Comparative Study of Local Cements with Imported Class G Cement at Different Temperatures for Oil Well Cementing Operations in Ghana
The purpose of this research is to determine the suitability of local cements as an alternative to imported class G cement at different temperatures using oil well cement additives. Laboratory investigations on both local and imported cements to determine physical properties such as thickening time, fluid loss, compressive strength, free fluid and rheology were conducted at bottom hole circulating temperature of 27°C (80°F) and 66°C (150°F). The results indicated that locally manufactured cements are compatible with oil well cement additives at 80°F and 150°F and can be used as an alternative to imported class G cement in oil and gas well cementing operations. However, premature gelations of test results were phenomenal at 66°C (150°F) for the entire locally manufactured cements especially sample CEM B. Therefore, for local cements to be used effectively for cementing high temperature operations, stringent quality control during their manufacturing process is imperative.
Synthesis and application of mesoporous silica nanoparticles as gas migration control additive in oil and gas cement
Abstract Cement failure has been widely reported to be one of the major causes of many catastrophic accidents in the oil and gas industry. Lack of cement integrity and strength has been associated with the potentially harmful gas migration from the reservoir into the wellbore. In this study, the potential application mesoporous silica nanoparticles as gas migration control additive for oil and gas cement was evaluated. Here, mesoporous silica nanoparticles were prepared using micelle templating sol-gel method and its performance as the additive was assessed against the commercial micro- and nano-sized silica particles. Based on the result, it is revealed that all of the cement samples prepared with or without additives exhibited sufficient rheological properties for field application. Nonetheless, results showed that silica nanoparticles addition would result in a slight increase in viscosity and yield point reduction of the cement slurry. Further investigations suggested that silica nanoparticles could also accelerate the hydration process due to their ability to provide a large surface area for nucleation reaction and the formation of C–S–H seeds. As a result, this would lead to the creation of a denser and stronger cement structure, which exhibits low porosity and permeability features. According to the result, the most optimum formulation was achieved when the cement was mixed with 0.0132 wt% of a mesoporous silica nanoparticle, with the compressive strength value of 6.67 MPa and the values of porosity and permeability of 8.94% and 2.47 × 10−17 m2 (0.025 mD), respectively.
Using Artificial Neural Networks to Predict the Quality and Performance of Oil-Field Cements
Inherent batch-to-batch variability, aging, and contamination are major factors contributing to variability in oil-field cement-slurry performance. Of particular concern are problems encountered when a slurry is formulated with one cement sample and used with a batch having different properties. Such variability imposes a heavy burden on performance testing and is often a major factor in operational failure. We describe methods that allow the identification, characterization, and prediction of the variability of oil-field cements. Our approach involves predicting cement compositions, particle-size distributions, and thickening-time curves from the diffuse reflectance infrared Fourier transform spectrum of neat cement powders. Predictions make use of artificial neural networks. Slurry formulation thickening times can be predicted with uncertainties of less than 10 percent. Composition and particle-size distributions can be predicted with uncertainties a little greater than measurement error, but general trends and differences between cements can be determined reliably. Our research shows that many key cement properties are captured within the Fourier transform infrared spectra of cement powders and can be predicted from these spectra using suitable neural network techniques. Several case studies are given to emphasize the use of these techniques, which provide the basis for a valuable quality control tool now finding commercial use in the oil field.