基于地理空间分析的合成套管腐蚀测井预测——数字孪生概念

Mohammad S. Al-Kadem, Ryyan Bayounis, Ayman Khalaf, Abdullah Alghamdi
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引用次数: 0

摘要

井下套管腐蚀监测是生产工程中的一个关键因素,因为它可以确保资产的完整性和安全性,最大限度地延长油井的寿命和使用寿命,并有助于成功的HSE管理计划。因此,为了监测套管的完整性,经常对井进行腐蚀和金属损失异常的测井。该研究探索了一种利用地理空间分析技术开发合成腐蚀测井的方法,以优化运营成本,补充缺失的测井资料,避免生产延迟和停工。该方法通过对现有测井数据进行地理空间分析,生成完整的综合腐蚀测井曲线,然后绘制整个油田的金属损失缺陷图。空间映射利用计算几何和计算机辅助工程技术,构建基于深度的三维地图。来自数百条测井曲线的数十万个数据点,由(1)深度、(2)套管规格、(3)水泥性能和(4)金属损失严重程度表示,这些数据点被输入到使用Kriging插值的框架中,以开发变异函数模型。通过开发变异函数模型,可以在每个深度点生成套管金属损失率图,从而建立完整的综合腐蚀测井曲线。可用的腐蚀测井数据集分为两部分;70%用于训练模型,剩下的30%用于测试。然后进行了交叉验证检查。开发的地理空间分析模型对使用地理空间分析生成的所有预测日志的总体置信度达到95%。最初研究的另一种方案将深度、金属损失率和井龄作为唯一的输入数据点。然而,本研究的准确率较低,只有90%。当将地层特征、套管和水泥性能纳入模型时,这一比例增加到95%。通过生成完整的现场金属损失严重程度图,开发的模型能够有效地优化1000条腐蚀测井要求。由于能够在不产生实际操作成本的情况下预测关键井的金属损失量,预计可节省高达数千万美元的成本。除了确保井的完整性之外,该方法还可以最大限度地减少H2S等腐蚀性气体的物理暴露,从而促进资产和人员的健康和安全。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Synthetic Casing Corrosion Log Prediction Using Geospatial Analysis – A Digital Twin Concept
Downhole casing corrosion monitoring is a key element in production engineering as it ensures the integrity and safety of assets, maximizes the life and serviceability of a well, and contributes to a successful HSE management programs. Consequently, wells are frequently logged for corrosion and metal loss anomalies to monitor casing integrity. This study explores a method using geospatial analytical techniques to develop synthetic corrosion logs to optimize OPEX, supplement missing logs, and avoid production deferment and downtimes. The proposed method generates full synthetic corrosion logs using geospatial analysis based on available logs, then it maps metal loss defects across the entire field. The spatial mapping builds a 3D map based on depth using computational geometry and computer-aided engineering. Hundreds of thousands of data points from hundreds of logs, represented by (1) depth, (2) casing specifications, (3) cement properties, and (4) metal loss severity, have been fed into the framework to develop a variogram model using Kriging interpolation. By developing the variogram model, a map is generated at each depth point with casing metal loss ratio, and hence a full synthetic corrosion log is built. The data set of available corrosion logs was split into two parts; 70% for training the model and the remining 30 % for testing. Then a cross-verification check was done as well. The developed geospatial analytical model achieved an overall confidence level of 95% of all predicted logs generated using the geospatial analysis. Another scenario was initially studied that incorporates depth, metal loss percentages, and well age as the only input data points. However, this study yielded a lower accuracy level of only 90%. This percentage increased to 95% when incorporating formation characteristics, casing and cement properties into the model. The developed model enabled effective optimization of 1000 corrosion logs requirement through the generation of a full field metal loss severity map. The cost avoidance can be estimated to reach up to tens of millions of dollars due to the ability of predicting metal loss for critical wells without actual operation costs. On top of assuring well integrity, the developed method promotes health and safety of assets and personnel as it minimizes physical exposure of corrosive gases such as H2S.
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