Artificial intelligence techniques and their applications in drilling fluid engineering: A review

Tracking the rheological properties of the drilling fluid is a key factor for the success of the drilling operation. The main objective of this paper is to relate the most frequent mud measurements (every 15 to 20 min) as mud weight (MWT) and Marsh funnel viscosity (MFV) to the less frequent mud rheological measurements (twice a day) as plastic viscosity (PV), yield point (YP), behavior index (n), and apparent viscosity (AV) for fully automating the process of retrieving rheological properties. The adaptive neuro-fuzzy inference system (ANFIS) was used to develop new models to determine the mud rheological properties using real field measurements of 741 data points. The data were collected from 99 different wells during drilling operations of 12 ¼ inches section. The ANFIS clustering technique was optimized by using training to a testing ratio of 80% to 20% as 591 data points for training and 150 points, cluster radius value of 0.1, and 200 epochs. The results of the prediction models showed a correlation coefficient (R) that exceeded 0.9 between the actual and predicted values with an average absolute percentage error (AAPE) below 5.7% for the training and testing data sets. ANFIS models will help to track in real-time the rheological properties for invert emulsion mud that allows better control for the drilling operation problems.

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

Development of an ANN-based soft-sensor to estimate the apparent viscosity of water-based drilling fluids

In this work, a new constitutive model of a magneto-rheological fluid (MRF) actuator is proposed using an extreme learning machine (ELM) technique to enhance the prediction accuracy of the field-dependent actuating force. After briefly reviewing existing rheological constitutive models of MRF actuator, ELM algorithm is formulated using a single-hidden layer feed-forward neural network. In this formulation, both the magnetic field and measured shear rates are used as inputs variables, while the shear stress predicted from the ELM training is used as an output variable. Subsequently, in order to validate the effectiveness of the proposed model, the target defined as the error between the prediction and measured data is set. Then, the fitness of the training and prediction performances is evaluated using a normalized root mean square error (NRMSE) method. It is shown that the shear stress estimation based on the ELM model using sinusoidal activation function is more accurate than conventional rheological constitutive models such as Herschel-Bulkley model. It is also demonstrated that the proposed model is capable of predicting the field-dependent yield stress which is defined as an actuating force of the MRF actuator without causing significant errors.

A new constitutive model of a magneto-rheological fluid actuator using an extreme learning machine method

Study the effect of synthesized graft copolymer on the inhibitive water based drilling fluid system

A feed-forward multilayer perceptron neural network model with Levenberg-Marquardt learning algorithm has been developed to predict rheological properties of water-based drilling fluids. The developed model was based on a three-layer network containing input, hidden, and output layer. The sigmoid function was applied as the transfer function in the hidden layer and linear transfer function in the output layer. Two statistical parameters, mean square errors and correlation coefficient, were used as a criterion for evaluating artificial neural network modeling performance. Accordingly, a comparison between experimental values and those predicted by the artificial neural network showed a good coincidence proving its high accuracy in estimating target value.

Artificial Neural Network Modeling of Plastic Viscosity, Yield Point, and Apparent Viscosity for Water-Based Drilling Fluids

The effects of guar gum polymer and lime powder addition on the fluid loss and rheological properties of bentonite dispersions have been investigated. Eleven different formulations have been prepared according to the American Petroleum Institute procedures and their rheological characteristics have been determined using a rotational viscometer. Four rheological models have been used to describe the pseudo-plastic behavior of the dispersions. It has been observed that all rheological properties increase with pH and show a maximum at pH value of about 11.5. The effect of addition of lime also extremely increases the fluid loss of bentonite dispersions.

Effect of Guar Gum Polymer and Lime Powder Addition on the Fluid Loss and Rheological Properties of the Bentonite Dispersions

This study investigated effect of temperature, concentration, and shear rate on rheological properties of xanthan gum aqueous solutions using a Couette viscometer at temperatures between 25°C and 55°C and concentrations of 0.25 wt% to 1.0 wt%. The Herschel–Bulkley model described very well the non-Newtonian behavior of xanthan gum solutions. Shear rate, temperature, and concentration affected apparent viscosity and an equation was proposed for the temperature and concentration effect valid for each shear rate. This article also presents an artificial neural network (ANN) model to predict apparent viscosity. Based on statistical analysis, the ANN method estimated viscosity with high accuracy and low error.

Experimental Study and Numerical Modeling of Rheological and Flow Behavior of Xanthan Gum Solutions Using Artificial Neural Network

The aim of this study is to investigate the influence of pH between 8.5 and 12.5, mixing time, and of NaCl, KCl, and CaCl2 concentration over the range of 0% to 20% on the rheological properties of pre-hydrated bentonite slurry following by treatments with different polymers. Viscometric data gathered by viscometer described well by the Herschel-Bulkley rheological model for all fluids tested. Increasing salt concentration and pH led to some significant changes in the rheological properties. Moreover, adding saturated salt to pre-hydrated slurry resulted in increasing shear stress and plastic viscosity for all fluids.

An Experimental Study of Influence of Salt Concentration, Mixing Time, and pH on the Rheological Properties of Pre-Hydrated Bentonite Slurries Treated by Polymers

Meisam Mirarab Razi

Papers

Artificial Neural Network Modeling of Plastic Viscosity, Yield Point, and Apparent Viscosity for Water-Based Drilling Fluids

Effect of Guar Gum Polymer and Lime Powder Addition on the Fluid Loss and Rheological Properties of the Bentonite Dispersions

Experimental Study and Numerical Modeling of Rheological and Flow Behavior of Xanthan Gum Solutions Using Artificial Neural Network

An Experimental Study of Influence of Salt Concentration, Mixing Time, and pH on the Rheological Properties of Pre-Hydrated Bentonite Slurries Treated by Polymers