Iron Nanoparticle Modified Smart Cement for Real Time Monitoring of Ultra Deepwater Oil Well Cementing Applications

Abstract

Better controls during well drilling and cementing operation are critical to ensure safety during construction and the entire service life of the wells. For a successful cementing operation determine the setting of cement in place length of cement supporting the casing and performance of the cement after hardening. At present there are no technologies available to monitor the cementing operations without using buried sensors that could weaken the cement sheath.In this study, smart cement with 0.38 water-to-cement ratio was modified with Iron nanoparticles (NanoFe) to have better sensing properties, so that its behavior can be monitored at various stages of construction and during the service life of wells. A series of experiments evaluated the smart cement behavior with and without NanoFe in order to identify the most reliable sensing properties that can also be relatively easily monitored. Tests were performed on the smart cement from the time of mixing to hardened state behavior. During the initial setting the electrical resistivity changed with time based on the amount of NanoFe used to modify smart oil well cement. A new quantification concept has been developed to characterize cement curing based on electrical resistivity changes in the first 24 hours of curing. When cement was modified with 0.1 percent of conductive filer (CF), the piezoresistive behavior of the hardened smart cement was substantially improved without affecting the rheological and setting properties of the cement. For the smart cement the resistivity change at peak stress was about 2000 times higher than the change in the compressive strain after 28 days of curing.The shear thinning behavior of the smart cement slurries with and without NanoFe at two different temperatures (25°C and 85°C) have been quantified using the new hyperbolic model and compared with another constitutive model with three material parameters, Vocadlo model. The results showed that the hyperbolic model predicated the shear thinning relationship between the shear stress and shear strain rate of the NanoFe modified smart cement slurries very well. Also the hyperbolic model has a maximum shear stress limit were as the other model did not have a limit on the maximum shear stress. Based on the hyperbolic model the maximum shear stresses produced by the 0 percent, 0.5 percent, and 1 percent of NanoFe at temperature of 25°C were 175 Pa, 224 Pa, and 298 Pa, respectively. The maximum shear stresses produced by the 0 percent, 0.5 percent, and 1 percent of NanoFe at temperature of 85°C were 349 Pa, 377 Pa and 465 Pa respectively. Additional of 1 percent NanoFe reduced the initial resistivity of the smart cement by 16 percent. In a 24-hour period the maximum change in the electrical resistivity (RI 24 hr) for the smart cement without NanoFe was 333 percent. The RI 24hr for the smart cement with NanoFe increased with the amount of NanoFe. Addition of 1 percent NanoFe increased the compressive strength of the smart cement by 26 percent and 42 percent after 1 day and 28 days of curin...

Rheology, curing temperature and mechanical performance of oil well cement: Combined effect of cellulose nanofibers and graphene nano-platelets

“…The Herschel-Bulkley model shows an infinite slope at zero shear rate, while the Vocadlo model exhibits a finite slope. With the Vocadlo model, similar results were obtained[27]. The value of τ 0 increased from 2.200 ± 0.322 to 3.170 ± 0.573 at the temperature range between 20 and 60 o C. Even though the Vocadlo model exhibited a quite high R 2 value, this model is not a good model to simulate the rheological behavior of CNF/GNP OWC slurry because the value of τ 0 is unacceptable low when compared the values from the other three models.…”

Smart cement modified with iron oxide nanoparticles to enhance the piezoresistive behavior and compressive strength for oil well applications

“…With the advancement of nanotechnology, polymer science and engineering, several of these materials can be a key to solving some of the problems encountered in oil and gas well cementing [1,8]. One of which is the use of nanotechnology and nanomaterials, which are beneficial for their large surface area, high aspect ratio, small size, low density and interesting physical and chemical properties [1,4]. Nanoparticles have had a strong influence on the mechanical properties of cementitious materials as they have a high surface area, providing a high chemical reactivity.…”

“…The conductivity of cement is mostly related to the three main parameters which are ion concentration, the number of charges per ion and the equivalent ionic conductivity. Other parameters that have significant effect on resistivity of cement during hydration are the curing temperature, curing condition and weight loss during curing [2][3][4].…”

“…Recent case studies on cementing failures have clearly identified several issues that resulted in various types of delays in the cementing operations. Preventing the loss of fluids to the formations and proper well cementing have become critical issues in well construction to ensure wellbore integrity because of varying down hole conditions [2][3][4]. The catastrophic accident in the Gulf of Mexico in April 2010 is one of the world's worst oil spills [5].…”

Soft Computing and Multi-Scal Model Technics to Predict the Early Age Compression Strength of Cement Paste as a Function of Polymer Contents, Water-to-Cement-Ratio, and Curing Ages

“…There are several methods for modeling the properties of materials, including computational modeling, statistical techniques, and recently developed tools such as regression analyses and Artificial Neural Networks (ANN) [25,26]. Multilinear regression analysis, M5P-tree, and ANN are techniques widely used to solve problems in construction project applications [27][28][29][30][31][32][33][34][35][36][37][38]. The most important characteristics of ANN is the ability to learn directly from examples and the great response to imperfect tasks.…”