents overwhelmingly found coaching to be supportive and beneficial to them and believed it contributed to increasing student achievement. Recommendations include the expansion of leadership coaching programs and further study into specifics of the coaching relationship.

Cetacean-oriented tourism has experienced rapid growth in the last two decades, accompanied by increasing concerns about the effects of tourism on target species. Here, we used shore-based observations of dusky dolphin (Lagenorhynchus obscurus) groups near Kaikoura, New Zealand, and generalised additive models to quantify changes in group speed, reorientation and linearity relative to natural and anthropogenic factors. Summer months were characterised by slower speeds and more changes in direction than winter months. Group speeds were higher at the beginning and end of the day, with slower speeds at midday. Speeds were slower when vessels were present, and dolphin groups changed direction more often when aircraft circled overhead. Effects were positively correlated with number of vessels present within 300 m. Short-term changes have potential to lead to long-term impacts, and further acoustic and energetic studies are proposed to elucidate whether these effects are likely to reduce individual fitness.

Modeling to study the performance of wireless networks in recent years has produced sets of nonlinear equations with interrelated parameters. Because these nonlinear equations have no closed-form solution, the numerical values of the parameters are calculated by iterative algorithms. In a Markov chain model of a wireless cellular network, one commonly used expression for calculating the handoff arrival rate can lead to a sequence of oscillating iterative values that fail to converge. We present an algorithm that generates a monotonic sequence, and we prove that the monotonic sequences always converges. Lastly, we give a further algorithm that converges logarithmically, thereby permitting the handoff arrival rate to be calculated very quickly to any desired degree of accuracy.

(1961). The Public Health Significance of Atmospheric Lead. Archives of Environmental Health: An International Journal: Vol. 3, No. 5, pp. 568-574.

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Those who have used an ebonite rod or a miniature static machine to charge an electroscope during wet weather realize that it leaves much to be desired. The step-up transformer operated on A.C. and using a vacuum tube rectifier is more satisfactory, but is complicated and expensive. For the past six months the author has been using a simple and inexpensive arrangement composed of a Ford spark coil operated on two or three dry cells. This gives a high voltage direct current whose positive pole is connected to a fine needle point placed about three-quarters of an inch from the post that supports the leaf. This distance is much greater than the actual sparking distance of the coil, but allows a gentle brush discharge to take place, slowly lifting the leaf to the desired potential. By being lifted slowly (two or three seconds) the leaf is spared any sudden jerk, which might result in damage. No switching device is needed except a push button in the primary circuit. The accompanying photograph and drawing (Fig...