Intelligent Directional Survey Data Analysis to Improve Directional Data Acquisition

Many countries rely on oil and gas production as it is an essential part of the global economy.As a result, various challenges may thrive from the process of extracting oil and gas from the ground that may affect the operational aspects of the construction process.So, it is important to maintain the production and Health, Safety & Environment (HSE).The project aims to automate the process of the Directional Survey Data (DSD) in a way that can be cost-effective for the operational process and more stable for future use.DSD relates to the process of horizontal directional drilling (HDD) and raw data obtained from the surveys using survey stations on the way to bore hole like azimuth and inclination etc.In this work, we propose a fully automatic Directional Survey Data Analysis system based on the recognition patterns.The dataset comprised of 34069 real-time instances has been used.Two machine learning algorithms and four deep learning algorithms were investigated in this regard.For the deep learning approach RNN, LSTM, BI-LSTM, and Extreme Learning Machine (ELM) were used, and for the machine learning approach SVM and Naï ve Bayes have been investigated.Selection of these candidate approaches was based on their promising nature in the related fields of study in terms of accuracy and precision.The experimental result demonstrated that Naï ve Bays got 100% accuracy, ANN, LSTM and GRU managed to get 100% accuracy, BI-LSTM had a slightly lower accuracy achieving 98.7%, Simple RNN was lower than BI-LSTM achieving 82% accuracy, SVM got 81.1% accuracy, while ELM had the lowest performance receiving 55.3% accuracy.Overall, the scheme outperforms state-of-the-art techniques in the literature.