1,720,955 research outputs found
An End-To-End Machine Learning Project for Detection of Stuck Pipe Symptoms During Tripping Operations
Full text linkNon-productive time due to stuck pipe costs the Oil and Gas industry substantial losses amounting to $250 million annually [1]. Thus, it is imperative for companies to invest in tools that can aid in prevention. This study integrates different concepts and methodologies from Petroleum Engineering, Data Analysis, and Machine Learning (ML). It aims to identify and extract hook load signatures before a stuck pipe event that can be used to train an ML model. The lack of transparent and consistent frameworks in many published papers using the same approach proved to be a problem. Hence, it is also our aim to present all the algorithms used.
In a Machine Learning project, data preparation accounts for about 80% of the work [2, 3]. For this reason, the author developed two web-based applications for cleaning and exploring raw drilling data. These provided time savings given the time constraints of this project.
Once the data was prepared, maximum and local minimum hook loads were extracted for tripping out and tripping in operations, respectively. During the study, a new concept for extracting the local minimum hook load was developed. It was able to identify the trend deviation as early as 4 hours and 30 minutes before the reported stuck pipe. Furthermore, all the extracted maximum and local minimum hook loads distinguished trend deviation between normal and deteriorating downhole conditions. This was not possible when basing solely on the real-time hook load.
Moreover, a long short term-memory network was trained using 50% of the extracted hook load signatures. This model was designed to predict and identify hook load trends during tripping operations. Then using the remaining data, the model was evaluated. Results showed that the model predicted hook loads with a mean absolute error of <3% from the average expected value. The model also resembled trends with a delay of utmost 20 minutes or six stands, particularly during the deteriorating conditions. Despite the model failing to forecast, it detected a deteriorating condition three hours before the stuck pipe incident. These results were heavily dependent on the amount and quality of data. Out of seven wells provided, only three were functional, having at least 0.2 Hz of measurement.
Further studies involving gathering more high quality drilling data and retraining the model are recommended to be able to create a model capable of forecasting the trend deviations earlier than the currently developed model
Applied Transfer Learning in Drilling Engineering
Full text linkPhD thesis in Information technologyDrilling in the oil and gas industry generates multimodal data crucial for decision-making in both operational and administrative units. The sheer volume of data produced throughout the lifecycle of a well presents opportunities and challenges. Deep learning (DL) has made significant progress in computer vision and language modeling. However, its adoption in niche industries like oil and gas drilling lags due to practical constraints such as limited on-site computational resources, high costs of developing models, and large data requirements to capture meaningful relationships.
In the dissertation, we explore transfer learning to address the DL application bottlenecks. We cover two areas: sequential drilling data for rate of penetration (ROP) prediction and language modeling for efficient data retrieval. In the first part, we leverage simulated data from physics-based simulators as supplemental data. Then, we explore the idea and techniques of transferring knowledge from pre-trained models to adapt to specific wells. Second, we examine the capabilities of generic large language models for drilling text data. Subsequently, we adapt a generic language model in the drilling domain to improve a document retriever. We show that transfer learning enables DL applications in the drilling more accessible. Finally, we aim to foster the development of applications by sharing Our collated and generated datasets
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
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