Numerical Investigation of Non-Newtonian Drilling Fluids During the Occurrence of a Gas Kick in a Petroleum Reservoir

Oliveira, F. F.; Sodré, C. H.; Marinho, J. L. G.

doi:10.1590/0104-6632.20160332s20140024

Braz. J. Chem. Eng.

2016

DOI: 10.1590/0104-6632.20160332s20140024

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Numerical Investigation of Non-Newtonian Drilling Fluids During the Occurrence of a Gas Kick in a Petroleum Reservoir

F. F. Oliveira

C. H. Sodré

J. L. G. Marinho

Abstract: -In this work, a simplified kick simulator is developed using the ANSYS ® CFX software in order to better understand the phenomena called kick. This simulator is based on the modeling of a petroleum well where a gas kick occurs. Dynamic behavior of some variables like pressure, viscosity, density and volume fraction of the fluid is analyzed in the final stretch of the modeled well. In the simulations nine different drilling fluids are used of two rheological categories, Ostwald de Waele, also known as Power-La… Show more

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Cited by 3 publications

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“…Elkatatny14 developed two ANN models for predicting invert emulsion mud plastic viscosity and apparent viscosity. Oliveira54 established a nonlinear regression model for estimating the OBM plastic viscosity using mud pressure and temperature. Igwilo55 utilized least-square and Gaussian elimination for building an OBM plastic viscosity predictive model as a function of mud temperature.…”

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Prediction of the Rheological Properties of Invert Emulsion Mud Using an Artificial Neural Network

et al. 2021

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Successful drilling operations require optimum well planning to overcome the challenges associated with geological and environmental constraints. One of the main well design programs is the mud program, which plays a crucial role in each drilling operation. Researchers focus on modeling the rheological properties of the drilling fluid seeking for accurate and real-time predictions that confirm its crucial potential as a research point. However, only substantial studies have real impact on the literature. Several AI-based models have been proposed for estimating mud rheological properties. However, most of them suffer from nonbeing field applicable attractive due to using non-readily field parameters as input variables. Some other studies have not provided a comprehensive description of the model to replicate or reproduce results using other datasets. In this study, two novel robust artificial neural network (ANN) models for estimating invert emulsion mud plastic viscosity and yield point have been developed using actual field data based on 407 datasets. These datasets include mud plastic viscosity (PV), yield point (YP), mud temperature (T), marsh funnel viscosity (MF), and solid content. The mathematical base of each model has been provided to provide a clear means for models' replicability. Results of the evaluation criteria depicted the outstanding performance and consistency of the proposed models over extant ANN models and empirical correlations. Statistical evaluation revealed that the plastic viscosity ANN model has a coefficient of determination (R 2 ) of 98.82%, a root-mean-square error (RMSE) of 1.37, an average relative error (ARE) of 0.12, and an absolute average relative error of 2.69, while for yield point, this model has a coefficient of determination (R 2 ) of 94%, a root-mean-square error (RMSE) of 0.76, an average relative error (ARE) of −0.67, and an absolute average relative error of 3.18.

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Prediction of the Rheological Properties of Invert Emulsion Mud Using an Artificial Neural Network

et al. 2021

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Tailoring NiMo-Based Catalysts for Production of Low-Viscosity Sustainable Hydrocarbon Bases for Drilling Muds from Secondary Gas Oils

et al. 2023

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This article presents the prospect of using the process of deep hydrodesulfurization and hydrodearomatization of secondary gas oils using highly active NiMo catalysts to obtain hydrocarbon bases for drilling fluids. Catalysts were synthesized using PMo heteropolyanions, citric acid, and diethylene glycol on alumina carriers with different pore volumes. This study showed that the concentration of the impregnating solution affects the composition and morphology of the active phase particles of the prepared catalyst, while the textural characteristics of the carrier influence the physicochemical properties and catalytic activity of the NiMo/Al2O3 catalysts. The catalyst that was synthesized using a carrier with the largest pore volume and an effective diameter of more than 7 nm exhibited the highest activity. It was demonstrated that the use of such a catalyst allows for the procurement of hydrocarbon bases for drilling fluids from mixtures of secondary gas oils at a hydrogen pressure of 15–20 MPa. This study has practical significance for the development of sustainable and economically efficient methods for the utilization of low-quality petroleum gas oils to produce high-margin environmentally friendly non-fuel petroleum products, as well as contributes to the development of economically efficient technologies for the utilization of petroleum raw materials.

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Flow Parameter Evaluation During the Occurrence of a Gas Kick in a Petroleum Drilling Using Turbulence Models: A Numerical Study

Oliveira

Sodré

Marinho

2016

BJPG

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The drilling of petroleum wells demand large investments and the use of highly skilled personnel in the operation to be viable economically. In extreme situations, drilling requires strict process control. One of the most important tasks in this process is controlling the pressure within the well to avoid the influx of formation fluids into the well, i.e. a kick. A kick consists of an unwanted formation fluid flow due to a pressure difference in the pit. To better understand this process, a simplified kick simulator using the ANSYS CFX software was developed and implemented to analyze the behavior of some variables inside the pit during the occurrence of a gas kick. In this model, the behavior of dynamic variables such as mud volume fraction, densities, and pressure of fluids inside the tube is evaluated during drilling using two types of drilling fluids and testing four different turbulence models: k-epsilon, RNG-k-epsilon, k-omega and SST. In these tests one can note that for both fluids analyzed, when using the SST model, there was a more rapid invasion of the gas. However, using the RNG k-epsilon model, a greater resistance to invasion was observed, and both fluids exhibited similar behavior when using the same turbulence model. One can also observe that the SST allows a greater pressure drop along the stretch of the well examined in the final moment of the simulation, while the RNG k-epsilon model is one that showed a lower pressure drop.

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Numerical Investigation of Non-Newtonian Drilling Fluids During the Occurrence of a Gas Kick in a Petroleum Reservoir

Cited by 3 publications

References 2 publications

Prediction of the Rheological Properties of Invert Emulsion Mud Using an Artificial Neural Network

Prediction of the Rheological Properties of Invert Emulsion Mud Using an Artificial Neural Network

Tailoring NiMo-Based Catalysts for Production of Low-Viscosity Sustainable Hydrocarbon Bases for Drilling Muds from Secondary Gas Oils

Flow Parameter Evaluation During the Occurrence of a Gas Kick in a Petroleum Drilling Using Turbulence Models: A Numerical Study

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