FIELD-SCALE GENERALITY OF THE MACHINE LEARNING MODELS

Drilling performance is directly related to fundamental aspects such as drilling variables that can affect the performance of the operation, the well stability, efficiency of drilling equipment, use of new technologies and operational parameters. Approximately 30% of the total time of construction of a well corresponds to the time rotating and sliding, in this order of ideas the optimization of the rate of penetration “ROP” has a direct impact on time and cost reduction. This reduction has as an added value: making viable economically the drilling campaigns and development of the fields. That is why one of the main objectives of the operating companies is to reduce the total time in which the true depth is reached, to reduce the costs of the operation but without affecting the main objective of the well drilling operations. To consider a good performance of the operations, many factors are involved being the rate of penetration one of the most important, without leaving behind the HSE performance, the stability of the well, integrity of the formation and final cost of the project. On the other hand, the data driven machine learning models are significantly different in conception process from physics-based models. The physic-based models try to understand the problem and propose proper models resembling he problem under certain assumptions and constraints. They seek methodology to reasonably determine the results given input. On the contrary, the machine learning models consider little about the details of the problem but train a working model mapping directly from inputs (knowns) to outputs (unknowns) through a black box of neutral networks. After that, researchers try to unveil the black box to analyze what happens there and enlighten what knowledge learned from there as to improve the model interpretability. Along the project, the relevant parameters for the machine learning predictive model were chosen considering the correlation and their dependency to ROP, the model was fed up, trained, and tested with the data set of one well and its accuracy was improved using hyperparameter tunning. After it, the algorithm was tested with five different data sets keeping constant the chosen parameters. Among them it was possible to determine that the Random Forest, Gradient Boosting and K Neighbors regressor were the ones with the highest coefficient of determination and the best performance, considering that any model in general can be improved reckoning also the importance of the learned lessons or field experience from petroleum engineering knowledge to enhance the quality of the inputs and the outputs of the model

Capacity-building activities related to climate change vulnerability and adaptation assessment and economic valuation for Fiji

The Terms of Reference for this work specified three objectives to the Fiji component: Objective 1a: to provide a prototype FIJICLIM model (covered under PICCAP funding) Objective 1b: to provide training and transfer of FIJICLIM Objective 1c: to present and evaluate World Bank study findings and to identify future directions for development and use of FIJICLIM (2-day workshop) Proceedings of the training course and workshop were prepared by the Fiji Department of Environment. The summaries from these proceedings reflect a very high degree of success with the contracted activities

Modelling the Developing Mind: From Structure to Change

This paper presents a theory of cognitive change. The theory assumes that the fundamental causes of cognitive change reside in the architecture of mind. Thus, the architecture of mind as specified by the theory is described first. It is assumed that the mind is a three-level universe involving (1) a processing system that constrains processing potentials, (2) a set of specialized capacity systems that guide understanding of different reality and knowledge domains, and (3) a hypecognitive system that monitors and controls the functioning of all other systems. The paper then specifies the types of change that may occur in cognitive development (changes within the levels of mind, changes in the relations between structures across levels, changes in the efficiency of a structure) and a series of general (e.g., metarepresentation) and more specific mechanisms (e.g., bridging, interweaving, and fusion) that bring the changes about. It is argued that different types of change require different mechanisms. Finally, a general model of the nature of cognitive development is offered. The relations between the theory proposed in the paper and other theories and research in cognitive development and cognitive neuroscience is discussed throughout the paper

Using think-aloud interviews to characterize model-based reasoning in electronics for a laboratory course assessment

Models of physical systems are used to explain and predict experimental results and observations. The Modeling Framework for Experimental Physics describes the process by which physicists revise their models to account for the newly acquired observations, or change their apparatus to better represent their models when they encounter discrepancies between actual and expected behavior of a system. While modeling is a nationally recognized learning outcome for undergraduate physics lab courses, no assessments of students' model-based reasoning exist for upper-division labs. As part of a larger effort to create two assessments of students' modeling abilities, we used the Modeling Framework to develop and code think-aloud problem-solving activities centered on investigating an inverting amplifier circuit. This study is the second phase of a multiphase assessment instrument development process. Here, we focus on characterizing the range of modeling pathways students employ while interpreting the output signal of a circuit functioning far outside its recommended operation range. We end by discussing four outcomes of this work: (1) Students engaged in all modeling subtasks, and they spent the most time making measurements, making comparisons, and enacting revisions; (2) Each subtask occurred in close temporal proximity to all over subtasks; (3) Sometimes, students propose causes that do not follow logically from observed discrepancies; (4) Similarly, students often rely on their experiential knowledge and enact revisions that do not follow logically from articulated proposed causes.Comment: 18 pages, 5 figure