The rheological properties of the drilling fluid are crucial to the success of the drilling project. The traditional mud experiments normally performed by the mud engineers provide rheological data with a small resolution. Monitoring higher-resolution rheological properties is particularly important for all-oil mud because it is widely used with problematic drilled formations. The design and monitoring of the drilling fluid rheology is a critical issue for drilling, and therefore, this paper is a contribution to the effort to completely automate the process of highly accurate and real-time recording of the rheological mud properties. This paper aims to develop intelligent predictive models for the mud rheological properties using artificial neural networks [ANN] by linking the high-frequency mud parameters such as fluid density or mud weight [MWT] and Marsh funnel viscosity [MFV] with the rheological measurements of low frequency for drilling mud such as plastic viscosity [PV], yield point [YP], behavior indicator [n] and viscosity appearance [AV]. New empirical correlations have additionally been established to assess the rheological properties of water. In order to construct ANN models, data was obtained from 56 different wells during drilling operations of different drilling sections with various sizes. The data was fairly enough for building and testing the models as 369 data points were obtained. The models were optimized by trainlm which was the best training function and tansig was the best transfer function. 42 neurons in the hidden layer optimized AV and PV models where 34 neurons optimized all other rheological models [YP, n, R300, and R600]. ANN models presented good results as correlation coefficient [R] was 0.9 and an average absolute [AAPE] error of less than 8% for training and testing data sets. The new models were used to derive the empirical correlations for the estimation of rheological parameters. The empirical correlations were extracted to easily monitor the rheological properties of an all-oil mud system in real-time, which enables better control of the drilling activity by maintaining rheological properties at optimal values as well as early detection of other problems that might require immediate interactions, including well control and stuck pipe.

钻井液的流变特性对钻井项目的成功至关重要。通常由泥浆工程师进行的传统泥浆实验提供的流变数据分辨率较低。监测高分辨率流变特性对全油泥浆尤为重要，因为它已广泛用于有问题的钻井地层。钻井液流变学的设计和监控是钻井的关键问题，因此，本文对完全自动化流变泥浆特性的高精度和实时记录过程的努力做出了贡献。本文旨在通过将高频泥浆参数（例如流体密度或泥浆重量[MWT]和沼泽漏斗粘度[MFV]）与水泥浆的流变学测量联系起来，使用人工神经网络[ANN]开发智能的泥浆流变特性预测模型。低频钻井泥浆，例如塑性粘度[PV]，屈服点[YP]，行为指标[n]和粘度外观[AV]。此外，还建立了新的经验相关性以评估水的流变特性。为了构建ANN模型，在不同大小的不同钻井段的钻井作业中，从56口井中获得了数据。获得了369个数据点，数据足以用于构建和测试模型。通过trainlm对模型进行了优化，trainlm是最好的训练函数，而tansig是最好的传递函数。隐藏层中的42个神经元优化了AV和PV模型，其中34个神经元优化了所有其他流变模型[YP，n，R300和R600]。训练和测试数据集的相关系数[R]为0.9，平均绝对[AAPE]误差小于8％，因此ANN模型显示出良好的结果。新模型用于导出流变参数估计的经验相关性。通过提取经验相关性，可以轻松实时地监控全油泥浆系统的流变特性，从而可以通过将流变特性保持在最佳值来更好地控制钻井活动，以及及早发现可能需要立即解决的其他问题。互动，