Real-Time Prediction of the Rheological Properties of Water-Based Drill-In Fluid Using Artificial Neural Networks

Abstract

The rheological properties of drilling fluids are the key parameter for optimizing drilling operation and reducing total drilling cost by avoiding common problems such as hole cleaning, pipe sticking, loss of circulation, and well control. The conventional method of measuring the rheological properties are time-consuming and require a high effort for equipment cleaning, so they are only measured twice a day. There is a need to develop an automated system to measure the rheological properties in real-time based on the frequent measurements of mud density, Marsh funnel time, and solid percent. The main objective of this paper is to apply a modified self-adaptive differential evolution technique to determine the optimum combination of an artificial neural network’s variables to precisely predict the rheological properties of water-based drill-in fluid using the frequent measuring of mud density, Marsh funnel time, and solid percent. The second objective is whitening the black box of an artificial neural network by developing five new empirical correlations to determine the rheological properties without the need for the artificial neural network models. Actual field measurements (900 data points) were used to train, test, and validate the artificial neural network models and the developed empirical correlations. The optimization process illustrated that the best training function was Bayesian regularization backpropagation (trainbr), and the best transferring function was Elliot symmetric sigmoid (elliotsig). The optimum number of neurons was 30 for the plastic viscosity and the flow consistency index, while it was 29 for apparent viscosity, yield point, and the flow behavior index. The developed artificial neural network models and empirical correlations predicted the rheological properties with high accuracy. The correlation coefficient (R) was more than 90%, and the average absolute percentage error was less than 8.6%. The new technique for rheological properties estimation is an example of the new development which will help the new generation to discover and extract oil and gas with less cost and with safer operations.

Real-Time Measurement of Drilling Fluid Rheological Properties: A Review

“…The model usually consists of three layers: an input layer-in addition to the Marsh funnel viscosity, the other drilling fluid parameters, such as mud weight and solid content, are also added as input elements; a hidden layer, which contains an optimized number of neurons; an output layer, which contains output parameters (PV, YP, AV, K, n, and τ 0 ). Abdelgawad et al [63] and Elkatatny et al [66] used the self-adaptive differential evolution (SaDe) algorithm to optimize the best combination of the ANN's parameters for rheological property estimation. SaDe proposed by Qin et al [57] is a special differential evolution algorithm with adaptive control parameters and mutation strategies based on learning experience [57,58].…”

“…According to the above theories, artificial intelligence methods are used to estimate rheological properties more accurately in real time based on parameters such as the Marsh funnel viscosity, mud weight, and solid content [ 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 ]. Bispo et al [ 72 ] used temperature, xanthan gum, bentonite, and barite to estimate AV.…”

“…Abdelgawad et al [ 63 ] and Elkatatny et al [ 66 ] used the self-adaptive differential evolution (SaDe) algorithm to optimize the best combination of the ANN’s parameters for rheological property estimation. SaDe proposed by Qin et al [ 57 ] is a special differential evolution algorithm with adaptive control parameters and mutation strategies based on learning experience [ 57 , 58 ].…”

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].…”

“…This higher R-value between the rheological properties with FV compared to MD can be explained as HBM is characterized by its high content of bentonite, which mainly affects the mud viscosity, not the mud weight. Table 3 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].…”