Oil Well Multi-view Reconstruction Technology Based on Neural Implicit Surface

In the field of oil extraction, accurate acquisition of the three-dimensional structural information of oil wells is extremely critical to improving extraction efficiency and ensuring production safety. Traditional oil well measurement and reconstruction methods have problems such as poor accuracy and difficulty in data acquisition when facing complex downhole environments. This paper proposes a multi-view reconstruction technology for oil wells based on neural implicit surfaces. By collecting and preprocessing multi-view image data and efficiently fitting with the help of neural implicit surface models, accurate reconstruction of the complex structure of oil wells can be achieved. The research results show that compared with traditional methods, this technology has significantly improved the reconstruction accuracy. In terms of effective feature point density, among the W01 – W05 scenes, the W03 scene has the highest density, about 15 points/cm<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>, and the remaining scenes fluctuate between 10 – 12 points/cm<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>, which can provide a more reliable basis for the planning, maintenance and management of oil wells.