Machine Learning Model for Monitoring Rheological Properties of Synthetic Oil-Based Mud

Abstract

The drilling fluid rheology is a critical parameter during the oil and gas drilling operation to achieve optimum drilling performance without nonproductive time or extra remedial operation cost. The close monitoring for rheological properties will help the drilling fluid crew to take quick actions to maintain the designed profiles for the drilling fluid rheology, especially when it comes to the flat rheology drilling fluid system, which is a new generation for harsh and specific drilling conditions that require flat profiles for the mud rheology regarding the temperature condition changes. The current study introduces a machine learning application toward predicting the rheology of synthetic oil-based mud (flat rheology type) for the full automation system of monitoring the mud rheological properties. Four models are developed, for the first time, to determine the rheological characteristics of flat rheology synthetic oil-based system using artificial neural networks. The developed models are capable of predicting the plastic and apparent viscosities, yield point, and flow behavior index from only the mud density and Marsh funnel as model inputs. The proposed models were trained and optimized from a real field dataset (369 measurements) with further testing the models using an unseen dataset of 153 data points. The predicted rheological properties achieved a high degree of accuracy versus the actual measurements and showed a coefficient of correlation range from 0.91 to 0.97 with an average absolute percentage error of less than 9.66% during the training and testing phases. Besides, machine learning-based correlations are proposed for estimating the rheological properties on the rig site without running the machine learning system for easy field applications.

Different Machine Learning Approaches to predict Gas Deviation Factor (Z-factor)

“…They predicted the value would be the products of weights from the neurons, used to predict the ultimate value in the forward propagation. It takes the opposite direction with backpropagation as the output is used to determine the most correct wheats to get the optimum results [33][34][35]. Scalar outputs would be predicted by the functional neurons from the first hidden layer which is considered as a functional layer.…”

Filter Cake Neural-Objective Data Modelling and Image Optimization

“…This model is widely seen as effective because it can extract weights and biases from the trained data and the equations used. The already pre-processed data imported used 10, 12, and 18 neurons as a means to validate the results compared with Alsabaa's research [19] in Figure 8b. More so, Figure 2(a-l) shows the propagation of more stable linear regression analysis by employing nftools' non-linear hidden neurons sigmoid with a linear output neuron based on the rheological parameters.…”

“…These profiles under consideration take into account the density of the fluid, apparent and plastic viscosity, yield point, and gel strengths; the relevancy of this check helps ascertain the circulation and pressure loss of the drilling fluids' performances. Alsabaa [19] had demonstrated the flow behavior of drilling mud using the Marsh funnel experiment; the author further used Fann 35 [20,21] rotating viscometer to define mud rheology. However, the continuous testing for the flat rheological properties of the synthetic-based drilling mud [22] in the fields and the laboratory remains a relevant job for the rig crews to do because, when it comes to multilateral wells, suitable drilling fluids are designed to cater for well control.…”

Enhancing Fracturing Fluid Viscosity in High Salinity Water: A Data-Driven Approach for Prediction and Optimization

“…These models gauged the cumulative impact of additives to enhance the gel strength and viscosity of the drilling mud, all while minimizing the need for extensive laboratory trials . Another study built four models utilizing ANN to predict the rheology of synthetic oil-based mud, including the apparent and the plastic viscosities, yield point, and flow behavior …”

“…10 Another study built four models utilizing ANN to predict the rheology of synthetic oil-based mud, including the apparent and the plastic viscosities, yield point, and flow behavior. 11 Similarly, 12 used ANN to predict the water-based mud rheology and filtration properties. The combination of the cuckoo optimization algorithm with a multilayer extreme learning machine is used to predict the unexpected change in viscosity in drilling operations.…”