In this paper the development, use, and evaluation of tasks based on the construct of school-related content knowledge are described. The tasks were used in seminars on organic chemistry for bachelor and master preservice chemistry teachers at a German university. For the evaluation a questionnaire with open and closed items was used. The tasks were rated by the preservice chemistry teachers as relevant for their future profession as a chemistry teacher if the content of the tasks is part of the school curriculum. If the content does not belong to the school curriculum, they rated the nature of the tasks still as relevant; they seem to recognize the importance of conceptual knowledge for their future profession. However, the master's preservice teachers argued with this conceptual knowledge more often than the bachelor's preservice teachers. Although the study is cross-sectional, a certain shift from the focus on the content to conceptual knowledge from bachelor's to master's preservice teachers can be observed.

In this paper, we describe a study on tasks following the construct of school-related content knowledge. We know from previous studies that such tasks were rated by the preservice chemistry teachers as important for their future profession. Those studies were conducted in a traditional course on organic chemistry which was organized around chemical families. Therefore, we used and evaluated the tasks again in a new course on organic chemistry which is organized around basic concepts in organic chemistry. The results of this evaluation show that the students rate the tasks equally well but use other arguments for their rating. They do not focus only on the content of the tasks and whether this content belongs to the school curriculum or not. The students of the conceptual course rated the content more often (95%) as important for their future profession compared with the students in the traditional course (57%). Both groups of students rated the importance of the nature of the task the same way.

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This paper describes the history matching and predictive case studies of twodeepwater Gulf of Mexico (GOM) fields using an advanced Bayes linear estimationtool.Advantages of the tool include significant acceleration of thehistory matching process, identification and quality measurements of multiplehistory matches, quantification of reservoir uncertainty, and an improvedunderstanding of reservoir performance.Additionally, a statisticalestimator of predictive simulation results is created to generate statisticallyvalid confidence intervals around performance predictions.This paperdescribes a practical workflow incorporating this tool to rapidly evaluatedeepwater producing gas fields, and illustrates its use to determine remainingfield potential and future development requirements of two fields, the Harrierand the Raptor Fields in the Pioneer Natural Resources-operated FalconCorridor. Introduction The deepwater GOM can contain fields with very prolific wells that can behighly profitable for an Operator.The loss of even one of these wells canadversely impact both short and long-term field production forecasts, thus cashflow and profitability.These impacts are especially significant whenthere are few, very high rate wells that contribute to the total fieldproduction.When such a well fails, it is crucial to understand the causesin order to determine how and if the situation can be remedied, the costnecessary to do so, and the risks involved.The goals are then tounderstand and reduce risks, to minimize cycle time and capital exposure, andto maximize profitability. If a well's failure to meet forecast expectations is attributed to reservoirperformance, a number of tools ranging from the very simple to the very complexcan be used to evaluate reservoir performance.The choice as to whichtools to use is dependent upon the amount and quality of data available, thecomplexity of the problem, the time available in which to make a decision, andthe magnitude of the capital required to execute the decision.Oftenhistory matching with 3-dimensional (3D) reservoir simulation is the tool ofchoice used to evaluate and explain production performance.However, thehistory-matching process can be very frustrating and time-consuming, even forfields that appear relatively simple in nature, because of the reservoirprocesses involved and the non-unique nature of the solution.[1]Consequently, much time and many resources can be spent in attempting toachieve even one history match.Frequently multiple solutions can be foundthat can satisfy history-match criteria but which yield divergent predictionoutcomes. Because of the high production rates in both the Harrier and Raptor Fields, rapid analysis and integration of production data were necessary to providequick answers to reservoir analysis and reservoir management questions, and toaddress well-intervention and deepwater rig availabilitydecisions.Pioneer selected 3D simulation and the implementation of anadvanced linear Bayesian tool to expedite the history matching and uncertaintyanalyses process.[2]3D static geologic models for both fields, built andupscaled for dynamic flow simulation prior to production start-up, had beenused for predictive simulations.Good quality pressure information frompermanent downhole gauges and daily gas production data were available for thecalibration of these models.Although there is a global workflow processthat encompasses the ‘seismic to simulation’ process, this paper focusesprimarily upon the dynamic history matching and predictive portion of theoverall process.

In this communication the development of an online course on the topic “organic chemistry” for nonmajor chemistry students is described and discussed. For this online course, the existing classroom course was further developed. New elements such as podcasts, task navigators, and a forum for discussing the solving of tasks or problems with the content were added. This new online course was evaluated. Therefore, a questionnaire was developed. This consists of questions with regard to the longtime learning behavior of the students and to the online learning. The results of this evaluation show that a preference for online learning and a preference for classroom teaching can be measured separately in two scales. Students’ values on the scale representing a preference for online learning correlate positively and significantly with confidence in the choice of the study subject, enthusiasm about the subject, and the ability to organize their learning, learning environment, and time management. They correlate also with the satisfaction concerning the materials provided. Students’ values for one of those teaching methods also correlate with their rating with regard to their exam preparation. Values representing a preference for online teaching correlate positively with students’ better feeling of exam preparation. Values representing a preference for classroom teaching show negative correlations with the values representing students’ similar or even better preparation for the exams as a result of online teaching. It is therefore not surprising that the ratings for the two scales correlate with the wish for a combination of online teaching and classroom teaching in the future. As a solution, a new course concept for the time after the corona virus crisis that suits all students is outlined in the outlook.