Gouda Abdelrahman, Khaled Samir, Gomaa Sayed, Attia Attia M
Petroleum Engineering and Gas Technology Department, Faculty of Energy and Environmental Engineering, The British University in Egypt, El Shorouk City, Cairo 11837, Egypt.
Mining and Petroleum Engineering Department, Faculty of Engineering, Al-Azhar University, Nasr City, Cairo 11371, Egypt.
ACS Omega. 2021 Nov 24;6(48):32948-32959. doi: 10.1021/acsomega.1c04937. eCollection 2021 Dec 7.
Successful drilling operations require optimum well planning to overcome the challenges associated with geological and environmental constraints. One of the main well design programs is the mud program, which plays a crucial role in each drilling operation. Researchers focus on modeling the rheological properties of the drilling fluid seeking for accurate and real-time predictions that confirm its crucial potential as a research point. However, only substantial studies have real impact on the literature. Several AI-based models have been proposed for estimating mud rheological properties. However, most of them suffer from non-being field applicable attractive due to using non-readily field parameters as input variables. Some other studies have not provided a comprehensive description of the model to replicate or reproduce results using other datasets. In this study, two novel robust artificial neural network (ANN) models for estimating invert emulsion mud plastic viscosity and yield point have been developed using actual field data based on 407 datasets. These datasets include mud plastic viscosity (PV), yield point (YP), mud temperature (), marsh funnel viscosity (MF), and solid content. The mathematical base of each model has been provided to provide a clear means for models' replicability. Results of the evaluation criteria depicted the outstanding performance and consistency of the proposed models over extant ANN models and empirical correlations. Statistical evaluation revealed that the plastic viscosity ANN model has a coefficient of determination ( ) of 98.82%, a root-mean-square error (RMSE) of 1.37, an average relative error (ARE) of 0.12, and an absolute average relative error of 2.69, while for yield point, this model has a coefficient of determination ( ) of 94%, a root-mean-square error (RMSE) of 0.76, an average relative error (ARE) of -0.67, and an absolute average relative error of 3.18.
成功的钻井作业需要优化井眼规划，以克服与地质和环境限制相关的挑战。泥浆方案是主要的井眼设计方案之一，在每次钻井作业中都起着至关重要的作用。研究人员专注于对钻井液的流变特性进行建模，寻求准确的实时预测，以证实其作为研究重点的关键潜力。然而，只有大量的研究才会对文献产生真正的影响。已经提出了几种基于人工智能的模型来估计泥浆流变特性。然而，由于使用不易获取的现场参数作为输入变量，它们中的大多数在现场应用方面缺乏吸引力。其他一些研究没有对模型进行全面描述，以便使用其他数据集来复制或重现结果。在本研究中，基于407个数据集的实际现场数据，开发了两种新颖的稳健人工神经网络（ANN）模型，用于估计反相乳化泥浆的塑性粘度和屈服点。这些数据集包括泥浆塑性粘度（PV）、屈服点（YP）、泥浆温度、马氏漏斗粘度（MF）和固相含量。每个模型的数学基础都已给出，以便为模型的可复制性提供清晰的方法。评估标准的结果表明，所提出的模型相对于现有的ANN模型和经验相关性具有出色的性能和一致性。统计评估显示，塑性粘度ANN模型的决定系数（ ）为98.82%，均方根误差（RMSE）为1.37，平均相对误差（ARE）为0.12，绝对平均相对误差为2.69，而对于屈服点，该模型的决定系数（ ）为94%，均方根误差（RMSE）为0.76，平均相对误差（ARE）为 -0.67，绝对平均相对误差为3.18。
