Ultrasonic pulse-echo dataset from numerical modelling for oil and gas well integrity investigations.

IF 6.9 2区综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES

Scientific Data Pub Date : 2025-04-01 DOI:10.1038/s41597-025-04851-x

Anja Diez, Erlend Magnus Viggen, Tonni Franke Johansen

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Abstract

The ultrasonic pulse-echo (PE) measurement is a crucial measurement technique to determine the integrity of oil and gas wells. Oil companies use various analysis techniques and corrections to derive the pipe thickness and impedance of the material behind the pipe from PE measurements that are carried out inside the pipe. While some field measurements are publicly available, they have no corresponding ground truth. We therefore simulated a dataset of PE measurements with ground truth. The dataset was generated using axisymmetric models and 3D models in COMSOL Multiphysics. The base geometry was based on common parameters from the field: oil-based mud on the inside of a 9.625 in pipe and cement on the outside of the pipe. From this base geometry, variations in the model parameters were introduced, for example, plate/pipe wall thickness, different materials on both sides of the wall, different pipe diameter, different annulus thicknesses, eccentering. The generated dataset allows detailed investigations of existing PE analysis algorithms, comparison of those and development of new PE analysis techniques.

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油气井完整性调查数值模拟的超声脉冲回波数据集。

超声脉冲回波（PE）测量是确定油气井完整性的一项重要测量技术。石油公司使用各种分析技术和校正方法，从管道内部进行的PE测量中得出管道厚度和管道后面材料的阻抗。虽然一些实地测量是公开的，但它们没有相应的地面真值。因此，我们模拟了一个具有地面真实值的PE测量数据集。数据集使用COMSOL Multiphysics中的轴对称模型和三维模型生成。基座的几何形状基于油田的常用参数：9.625 in管内部是油基泥浆，管外部是水泥。在此基础几何基础上，引入了板/管壁厚度、管壁两侧不同材料、管径不同、环空厚度不同、偏心等模型参数的变化。生成的数据集允许对现有PE分析算法进行详细调查，对这些算法进行比较，并开发新的PE分析技术。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Scientific Data Social Sciences-Education

CiteScore

11.20

自引率

4.10%

发文量

689

审稿时长

16 weeks

期刊介绍： Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.