Apparent Viscosity Prediction of Water-Based Muds Using Empirical Correlation and an Artificial Neural Network

Al‐Khdheeawi, Emad A.; Mahdi, Doaa Saleh

doi:10.3390/en12163067

Cited by 39 publications

(18 citation statements)

References 26 publications

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“…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.…”

Section: Real-time Measurement Technologiesmentioning

confidence: 99%

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

Liu

Zhang

Gao

et al. 2021

Sensors

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The accurate and frequent measurement of the drilling fluid’s rheological properties is essential for proper hydraulic management. It is also important for intelligent drilling, providing drilling fluid data to establish the optimization model of the rate of penetration. Appropriate drilling fluid properties can improve drilling efficiency and prevent accidents. However, the drilling fluid properties are mainly measured in the laboratory. This hinders the real-time optimization of drilling fluid performance and the decision-making process. If the drilling fluid’s properties cannot be detected and the decision-making process does not respond in time, the rate of penetration will slow, potentially causing accidents and serious economic losses. Therefore, it is important to measure the drilling fluid’s properties for drilling engineering in real time. This paper summarizes the real-time measurement methods for rheological properties. The main methods include the following four types: an online rotational Couette viscometer, pipe viscometer, mathematical and physical model or artificial intelligence model based on a Marsh funnel, and acoustic technology. This paper elaborates on the principle, advantages, limitations, and usage of each method. It prospects the real-time measurement of drilling fluid rheological properties and promotes the development of the real-time measurement of drilling rheological properties.

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Section: Real-time Measurement Technologiesmentioning

confidence: 99%

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

Liu

Zhang

Gao

et al. 2021

Sensors

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“…This gel structure is formed by the interaction of drilling fluid components and it mainly depends on the size, concentration, and electrostatic charge of the solid particles like (clay and nanoparticles). This property should be carefully monitored to preserve solids without causing extreme recirculation pressure through the mud pumps start up [2] Generally, the gel strengths are measured by a rotational viscometer after the mud has set gently for two different periods of time (ten seconds and ten minutes).…”

Section: Rheological Propertiesmentioning

confidence: 99%

“…Then it returns back to the surface through the annular space between the drilling string and the borehole [1]. Primarily, drilling fluids are used for cooling down and lubricating the drilling bit and the drilling string, suspend the rock cutting when the circulation paused, carrying the rock cutting from borehole to the surface, maintaining the hydrostatic head pressure (Ph) greater than the pore pressure (Pf) to avoid any kind of unwanted formation fluids to flow to the well [2], [3] Generally, drilling mud design is on the most essential aspects to consider during the well construction and completion stages. The proper selection of the drilling mud is one of the key factors for succussing any drilling operation, which is typically based on its performance, cost, and environmental influence.…”

Section: Introductionmentioning

confidence: 99%

Evaluation the performance of conventional water-based mud characteristics by applying Zinc Oxide and Silica Dioxide Nanoparticles materials for a selected well in The Kurdistan/ Iraq

AlBajalan

Haias

2021

Preprint

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Nanomaterials have gained a wide interest in the oil and gas industry due to its immense applicability. Nanomaterial is being used for formulating a new generation of drilling mud which is known as Nano- mud. Where it has the ability to improve mud properties and eliminate bore hole problems. Using nanoparticles as an additive agent in conventional drilling mud can lead to a more efficient drilling process in troublesome formations. In this experimental study, several conventional water-based muds from a selected well drilled in the Kurdistan / Iraq oil field have been prepared. Then nano-drilling muds were formulated by dispersing SiO2 and ZnO nanoparticles in concentrations ranging from (0.25 to 1 wt.%) to conventional water-based mud (WBM). The objective of this experimental study is evaluating and comparing the performance of conventional water-based muds after adding SiO2 and ZnO nanoparticles. This comparison was performed by carrying out a series of laboratory experiments to determine the rheological and mud filtrate properties. The results demonstrated that adding SiO2 and ZnO NPs to conventional water-based mud improved the rheological behavior and provided better filtration control compared to conventional drilling muds. However, there was little or no impact of nanomaterials on the mud density for all mud systems.

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“…Al-Khdheeawi and Mahdi [35] applied an ANN to predict the apparent viscosity of water-based drilling fluid using the mud density and Marsh funnel time. They concluded that the developed ANN correlation could predict AV with an average absolute percentage error (AAPE) of 8.6% and a correlation coefficient of 98.8%.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

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

Elkatatny

2019

Sustainability

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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.

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Apparent Viscosity Prediction of Water-Based Muds Using Empirical Correlation and an Artificial Neural Network

Cited by 39 publications

References 26 publications

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

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

Evaluation the performance of conventional water-based mud characteristics by applying Zinc Oxide and Silica Dioxide Nanoparticles materials for a selected well in The Kurdistan/ Iraq

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

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