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This may be linked to the networks learning and sharing the biases of the team behind the reference interpretations. We also compare with how well human reinterpretations of the log data match the reference interpretations, finding that the networks match the reference somewhat better. For comparison, a random-guess baseline gives matches of 16.7%, 44.4%, and 50%, respectively. For hydraulic isolation, the interpretations match the reference 86.7% of the time. For bond quality, the networks’ interpretation exactly matches the reference 51.6% of the time and is off by no more than one class 88.5% of the time. We quantify the networks' performance by comparing over all segments how well the networks' interpretations of unseen data match the reference interpretations. More specifically, the task of the networks is to classify the bond quality (among 6 ordinal classes) and the hydraulic isolation (2 classes) in each 1m depth segment of each well based on the surrounding 13 m of well log data. Thus, the networks learn the connections between data and interpretations during training.
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#Techlog change reference how to#
Average client rating (based on 22 attendee reviews) The Course prepares the student to recognize the value of Management Systems, the use of a Qulaity Management System to manage Contractors, and how to participate in the assessment of a Contractor using the API Q2. This system is based on deep convolutional neural networks, which we train in a supervised manner using a dataset of around 60 km of interpreted well log data. Managing Service Contractor Quality: API Q2. To aid these interpreters, we propose a system for automatically interpreting cement evaluation logs, which they can use as a basis for their own interpretation. Cement evaluation logs must therefore be interpreted by trained professionals. However, well logging results are complex and can be ambiguous, and decisions associated with significant risks may be taken based on their interpretation. The integrity of cement in cased boreholes is typically evaluated using well logging.