Data-Driven Framework to Predict the Rheological Properties of CaCl2 Brine-Based Drill-in Fluid Using Artificial Neural Network

Abstract

Calcium chloride brine-based drill-in fluid is commonly used within the reservoir section, as it is specially formulated to maximize drilling experience, and to protect the reservoir from being damaged. Monitoring the drilling fluid rheology including plastic viscosity, P V , apparent viscosity, A V , yield point, Y p , flow behavior index, n , and flow consistency index, k , has great importance in evaluating hole cleaning and optimizing drilling hydraulics. Therefore, it is very crucial for the mud rheology to be checked periodically during drilling, in order to control its persistent change. Such properties are often measured in the field twice a day, and in practice, this takes a long time (2–3 h for taking measurements and cleaning the instruments). However, mud weight, M W , and Marsh funnel viscosity, M F , are periodically measured every 15–20 min. The objective of this study is to develop new models using artificial neural network, ANN, to predict the rheological properties of calcium chloride brine-based mud using M W and M F measurements then extract empirical correlations in a white-box mode to predict these properties based on M W and M F . Field measurements, 515 points, representing actual mud samples, were collected to build the proposed ANN models. The optimized parameters of these models resulted in highly accurate results indicated by a high correlation coefficient, R, between the predicted and measured values, which exceeded 0.97, with an average absolute percentage error, AAPE, that did not exceed 6.1%. Accordingly, the developed models are very useful for monitoring the mud rheology to optimize the drilling operation and avoid many problems such as hole cleaning issues, pipe sticking and loss of circulation.

The Recent Development of Artificial Intelligence for Smart and Sustainable Energy Systems and Applications

“…[4] # Home energy management and ambient assisted living Non-intrusive load monitoring techniques [5] Non-intrusive load monitoring for energy disaggregation Genetic algorithm; support vector machine; multiple kernel learning [6] Optimizing residential energy consumption Bacterial foraging optimization; flower pollination [7] Non-intrusive load monitoring for energy disaggregation Long short-time memory and decision tree [8] Energy efficient coverage in wireless sensor network Distributed genetic algorithm [9] Estimation of load and price of electric grid Enhanced logistic regression; enhanced recurrent extreme learning machine; classification and regression tree; relief-F and recursive feature elimination [10] Detection of the insulators in power transmission and transformation inspection images Improved faster region-convolutional neural network [11] Non-intrusive load monitoring for energy disaggregation Concatenate convolutional neural network [12] Non-intrusive load monitoring for energy disaggregation Linear-chain conditional random fields [13] Prediction of the rheological properties of calcium chloride brine-based mud Artificial neural network [14] Estimation of Static Young's Modulus for sandstone formation Artificial neural network; self-adaptive differential evolution # Review article.…”

“…A. Gowida, S. Elkatatny, E. Ramadan, and A. Abdulraheem wrote an article "Data-driven framework to predict the rheological properties of CaCl2 brine-based drill-in fluid using artificial neural network" [13]. Artificial neural network was adopted to forecast the rheological properties of brine-based drill-in fluid so that it could avoid the loss of circulation, pipe sticking, and hole cleaning.…”

Real-Time Prediction of Rheological Properties of Invert Emulsion Mud Using Adaptive Neuro-Fuzzy Inference System

“…Recently, Gowida et al [48] used the ANN to predict the CaCl 2 brine-based drill-in fluid properties. Elkatatny et al [37] presented a new approach to predict the mud rheology of NaCl water-based drill-in fluid using AI to provide five correlations for the rheological properties and three input parameters which are mud weight, Marsh funnel viscosity and solid volume percent.…”

Newly Developed Correlations to Predict the Rheological Parameters of High-Bentonite Drilling Fluid Using Neural Networks

“…Recently, AI has been widely used in the area of drilling fluid [39]. Some of these applications are drilling optimization [40], optimizing drilling hydraulics [41], and prediction of rheological properties of invert emulsion mud, KCl water-based mud, CaCl 2 drilling fluid, NaCl water-based drill-in fluid rheological properties [42][43][44][45]. Additionally, new systems were developed using the integration between sensitive sensors measurements and AI application to estimate rheological parameters of non-Newtonian fluids [46].…”