A Developed Robust Model and Artificial Intelligence Techniques to Predict Drilling Fluid Density and Equivalent Circulation Density in Real Time.

When drilling deep wells, it is important to regulate the formation pressure and prevent kicks. This is achieved by controlling the equivalent circulation density (ECD), which becomes crucial in high-pressure and high-temperature wells. ECD is particularly important in formations where the pore pressure and fracture pressure are close to each other (narrow windows). However, the current methods for measuring ECD using downhole sensors can be expensive and limited by operational constraints such as high pressure and temperature. Therefore, to overcome this challenge, two novel models named ECDeffc.m and MWeffc.m were developed to predict ECD and mud weight (MW) from surface-drilling parameters, including standpipe pressure, rate of penetration, drill string rotation, and mud properties. In addition, by utilizing an artificial neural network (ANN) and a support vector machine (SVM), ECD was estimated with a correlation coefficient of 0.9947 and an average absolute percentage error of 0.23%. Meanwhile, a decision tree (DT) was employed to estimate MW with a correlation coefficient of 0.9353 and an average absolute percentage error of 1.66%. The two novel models were compared with artificial intelligence (AI) techniques to evaluate the developed models. The results proved that the two novel models were more accurate with the value obtained from pressure-while-drilling (PWD) tools. These models can be utilized during well design and while drilling operations are in progress to evaluate and monitor the appropriate mud weight and equivalent circulation density to save time and money, by eliminating the need for expensive downhole equipment and commercial software.

Carbon Dioxide Applications for Enhanced Oil Recovery Assisted by Nanoparticles: Recent Developments.

Carbon dioxide (CO2) in enhanced oil recovery (EOR) has received significant attention due to its potential to increase ultimate recovery from mature conventional oil reserves. CO2-enhanced oil recovery (CO2-EOR) helps to reduce global greenhouse gas emissions by sequestering CO2 in subterranean geological formations. CO2-EOR has been exploited commercially over recent decades to improve recovery from light and medium gravity oil reservoirs in their later stages of development. CO2 tends to be used in either continuous flooding or alternated flooding with water injection. Problems can arise in CO2-flooded heterogeneous reservoirs, due to differential mobility of the fluid phases, causing viscous fingering and early CO2 penetration to develop. This study reviews the advantages and disadvantages of the techniques used for injecting CO2 into subsurface reservoirs and the methods adopted in attempts to control CO2 mobility. Recently developed methods are leading to improvements in CO2-EOR results. In particular, the involvement of nanoparticles combined with surfactants can act to stabilize CO2 foam, making it more effective in the reservoir from an EOR perspective. The potential to improve CO2 flooding techniques and the challenges and uncertainties associated with achieving that objective are addressed.

Enhancing the Quality of Service for Real Time Traffic over Optical Burst Switching (OBS) Networks with Ensuring the Fairness for Other Traffics.

Optical burst switching (OBS) networks have been attracting much consideration as a promising approach to build the next generation optical Internet. A solution for enhancing the Quality of Service (QoS) for high priority real time traffic over OBS with the fairness among the traffic types is absent in current OBS' QoS schemes. In this paper we present a novel Real Time Quality of Service with Fairness Ratio (RT-QoSFR) scheme that can adapt the burst assembly parameters according to the traffic QoS needs in order to enhance the real time traffic QoS requirements and to ensure the fairness for other traffic. The results show that RT-QoSFR scheme is able to fulfill the real time traffic requirements (end to end delay, and loss rate) ensuring the fairness for other traffics under various conditions such as the type of real time traffic and traffic load. RT-QoSFR can guarantee that the delay of the real time traffic packets does not exceed the maximum packets transfer delay value. Furthermore, it can reduce the real time traffic packets loss, at the same time guarantee the fairness for non real time traffic packets by determining the ratio of real time traffic inside the burst to be 50-60%, 30-40%, and 10-20% for high, normal, and low traffic loads respectively.