New correlations for better monitoring the all-oil mud rheology by employing artificial neural networks

Abstract

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.