The extraction of oil is accompanied by water and sediments that, mixed with the oil, cause the formation of scale depositions in the pipelines walls promoting the reduction of the inner diameter of the pipes, making it difficult for the fluids to pass through interest. In this sense, there is a need to control the formation of these depositions to evaluate preventive and corrective measures regarding the waste management of these materials, as well as the optimization of oil extraction and transport processes. Noninvasive techniques such as gamma transmission and scattering can support the determination of the thickness of these deposits in pipes. This paper presents a novel methodology for prediction of scale with eccentric deposition in pipes used in the offshore oil industry and its approach is based on the principles of gamma densitometry and deep artificial neural networks (DNNs). To determine deposition thicknesses, a detection system has been developed that utilizes a 1 mm narrow beam geometry of collimation aperture comprising a source of ¹³⁷Cs and three properly positioned 2″×2″ NaI(Tl) detectors around the system, pipe-scale-fluid. Crude oil was considered in the study, as well as eccentric deposits formed by barium sulfate, BaSO₄. The theoretical models adopted a static flow regime and were developed using the MCNPX mathematical code and, secondly, used for the training and testing of the developed DNN model, a 7-layers deep rectifier neural network (DRNN). In addition, the hyperparameters of the DRNN were defined using a Baysian optimization method and its performance was validated via 10 experiments based on the K-Fold cross-validation technique. Following the proposed methodology, the DRNN was able to achieve, for the test sets (untrained samples), an average mean absolute error of 0.01734, mean absolute relative error of 0.29803% and R2 Score of 0.9998813 for the scale thickness prediction and an average accuracy of 100% for the scale position prediction. Therefore, the results show that the 7-layers DRNN presents good generalization capacity and is able to predict scale thickness with great precision, regardless of its position inside the tube.

石油的提取伴随着水和沉积物，这些沉积物与石油混合，导致在管道壁中形成水垢沉积，从而促进了管道内径的减小，从而使流体难以通过感兴趣的区域。从这个意义上讲，需要控制这些沉积物的形成，以评估关于这些材料的废物管理以及采油和运输过程的优化的预防和纠正措施。诸如伽马透射和散射之类的非侵入性技术可以支持确定管道中这些沉积物的厚度。本文提出了一种用于预测海上石油工业管道中偏心沉积物垢的新颖方法，其方法基于伽玛密度法和深层人工神经网络（DNN）原理。为确定沉积厚度，已开发出一种检测系统，该系统利用1 mm的准直孔径窄光束几何形状，包括一个¹³⁷ Cs和三个围绕系统正确放置的2“×2” NaI（Tl）检测器，管道规模的流体。研究中考虑了原油以及由硫酸钡，BaSO ₄形成的偏心沉积物。该理论模型采用静态流态，并使用MCNPX数学代码进行了开发，其次，该模型用于训练和测试已开发的DNN模型（一个7层深整流器神经网络（DRNN））。此外，使用贝叶斯优化方法定义了DRNN的超参数，并基于K-Fold交叉验证技术通过10个实验验证了其性能。按照所提出的方法，DRNN对于测试集（未经训练的样本）能够实现平均平均绝对误差为0.01734，平均绝对相对误差为0.29803％和R2得分为0.9998813（用于鳞片厚度预测）和平均精度100％用于刻度位置预测。因此，