Drilling operations around the world employ a concept called overbalance. During this process, it is well known that dynamic and static filtration can occur. Thin filter cakes and low fluid-invasion rates are extremely desirable to promote optimal logging conditions and permeability return. The aim of this work was to compare the different behavior between dynamic and static filtration in drilling wells. To investigate the filtration process of Newtonian suspensions, we built a dynamic and static filtration loop with which we acquired experimental filtration volume data as a function of time. The filtration loop included a tank mixer where a Newtonian aqueous calcium carbonate polydisperse suspension was homogenized. The suspension was pumped through tubes to a dynamic or a static filtration cell. We validated a theoretical model based on Darcy's law and on mass conservation proposed by Ferreira and Massarani (2005). That model predicted mud cake buildup and filtrate flow rate for Newtonian suspensions. Relying on both models and the experimental data, filter cake parameters were calculated. We discuss, based on these parameters, the effects of the filtration configuration in dynamic and static modes. Finally, we generalized Ferreira and Massarani's model (2005) for procedures involving non-Newtonian suspensions. This new model can predict dynamic filtration and fluid invasion for non-Newtonian suspensions as drilling fluids.
This study proposes, through the coupling of a linear filtration formulation (laboratory configuration) and a radial single-phase formulation (wellbore vicinity), to predict fluid-invasion depth-ofdrilling fluid filtrate in the reservoir rock. Modeling is validated with linear and radial laboratory tests, as well as with resistivity logs run in offshore wells from Campos basin, offshore Brazil.The proposed methodology is required for optimum drillingfluid design to be used in the drilling of reservoir sections in both exploratory and developmental wells in Campos basin. Drilling-Fluid Design Criteria for Minimum InvasionAn adequate drilling-fluid design requires bridging-agent size distribution and concentration optimization. The ability of the fluid system to prevent invasion is normally evaluated by standardized static-filtration experiments. In these tests, the fluid is pressurized through a filter paper or into a consolidated inert porous medium. The volume that crosses the porous core is monitored continuously. Santos et al. 5 present an experimental study questioning the reliability of filter-paper-filtration experiments to reproduce invasion into unconsolidated reservoirs.The main goal of this study is to establish a simplified methodology to correlate laboratory filtration tests and radial invasion in the reservoir. In other words, to define laboratory filtration requirements for a given accepted invasion. This task will be performed in three different steps: (1) modeling the laboratory linear experiment (flow through a consolidated porous medium),
Fluid losses are still today one of the most challenging problems in well construction. The scenarios faced by operators during development and exploratory campaigns in the deep water pre-salt area are characterized by natural fractures, vugs and caves. Therefore, problems related to loss of circulation are critical, increasing the non-productive time and consequently, well construction costs. Additionally, in several situations, conventional drilling limitations prevent the reaching of the well target. The present study proposes the application of a methodology to define optimum loss control material (cross-linked pills, fluid loss squeeze, bridging agents, cement), among those available for each situation, to minimize lost circulation events during drilling operations. An Artificial Intelligence strategy based on Supervised Learning was defined to generalize data collected from five hundred lost circulation events over a three years period. Human Computer Interaction principles were used on the development of an interface where the field engineer can interact with training data while having little to no Machine Learning knowledge. The use of empirical analysis and learning strategies as tools to assist the decision making process in the form of lost circulation countermeasures is described by this paper. The method was validated on data collected from several different wells in the Santos Basin, Brazil, pre-salt area. The strategy was already applied in two real cases resulting in a six days well construction time saving.
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